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Sex Differences in the Connectivity of the Subgenual Anterior Cingulate Cortex: Implications for Pain
Habituation
by
Gang Wang
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Medical Science
University of Toronto
© Copyright by Gang Wang 2013
ii
Sex Differences in the Connectivity of the Subgenual
Anterior Cingulate Cortex: Implications for Pain Habituation
Gang Wang
Master of Science
Institute of Medical Science
University of Toronto
2013
Abstract
Women exhibit greater habituation to painful stimuli than men. The neural mechanism
underlying this sex difference is unknown. However, pain habituation has been
associated with pain-evoked activity of the subgenual anterior cingulate cortex (sgACC),
implicating a connection between the sgACC and the descending pain antinociceptive
system. Therefore, the thesis hypothesis was that women have stronger connectivity than
men between the sgACC and the descending antinociceptive system. Healthy subjects
provided informed consent. 3T MRI images included anatomical diffusion-weighted
imaging for structural connectivity analyses (SC) with probabilistic tractography and
resting-state functional images for functional connectivity (FC) analyses. Women had
stronger sgACC FC with nodes of the descending pain modulation system (raphe, PAG)
and the medial thalamus. In contrast, men had stronger sgACC FC with nodes of the
salience/attention network (anterior insula, TPJ) and stronger sgACC SC with the
hypothalamus. These findings implicate a mechanism for pain habituation and its
associated sex differences.
iii
Acknowledgments
I would like to first thank Dr. Karen D Davis for her guidance. She is an exceptional
supervisor for her patience with her students. Not only did she teach me how to conduct
research, but, more importantly, she taught me how to educate myself in conducting
research. She helped shape me into an independent and autodidactic researcher. By
asking me the right questions without providing direct answers, Karen has enabled me to
explore the field of Neuroscience - Neuroimaging and Pain. Her suggestions for me to
participate in journal clubs and seminars made it possible for me to interact with and
learn from honourable and modest researchers, e.g., Dr. Barry Sessle, Dr. Limor Avivi-
Arber, and Dr. Jonathan Dostrovsky.
I also want to express my gratitude for Dr. Adrian Crawley, who has always made time in
his office, in the hallway, or by phone for answering my questions related to statistical
analyses and my project. As one of the most humble supervisor, Dr. Adrian Crawley
often provided suggestions that are critical for the success of my project and future
publications.
I would like to thank Dr. Judith Hunter, who has always shown interest and optimism in
my project and ability to carry it out successfully. Her expertise in Neuroscience has
urged and helped me explore the discipline and look for more implications of my
research results. Her humour and cheerfulness during my PAC meetings has motivated
me to remain in academia in the future.
From Drs. Karen D Davis, Adrian Crawley, and Judith Hunter, I have learned to read,
analyze, and think more critically and from a broader perspective. They have changed the
way I understand academic publications and research results.
I would also like to acknowledge a number of students in the Davis lab: Dr. Nathalie
Erpelding who generally shared with me her collected MRI data – the start of my thesis
project; Dr. Massieh Moayedi who has shown me exemplary critical thinking skills;
Danielle Desouza who have always been cheerful and energetic; Aaron Kucyi who
showed me early approach for fMRI analysis and who provided me the learning
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opportunity in engineering the TENS Driver as well as programming the online pain
rating software in E-Prime; and Dr. Ruma Goswami who I had the pleasure to collaborate
with in her early stages of probabilistic tractography analyses. She has been one of the
most humble post-doctorate I have ever met.
When I had trouble with understanding fMRI analysis approaches, Mr. Geoff Pope had
always been available, helpful, and patient in answering my questions. His understanding
of neuroimaging techniques as well honesty in his extend of knowledge has guided me in
the right direction.
In a number of labs where I conducted research, supervisors are usually the leaders for
the research group, providing directions. The Davis Lab is unique in the sense that every
member in the lab is a leader in her/his own field, and that each person is able to work
independently and successfully to challenge and to broaden the boundary of the
knowledge that is currently known. What I learned at the Davis Lab will not only guide
me in research but also in any aspect of my future when I aspire for excellence.
I am grateful for my funding sources: OGS, IMS Entrance Scholarship, TWRI poster
design award, CIHR grant (Dr. Karen D Davis), and IMSSA Award for Community
Leadership.
A special acknowledgement to my grandmother, who recently passed away. may she
always live in my heart. May her kindness, selflessness, unconditional love, humour,
optimism, and altruism be constantly reflected in my actions to lighten people, so that
these qualities—her legacy—will perpetually be relived by and be passed onto others for
the many days to come ...
At last, I want to thank my parents, who gave me life and made significant sacrifices to
bring me to Canada, making it possible for me to learn from the very best and the very
brightest minds in the world.
v
Table of Contents
Acknowledgments.............................................................................................................. iii
Table of Contents ................................................................................................................ v
List of Tables .................................................................................................................... vii
List of Figures .................................................................................................................. viii
List of Appendices ............................................................................................................ xii
List of Abbreviations ....................................................................................................... xiii
1 INTRODUCTION, AIM, AND HYPOTHESIS ........................................................ 1
2 LITERATURE REVIEW ........................................................................................... 3
2.1 Pain ......................................................................................................................... 3
2.1.2 Ascending Pain Pathways ........................................................................... 4
2.1.3 Descending pain modulation....................................................................... 5
2.1.4 Adaptation and habituation ......................................................................... 9
2.1.5 Diffuse noxious inhibitory controls .......................................................... 10
2.1.6 Sex differences .......................................................................................... 11
2.2 Subgenual anterior cingulate cortex ...................................................................... 16
2.2.2 Anatomy and connectivity ........................................................................ 16
2.2.3 sgACC function ........................................................................................ 18
2.3 Resting state BOLD-fMRI .................................................................................... 19
2.3.2 MRI ........................................................................................................... 20
2.3.3 Decay of MR Signal.................................................................................. 21
2.3.4 BOLD signal ............................................................................................. 22
2.3.5 Aliasing ..................................................................................................... 23
2.3.6 Smoothing ................................................................................................. 23
2.4 Diffusion tensor imaging ...................................................................................... 27
3 METHODS ............................................................................................................... 30
3.1 Participants ............................................................................................................ 30
3.2 Brain Imaging Acquisition .................................................................................... 30
3.2.2 Pre-processing and correlation analysis .................................................... 31
3.2.3 Subject level statistical analyses ............................................................... 32
3.2.4 Group level statistical analyses ................................................................. 33
3.3 Probabilistic tractography ..................................................................................... 36
3.3.2 Pre-processing ........................................................................................... 36
3.3.3 Subject level statistical analyses ............................................................... 37
3.3.4 Group level statistical analyses ................................................................. 38
4 RESULTS ................................................................................................................. 43
4.1 Resting-state fMRI ................................................................................................ 43
4.1.2 Overview of findings ................................................................................ 43
vi
4.1.3 sgACC functional connectivity: group findings ....................................... 43
4.1.4 Sex differences in sgACC functional connectivity ................................... 44
4.2 Probabilistic tractography ..................................................................................... 62
4.2.2 Overview of findings ................................................................................ 62
4.2.3 sgACC anatomical connectivity in males and females ............................. 62
4.2.4 Sex differences in sgACC anatomical connectivity.................................. 63
5 DISCUSSION ........................................................................................................... 71
5.1 SUMMARY OF MAIN FINDINGS .................................................................... 71
5.2 DELINEATION OF PAIN PATHWAYS WITH MRI-BASED CONNECTIVITY
TECHNIQUES: ADVANTAGES AND LIMITATIONS ................................................ 72
5.2.2 PAG and descending modulation pathway ............................................... 75
5.2.3 Raphe and descending modulation pathway ............................................. 76
5.2.4 MD thalamus and medial system .............................................................. 77
5.2.5 Salience and attention network ................................................................. 79
5.2.6 Hypothalamus and descending modulation pathway ................................ 82
5.3 FUTURE DIRECTIONS ...................................................................................... 86
5.4 CONCLUSION ..................................................................................................... 87
References ......................................................................................................................... 91
Appendices ...................................................................................................................... 108
Appendix I: Tractograms in both Women and Men ............................................... 109
Appendix II: Tractograms in Women....................................................................... 133
Appendix III: Tractograms in Men ............................................................................ 157
vii
List of Tables
Table 3-1. Combined thresholding ................................................................................... 35
Table 3-2. Probabilistic tractography seed and target definition ...................................... 39
Table 4-1. Resting-state group FC to sgACC: summary of main sex differences findings
........................................................................................................................................... 55
Table 4-2. Resting-state group FC to sgACC: main findings of interest .......................... 56
Table 4-3. Resting-state group FC to sgACC seeds: additional findings ......................... 58
Table 4-4. Group SC to sgACC: summary of main findings ............................................ 69
Table 4-5. sgACC anatomical connectivity in females and males ................................... 70
viii
List of Figures
Figure 2-1. A sine wave with 1 second period (Olshausen, 2000) ................................... 26
Figure 2-2. Gaussian kernel distribution and full width at half max (FWHM) ................ 26
Figure 2-3. Measuring diffusion with MRI....................................................................... 29
Figure 3-1. sgACC seeds for rs-fMRI............................................................................... 34
Figure 3-2. Classification probabilistic tractography method ........................................... 40
Figure 3-3. Probabilistic tractography – analysis 1 .......................................................... 41
Figure 3-4. Probabilistic tractography – analysis 2 .......................................................... 42
Figure 4-1. Resting-state group FC to sgACC: summary of main findings in sex
differences. ........................................................................................................................ 46
Figure 4-2. Representative individual subject examples of the time series of resting state
activity within seed-target pairs of regions that show sex differences. ............................ 47
Figure 4-3. Resting-state female & male group FC with sgACC seed A, C, E, H ........... 48
Figure 4-4. Resting-state female group FC with sgACC seed H ...................................... 49
Figure 4-5. Resting-state male group FC with sgACC seed B, C, D, E, F, J ................... 50
Figure 4-6. Stronger resting-state functional connectivity with sgACC seeds A, L, and H
in female group than male group ...................................................................................... 51
Figure 4-7. Stronger resting-state FC with sgACC seeds A, B, C, D, E, F, and J in male
group than female group ................................................................................................... 52
Figure 4-8. Main regions of stronger sgACC FC in females compared to males ............. 53
Figure 4-9. Main regions of stronger sgACC FC in males compared to females ............. 54
Figure 4-10. Summary – sgACC structural connectivity in the group of all subjects ...... 64
Figure 4-11. Stronger anatomical connectivity between left sgACC and left Hy in male
group than female group ................................................................................................... 65
Figure 4-12. Female group anatomical connectivity between sgACC seeds and targets . 66
Figure 4-13. Male group anatomical connectivity between sgACC seeds and targets ..... 67
Figure 4-14. Female and male group anatomical connectivity between sgACC seeds and
targets ................................................................................................................................ 68
Figure 5-1.Structural connections from sgACC (green lines) or from sgACC-associated
regions (purple lines) reviewed from structural studies .................................................... 88
Figure 5-2. Descending modulation axonal projections ................................................... 89
Figure 5-3. Descending pain modulation pathways .......................................................... 90
Figure A.I-1. Structural connectivity between the left subgenual anterior cingulate and
periaqueductal gray in all subjects .................................................................................. 109
Figure A.I-2. Structural connectivity between the left subgenual anterior cingulate and
left hypothalamus in all subjects ..................................................................................... 110
Figure A.I-3. Structural connectivity between the left subgenual anterior cingulate and
right hypothalamus in all subjects................................................................................... 111
Figure A.I-4. Structural connectivity between the left subgenual anterior cingulate and
left amygdala in all subjects ............................................................................................ 112
Figure A.I-5. Structural connectivity between the left subgenual anterior cingulate and
right amygdala in all subjects ......................................................................................... 113
Figure A.I-6. Structural connectivity between the left subgenual anterior cingulate and
left anterior insula in all subjects .................................................................................... 114
ix
Figure A.I-7. Structural connectivity between the left subgenual anterior cingulate and
right anterior insula in all subjects .................................................................................. 115
Figure A.I-8. Structural connectivity between the left subgenual anterior cingulate and
left lateral thalamus in all subjects .................................................................................. 116
Figure A.I-9. Structural connectivity between the left subgenual anterior cingulate and
left medial thalamus in all subjects ................................................................................. 117
Figure A.I-10. Structural connectivity between the left subgenual anterior cingulate and
right medial thalamus in all subjects ............................................................................... 118
Figure A.I-11. Structural connectivity between the left subgenual anterior cingulate and
right lateral thalamus in all subjects................................................................................ 119
Figure A.I-12. Structural connectivity between the right subgenual anterior cingulate and
periaqueductal grey in all subjects .................................................................................. 120
Figure A.I-13. Structural connectivity between the right subgenual anterior cingulate and
left hypothalamus in all subjects ..................................................................................... 121
Figure A.I-14. Structural connectivity between the right subgenual anterior cingulate and
right hypothalamus in all subjects................................................................................... 122
Figure A.I-15. Structural connectivity between the right subgenual anterior cingulate and
left amygdala in all subjects ............................................................................................ 123
Figure A.I-16. Structural connectivity between the right subgenual anterior cingulate and
right amygdala in all subjects ......................................................................................... 124
Figure A.I-17. Structural connectivity between the right subgenual anterior cingulate and
right anterior insula in all subjects .................................................................................. 125
Figure A.I-18. Structural connectivity between the right subgenual anterior cingulate and
left anterior insula in all subjects .................................................................................... 126
Figure A.I-19. Structural connectivity between the right subgenual anterior cingulate and
left lateral thalamus in all subjects .................................................................................. 127
Figure A.I-20. Structural connectivity between the right subgenual anterior cingulate and
left medial thalamus in all subjects ................................................................................. 128
Figure A.I-21. Structural connectivity between the right subgenual anterior cingulate and
right medial thalamus in all subjects ............................................................................... 129
Figure A.I-22. Structural connectivity between the right subgenual anterior cingulate and
right lateral thalamus in all subjects................................................................................ 130
Figure A.I-23. Structural connectivity between the right subgenual anterior cingulate
(seed H) and left anterior midcingulate in all subjects ................................................... 131
Figure A.I-24. Structural connectivity between the right subgenual anterior cingulate
(seed H) and right anterior midcingulate in all subjects ................................................. 132
Figure A.II-1. Structural connectivity between the left subgenual anterior cingulate and
periaqueductal grey in women. ....................................................................................... 133
Figure A.II-2. Structural connectivity between the left subgenual anterior cingulate and
left hypothalamus in women ........................................................................................... 134
Figure A.II-3. Structural connectivity between the left subgenual anterior cingulate and
right hypothalamus in women ......................................................................................... 135
Figure A.II-4. Structural connectivity between the left subgenual anterior cingulate and
left amygdala in women .................................................................................................. 136
Figure A.II-5. Structural connectivity between the left subgenual anterior cingulate and
right amygdala in women................................................................................................ 137
x
Figure A.II-6. Structural connectivity between the left subgenual anterior cingulate and
left anterior insula in women .......................................................................................... 138
Figure A.II-7. Structural connectivity between the left subgenual anterior cingulate and
right anterior insula in women ........................................................................................ 139
Figure A.II-8. Structural connectivity between the left subgenual anterior cingulate and
left lateral thalamus in women ........................................................................................ 140
Figure A.II-9. Structural connectivity between the left subgenual anterior cingulate and
left medial thalamus in women ....................................................................................... 141
Figure A.II-10. Structural connectivity between the left subgenual anterior cingulate and
right medial thalamus in women ..................................................................................... 142
Figure A.II-11. Structural connectivity between the left subgenual anterior cingulate and
right lateral thalamus in women ...................................................................................... 143
Figure A.II-12. Structural connectivity between the right subgenual anterior cingulate and
periaqueductal grey in women ........................................................................................ 144
Figure A.II-13. Structural connectivity between the right subgenual anterior cingulate and
left hypothalamus in women ........................................................................................... 145
Figure A.II-14. Structural connectivity between the right subgenual anterior cingulate and
right hypothalamus in women ......................................................................................... 146
Figure A.II-15. Structural connectivity between the right subgenual anterior cingulate and
left amygdala in women .................................................................................................. 147
Figure A.II-16. Structural connectivity between the right subgenual anterior cingulate and
right amygdala in women................................................................................................ 148
Figure A.II-17. Structural connectivity between the right subgenual anterior cingulate and
right anterior insula in women ........................................................................................ 149
Figure A.II-18. Structural connectivity between the right subgenual anterior cingulate and
left anterior insula in women .......................................................................................... 150
Figure A.II-19. Structural connectivity between the right subgenual anterior cingulate and
left lateral thalamus in women ........................................................................................ 151
Figure A.II-20. Structural connectivity between the right subgenual anterior cingulate and
lateral medial thalamus in women .................................................................................. 152
Figure A.II-21. Structural connectivity between the right subgenual anterior cingulate and
right medial thalamus in women ..................................................................................... 153
Figure A.II-22. Structural connectivity between the right subgenual anterior cingulate and
right lateral thalamus in women ...................................................................................... 154
Figure A.II-23. Structural connectivity between the right subgenual anterior cingulate
(seed H) and left anterior midcingulate in women .......................................................... 155
Figure A.II-24. Structural connectivity between the right subgenual anterior cingulate
(seed H) and right anterior midcingulate in women ....................................................... 156
Figure A.III-1. Structural connectivity between the left subgenual anterior cingulate in
men. ................................................................................................................................. 157
Figure A.III-2. Structural connectivity between the left subgenual anterior cingulate and
left hypothalamus in men ................................................................................................ 158
Figure A.III-3. Structural connectivity between the left subgenual anterior cingulate and
right hypothalamus in men.............................................................................................. 159
Figure A.III-4. Structural connectivity between the left subgenual anterior cingulate and
left amygdala in men ....................................................................................................... 160
xi
Figure A.III-5. Structural connectivity between the left subgenual anterior cingulate and
right amygdala in men .................................................................................................... 161
Figure A.III-6. Structural connectivity between the left subgenual anterior cingulate and
left anterior insula in men ............................................................................................... 162
Figure A.III-7. Structural connectivity between the left subgenual anterior cingulate and
right anterior insula in men ............................................................................................. 163
Figure A.III-8. Structural connectivity between the left subgenual anterior cingulate and
left lateral thalamus in men ............................................................................................. 164
Figure A.III-9. Structural connectivity between the left subgenual anterior cingulate and
left medial thalamus in men ............................................................................................ 165
Figure A.III-10. Structural connectivity between the left subgenual anterior cingulate and
right medial thalamus in men .......................................................................................... 166
Figure A.III-11. Structural connectivity between the left subgenual anterior cingulate and
right lateral thalamus in men........................................................................................... 167
Figure A.III-12. Structural connectivity between the right subgenual anterior cingulate
and periaqueductal grey in men ...................................................................................... 168
Figure A.III-13. Structural connectivity between the right subgenual anterior cingulate
and left hypothalamus in men ......................................................................................... 169
Figure A.III-14. Structural connectivity between the right subgenual anterior cingulate
and right hypothalamus in men ....................................................................................... 170
Figure A.III-15. Structural connectivity between the right subgenual anterior cingulate
and left amygdala in men ................................................................................................ 171
Figure A.III-16. Structural connectivity between the right subgenual anterior cingulate
and right amygdala in men .............................................................................................. 172
Figure A.III-17. Structural connectivity between the right subgenual anterior cingulate
and right anterior insula in men ...................................................................................... 173
Figure A.III-18. Structural connectivity between the right subgenual anterior cingulate
and left anterior insula in men ........................................................................................ 174
Figure A.III-19. Structural connectivity between the right subgenual anterior cingulate
and left lateral thalamus in men ...................................................................................... 175
Figure A.III-20. Structural connectivity between the right subgenual anterior cingulate
and left medial thalamus in men ..................................................................................... 176
Figure A.III-21. Structural connectivity between the right subgenual anterior cingulate
and right medial thalamus in men ................................................................................... 177
Figure A.III-22. Structural connectivity between the right subgenual anterior cingulate
and right lateral thalamus in men .................................................................................... 178
Figure A.III-23. Structural connectivity between the subgenual anterior cingulate (seed
H) and left anterior midcingulate in men ........................................................................ 179
Figure A.III-24. Structural connectivity between the subgenual anterior cingulate (seed
H) and right anterior midcingulate in men ...................................................................... 180
xii
List of Appendices
Appendices ...................................................................................................................... 108
Appendix I: Tractograms in both Women and Men ............................................... 109
Appendix II: Tractograms in Women....................................................................... 133
Appendix III: Tractograms in Men ............................................................................ 157
xiii
List of Abbreviations
ADC Apparent diffusion coefficient
aINS Anterior insula
aMCC Anterior midcingulate
Amy Amygdala
BA Brodmann area
BOLD Blood oxygenation level dependent
CL Centrolateral nucleus
CNS Central nervous system
CPM Conditioned pain modulation
DBS Deep brain stimulation
DLF Dorsolateral funiculus
DLPFC Dorsolateral prefrontal cortex
DNIC Diffuse noxious inhibitory control
DTI Diffusion tensor imaging
F Female
FA Fractional anisotropy
FC Functional connectivity
fMRI Functional magnetic resonance imaging
FSL Functional magnetic resonance imaging of brain software library
FWHM full width at half max
Glu Glutamate
Hi Hippocampus
Hy Hypothalamus
ICA Independent component analysis
INS Insula
L Left
LC Locus ceruleus nucleus
M Male
MD Medial dorsal thalamic nucleus
MD Mean diffusivity
MDvc Ventrocaudal part of medial dorsal nucleus
NACs Nucleus accumbens
NCF Nucleus cuneiformis
NMDAR N-Methyl-D-aspartic acid receptor
NRM Nucleus raphe magnus
OFC Orbitofrontal cortex
PAG Periaqueductal gray
xiv
PB Parabrachial nucleus
PCA Principal component analysis
PCC Posterior cingulate cortex
Pf Parafascicular nucleus
PFC Prefrontal cortex
pgACC Pregenual ACC
pINS Posterior insula
PVG Paraventricular gray
R Right
RF Reticular formation
ROI Region of interest
rs Resting state
RVM Rostroventral medulla
S1 Primary somatosensory cortex
S2 Secondary somatosensory cortex
s24 Subgenual BA24
s32 Subgenual BA32
SC Structural connectivity
SC DH Spinal cord dorsal horn
sgACC Subgenual anterior cingulate
SHT Spinohypothalamic tract
SPM Statistical parametric mapping
STT Spinothalamic tract
TE Echo time
Th Thalamus
TI Inversion time
TPJ Temporoparietal junction
TR Repetition time
VAN Ventral attention network
VAS Visual analog scale
VL Ventrolateral nucleus
VMpo Posterior part of ventromedial nucleus
VP Ventroposterior nucleus
1
1 INTRODUCTION, AIM, AND HYPOTHESIS
Pain perception is a product of both incoming signals relayed from the body to the brain,
and modulatory pathways that include descending pathways that arise from cortical and
brainstem regions. Two types of modulation are known as pain adaptation (to sustained
stimuli) and pain habituation (to repeated stimuli). Dysfunctional pain habituation has
been associated with chronic pain conditions (Flor, et al., 2004; Peters, et al., 1989;
Proietti Cecchini, et al., 2003; Valeriani, et al., 2003). Thus, uncovering the mechanism
of pain habituation might lead to more targeted treatments for chronic pain. An fMRI
study has shown that pain habituation over the course of eight days is associated with an
increase in pain-evoked activity of the subgenual anterior cingulate cortex (sgACC, area
25) that then resolves after one year (Bingel, et al., 2008; Bingel, et al., 2007). Given the
role of sgACC and of descending modulation network in pain habituation, these findings
indicate a possible connection between the sgACC and the descending pain
antinociceptive system mediating pain habituation. Further, psychophysical studies have
shown that women exhibit greater heat pain adaptation to a prolonged painful stimulus
and greater habituation to repeated painful stimuli than men (Hashmi and Davis, 2009).
The neural mechanism underlying this sex difference in habituation is unknown but given
the findings from the Bingel group, it could involve sgACC connectivity with
antinociceptive pathways.
Thus, the AIM of this thesis was to delineate the connectivity of the sgACC to other
brain regions implicated in pain and its modulation and to determine whether there are
sex differences in these connectivities.
Towards this aim, the HYPOTHESIS tested was:
Women have stronger functional connectivity (FC) and structural connectivity (SC) than
men between the sgACC and 1) brain areas implicated in pain processing including the
thalamus; as well as 2) key nodes of the descending antinociceptive system including the
periaqueductal gray (PAG), the raphe nucleus, insula, amygdala, and hypothalamus.
2
This thesis tested the two hypotheses from the perspective of both functional and
structural brain connectivity by using resting state functional magnetic resonance imaging
(fMRI) and MRI-diffusion tensor imaging based probabilistic tractography methods.
3
2 LITERATURE REVIEW
2.1 Pain
The International Association for the Study of Pain has defined pain as ―an unpleasant
sensory and emotional experience associated with actual or potential tissue damage, or
described in terms of such damage‖ (Merskey and Bogduk, 1994). Pain exists to motivate
an organism to avoid noxious stimuli, thereby protecting it against injury (Woolf, 2004).
Pain can be classified by cause and by duration. The former divides pain into nociceptive,
inflammatory, and neuropathic groups. The latter divides pain into transient, acute, and
chronic types. Transient pain occurs as a result of small or no tissue damage whereby the
pain stops when the stimulus is removed or shortly afterwards (Melzack and Wall, 1996).
However, acute pain typically endures somewhat longer until the healing process takes
place (Fields, et al., 1999). Examples of acute pain include traumatic and post-surgical
pain. Chronic pain is a persistent and debilitating type of pain, which lingers on even
after healing and after the cause of pain appears to be gone (typically greater than 3-6
months).
The pain experience is often described in terms of three dimensions: sensory-
discriminative, cognitive-evaluative, and affective-emotional (Melzack and Casey, 1968).
The sensory dimension describes attributes of pain such as its location, duration, temporal
characteristics, quality, and intensity. While the cognitive-evaluative dimension takes into
consideration of attention, past experience, and cognitive factors that modulates sensory
intensity and quality, the affective-motivational dimension describes the unpleasant
feeling of pain (Melzack and Casey, 1968).
4
2.1.2 Ascending Pain Pathways
There are many types of primary afferent nociceptive neurons that fall into the general
category of either small diameter, myelinated A-delta nociceptors that conduct signals at
approximately 3-30m/s (Melzack and Wall, 1996), and smaller diameter, unmyelinated
C-fibre nociceptors with conduction velocities of approximately 0.5-2.5m/s (Melzack and
Wall, 1996). Both A-delta and C-fibre nociceptors can respond to either a single modality
or to multiple modalities (i.e., polymodal) of stimulus energies (e.g., heat, cold,
mechanical, and chemical). Nociceptors can also be classified according to their
thresholds (e.g., A-mechanoheat type 1 and type 2 nociceptors), encoding of stimulus
intensity, and adaptation properties to sustained stimuli (e.g., slowly adapting vs rapidly
adapting) (Meyer, et al., 2006).
The primary afferents nociceptors are first order neurons and their cell bodies reside in
the dorsal root ganglia. The sensory information received at the periphery is conducted
along the axon into the CNS to terminate in the spinal dorsal horn – laminae I, IV, and V
(Fields, et al., 1999). Axons of the second order spinal cord neurons cross over to the
contralateral side of the spinal cord and ascend to the thalamus where they synapse onto
third order neurons. Some spinal cord neurons also send projects to the brainstem. Thus,
peripheral nociceptive signals can reach the brain via many ascending pathways,
including spinomedullary projections, spinobulbar projections, the spinohypothalamic
tract (SHT) and the spinothalamic tract (STT) (Dostrovsky and Craig, 2006). The third
order neurons then project to various sensory processing regions in the brainstem and
cortex.
The STT relays sensory information related to pain, temperature, and crude touch from
the body to the cortex. The STT consists of both an anterior and lateral system. The
anterior STT is located within the ventral funiculus, and the lateral STT resides within the
lateral funiculus. These two pathways synapse onto thalamic neurons in many medial and
lateral thalamic nuclei including posterior part of ventromedial nucleus (VMpo),
ventroposterior nucleus (VP), ventrolateral nucleus (VL), centrolateral nucleus (CL),
parafascicular nucleus (Pf) , and ventrocaudal part of medial dorsal nucleus (MDvc).
These thalamic neurons then relay the sensory information to various cortical regions for
5
further processing. Neurons in the VP receive input from laminae IV and V and project to
the somatosensory cortex (Rausell and Jones, 1991). There is a dense projection of STT
axons from lamina I to VMpo neurons (Craig, 2003), from where sensory information is
then relayed to the dorsal posterior insula. Moreover, MDvc receives moderate amount of
STT projections from lamina I (Albe-Fessard, et al., 1975) and relays the sensory signals
to neurons in BA24 of cingulate cortex. Other than MDvc, the remaining MD thalamic
nuclei relay the sensory information to prefrontal cortex (PFC) and orbitofrontal cortex
(OFC) (Craig, 2003; Ray and Price, 1993). Furthermore, VMpo sends projections to
dorsal posterior insula, while VPI projects to second somatosensory cortex (S2) and
retroinsula (Dostrovsky and Craig, 2006).
Pain perception is thought to be the final product of activity among multiple cortical
regions including the primary somatosensory cortex (S1), insula, anterior and mid
cingulate cortex (ACC/MCC), and PFC (Treede, et al., 1999). There are several other
cortical regions that have been implicated in pain processes. For instance, the dorsolateral
PFC (DLPFC) can modulate pain processing, which was shown by the successful use of
DLPFC as a target in repetitive transcranial magnetic stimulation (rTMS) for treating
chronic migraine patients (Brighina, et al., 2004). Based on human electrophysiology
studies, single neurons in the MCC can be specifically activated by noxious stimuli
(Hutchison, et al., 1999). Another electrophysiology study on rabbits revealed that
noxious colon distention increased activity in ACC/MCC, suggesting the role of
ACC/MCC in nociception processing (Sikes, et al., 2008).
2.1.3 Descending pain modulation
Pain is thought to be attenuated through endogenous mechanisms in many situations, e.g.,
accidents or battlefield (Beecher, 1946). One such modulation system is through the
descending pain modulatory pathway, with main hubs in the periaqueductal grey (PAG)
and nucleus raphe magnus (NRM) of the rostroventral medulla (RVM) that impact the
activity of neurons in the spinal cord dorsal horn. In this pathway, PAG sends neuronal
projections to RVM (Beitz, 1982b), which sends serotonergic projections to the spinal
6
cord dorsal horn via dorsal part of lateral fasciculus; these serotonergic projections inhibit
the dorsal horn nociceptors (Basbaum and Fields, 1984).
This endogenous descending system was first discovered in 1969, when electrical
stimulation in the dorsolateral central grey (an area now referred to as the PAG) in rats
was shown to produce analgesia without adverse effects on motor function (Reynolds,
1969). In 1977, deep brain stimulation of the PAG/periventricular gray (PVG) was shown
to be effective in producing analgesia in six patients with chronic pain (Hosobuchi, et al.,
1977). The following year, electrophysiology studies revealed that electrical stimulation
and opiate microinjection at PAG could excite cells in the NRM, whose activity was
reduced when opioid antagonist, naloxone, was injected in PAG (Fields and Anderson,
1978), implicating raphe’s role in modulating the nociceptive afferent signals from spinal
dorsal horn. Finally, in the same year, an endogenous pain control circuitry was
proposed. Activated by pain, the circuit consisted of cells in the midbrain (PAG) that
excite serotonergic neurons in the rostral medulla, which then inhibit the nociceptive
afferent input to spinal cord dorsal horn cells (Basbaum and Fields, 1978).
Opioids play an important role in the descending pain modulation, and the main opioids
receptors involved are mu and kappa receptors (Stamford, 1995). How are the
nociceptive afferent signals modulated? They are thought to be regulated in a top-down
fashion by higher brain areas including cingulo-frontal regions, amygdala, and
hypothalamus (Hadjipavlou, et al., 2006). Early probabilistic tractography study has
shown significant SC between PAG and cortical regions (PFC, amygdala, thalamus,
hypothalamus, RVM) (Kong, et al., 2010b), providing the anatomical basis for
modulation of pain.
In another study, the activity in the anterior cingulate cortex (ACC) reduced pain
perception from noxious stimuli in rats with nerve injuries; this pain attenuation required
an intact PAG, suggesting a pain modulation pathway involving ACC and PAG (LaBuda
and Fuchs, 2005). In rats, antinociception, which was facilitated by microinjection of a
mu receptor agonist into the amygdala, was modulated by opioid microinjections into
either PAG or RVM, suggesting that antinociception may involve the neurocircuitry
7
involving these three regions (Helmstetter, et al., 1998). In rhesus monkeys, bilateral
lesions in the amygdala led to lack of antinociception and fear response, implicating the
role of amygdala in antinociception (Manning, et al., 2001). In rat studies, the
hypothalamus was postulated to release arginine vasopressin to NRM, leading to pain
modulation and inhibition (Yang, et al., 2008; Yang, et al., 2009a; Yang, et al., 2009b).
2.1.3.1 Periaqueductal grey
The PAG is a region located medially around the cerebral aqueduct in the tegmentum of
the midbrain, in a region that extends from the opening of the third ventricle to the
pericerulear area (regions surrounding locus ceruleus) in the pons (Basbaum and
Bushnell, 2009; Heinricher and Ingram, 2009). The hypothalamus stretches into the PAG,
which terminates at the anterior border of the fourth ventricle (Nieuwenhuys, et al.,
2008). Animal studies have reported that opiate injection at PAG induces analgesia
(Jacquet and Lajtha, 1976; Lewis and Gebhart, 1977), which can then be blocked by
opiate antagonist injection (Jacquet and Lajtha, 1976; Tsou and Jang, 1964; Vigouret, et
al., 1973).
The PAG receives input from the spinal cord ascending fibres – lamina I (Azkue, et al.,
1998; Hylden, et al., 1986; Menetrey, et al., 1982), nucleus cuneiformis (NCF), nucleus
raphe magnus (Beitz, 1982a), PFC, insula (INS), amygdala, and the hypothalamus (An, et
al., 1998; Bandler and Shipley, 1994; Floyd, et al., 2000). The output targets of PAG
neurons include the RVM, locus coeruleus/subcoeruleus (LC/SC), A5 noradrenergic cell
group, pontine parabrachial nuclei, nucleus tractus solitarius (NTS), hypothalamus, and
amygdala (Basbaum and Bushnell, 2009; Heinricher and Ingram, 2009). In addition,
PAG projects to the medial thalamus and to OFC in rats (Cameron, et al., 1995a;
Coffield, et al., 1992).
The output of the PAG varies across its subregions. The ventrolateral PAG projects to the
ventrolateral RVM and neighbouring reticular formation (Fields, et al., 1999). The
8
dorsolateral PAG projects to the pontine tegmentum and ventrolateral medulla - a region
of autonomic control (Bajic and Proudfit, 1999; Van Bockstaele, et al., 1991).
Deep brain stimulation (DBS), targeting the PAG/PVG region, has produced effective in
pain treatment but its effectiveness varies across studies and patient populations (Bittar,
et al., 2005) (Adams, et al., 1974; Dieckmann and Witzmann, 1982; Gybels and Kupers,
1987; Hosobuchi, 1986; Hosobuchi, et al., 1975; Kumar, et al., 1997; Levy, et al., 1987;
Mazars, 1975; Meyerson, et al., 1993; Nguyen, et al., 1997; Parrent, et al., 1992; Plotkin,
1982; Richardson and Akil, 1977; Siegfried, et al., 1980; Tasker and Vilela Filho, 1995;
Tsubokawa, et al., 1991a; Tsubokawa, et al., 1991b; Turnbull, et al., 1980; Young, et al.,
1985).
2.1.3.2 Rostral ventromedial medulla
The RVM is an important intermediary region between PAG and the spinal cord dorsal
horn. It contains the reticular formation and the nucleus raphe magnus (Fields, et al.,
2006). Animal studies have identified a prominent sensory projection ascending from the
spinal cord to nucleus reticularis gigantocellularis, which projects to the raphe within
RVM (Basbaum, et al., 1978; Gallager and Pert, 1978). The RVM also receives input
from the PAG via projections from dorsal raphe via serotonergic neurons (Beitz, 1982b)
and from the insula (Hermann, et al., 1997). Several studies have demonstrated that the
RVM transmits information from the PAG to the spinal cord. For instance, PAG
stimulation was less effective in facilitating analgesia when RVM was inhibited by
lidocaine (Gebhart, et al., 1983) or amino acid antagonist injection (Aimone and Gebhart,
1986; Fields, et al., 1991).
Via the dorsolateral funiculus (Abols and Basbaum, 1981), the RVM projects to neurons
in the dorsal horn of the spinal cord including laminae I, II, and V (Basbaum and Fields,
1978), which receive nociceptive afferent signals. Signals along this pathway can be
attenuated by interneurons in laminae I and II, which contain inhibitory neurotransmitters
including enkephalin and GABA (Todd, et al., 1992).
9
The RVM consists of neurons that can both inhibit and facilitate nociception. These
actions are due to on cells and off cells in the RVM. While the former fire when RVM is
facilitating pain, the latter fire when RVM is inhibiting pain, leading to analgesia
(Heinricher, et al., 1989; Heinricher and Ingram, 2009).
2.1.4 Adaptation and habituation
Pain adaptation has been used to describe the decrease of pain during a single
administration of painful stimulus (Greene and Hardy, 1962). A related phenomenon is
called pain habituation, which occurs when there is a decrease of pain when a stimulus is
repeated (Bingel, et al., 2007; Glaser and Whittow, 1953; Hashmi and Davis, 2009). A
term related to the perceptual phenomena of habituation is ―fatigue‖, which has been used
to describe a neurophysiological phenomena whereby A-delta or C nociceptive afferent
activity attenuates over the administration of multiple, repeated noxious stimuli (Peng, et
al., 2003). Nociceptors can recover from fatigue within minutes. For instance, clinical
studies have shown that nociceptive-C fibres require about 4-10 min to recover after 49 C
thermal stimulation (LaMotte and Campbell, 1978). On the contrary, pain habituation
may last for days.
Habituation to thermal stimuli can occur in response to stimuli delivered in a relatively
short time frame or long time frames. For instance, habituation to repeated heat pain
stimuli over many minutes was observed in a psychophysics study of 32 subjects
(Hashmi and Davis, 2009). On the other hand, habituation to heat pain was also observed
when delivered in test sessions repeated over a period of 22 days in an fMRI study that
found increased sgACC activity in parallel to the habituation (Bingel, et al., 2007). This
group also reported that subjects that showed habituation had cortical thickening of the
MCC and S1 cortex (Teutsch, et al., 2008).
Pain habituation is associated with the antinociceptive system in the nervous system. The
activation of sgACC during habituation suggests its importance in mediating habituation.
In fact, sgACC is associated with pain modulation by hypnosis- and placebo-induced
analgesia (Kupers, et al., 2005; Petrovic, et al., 2002). Such endogenous antinociception
10
is associated with endogenous opioid systems (Petrovic, et al., 2002). Understanding the
mechanism of pain habituation is important, because dysfunctional habituation to pain
has been proposed as a potential mechanism that may lead to chronic pain (Bingel, et al.,
2007).
2.1.5 Diffuse noxious inhibitory controls
The term ―diffuse noxious inhibitory controls‖ (DNIC) refers to a phenomenon whereby
a noxious-evoked response is inhibited by another noxious stimulus applied to remote,
widespread areas of the body. This effect was initially described in a 1979
electrophysiology study in which the response of dorsal horn neurons to noxious stimuli
was inhibited by heterotopic noxious stimuli such as noxious pinch, transcutaneous
electrical nerve stimulation (TENS), and bradykinin (Le Bars, et al., 1979a). Thereafter,
Le Bars’ group coined the term, ―diffuse noxious inhibitory controls‖, because the stimuli
that produced this effect a) could be applied to sites anywhere in the body outside of the
test stimulus receptive field, and b) needed to be in the noxious range. They also found
that the duration of the conditioning stimulus determined the length of the DNIC effect
(Le Bars, et al., 1979a), and that DNIC effects were only found for neurons that received
inputs from both A-fibres and C-fibres (Le Bars and Willer, 1988). In human studies,
the DNIC effect can last about 5 minutes (Campbell, et al., 2008; Kakigi, 1994; Kosek
and Ordeberg, 2000) although other studies found slightly longer DNIC effects lasting 5-
8 minutes (France and Suchowiecki, 1999; Serrao, et al., 2004). Recently, a newer term –
―conditioned pain modulation (CPM)‖ - was introduced for studies of DNIC-like effects
in humans, replacing ―DNIC‖, which is reserved for usage in animal studies (Yarnitsky,
2010; Yarnitsky, et al., 2010).
The DNIC effect related to an earlier clinical phenomenon, in which noxious stimuli,
administered via TENS, led to pain relief in chronic pain patients (Melzack, 1975). The
cause of this pain relief, as reflected by results of McGill Pain Questionnaire, was at the
time referred to as ―counter-irritation‖.
11
Over the last 50 years, studies have shown that DNIC requires supraspinal brain regions.
For instance, in rat studies, the DNIC effect was eliminated by spinal cord transection at
the cervical level (Cadden, et al., 1983; Le Bars, et al., 1979b). Additional studies in cats
showed that noxious electrical stimulation inhibited neuronal activity in the dorsal horn
due to a noxious heat stimulus, illustrating the DNIC effect. However, this effect was
gone after lidocaine injection in the raphe nucleus, implying the necessity of raphe
nucleus activity in producing the DNIC effect (Morton, et al., 1987). In the 1990s, lesion
studies in rats revealed that the PAG, cuneiformis nucleus, parabrachial nucleus
(Bouhassira, et al., 1990), rostral ventromedial medullar (Bouhassira, et al., 1993), and
the subnucleus reticularis dorsalis (SDR) (Bouhassira, et al., 1992) are required for
DNIC. Involvement of a supraspinal mechanism was further supported by clinical
findings in which tetraplegic patients with severed spinal cords lacked DNIC-like effects
(Roby-Brami, et al., 1987).
2.1.6 Sex differences
Many chronic pain conditions show sex differences in prevalence, often being
predominant in women (Berkley, 1997). In some cases, this has been presumed to be due
to differences in pain sensitivity (Mogil, 2012; Racine, et al., 2012a; Racine, et al.,
2012b). For example, many studies have found that women have lower pain thresholds,
especially heat-evoked pain (Fillingim, et al., 2009). Also, among 23 studies that
involved experimental heat pain, 81% (12/17) reported lower pain threshold in women,
and 94% (15/16) of the studies showed less pain tolerance in women (Fillingim, et al.,
2009). However, using sustained, repeated suprathreshold stimuli, a different picture of
sex differences emerged in that women showed greater within-stimulus adaptation and
habituation to repeated painful stimuli (Hashmi and Davis, 2009; Hashmi and Davis,
2010). Thus, this issue of sex differences in pain sensitivity is complex and depends upon
the type of measure used to assess pain sensitivity.
In a 2010 review, among 13 studies that examined sex differences in DNIC, 50% showed
significant sex differences, with the majority demonstrating greater DNIC effects in men
12
than women (Serrao, et al., 2004). For instance, in 2003, a study used contact heat pain as
the test stimulus and muscle pain as the conditioning stimulus to induce DNIC in healthy
controls and female fibromyalgia patients. While healthy male controls showed
significant DNIC effect, this effect was absent in both female controls and patients
(Weissman-Fogel, et al., 2008). Another study in 2004 utilized the nociceptive flexion
reflex – a nociception correlate - as the test stimulus and cold pressor test as the
conditioning stimulus to elicit DNIC in healthy men and women. Specifically, they found
stronger DNIC in men than women (van Wijk and Veldhuijzen, 2010). Further, in 2008, a
study employed contact heat pain as the test stimulus and muscle pain as the conditioning
stimulus on healthy men and women to induce DNIC. They observed greater DNIC in
men than women (Staud, et al., 2003).
2.1.6.1 Sex difference in pain processing
Sex differences may play a role in the variation of cortical response to pain (Apkarian, et
al., 2005; Fillingim, et al., 2009), which has been supported by electrophysiology, PET,
and fMRI studies. For example, mechanical C-fibre nociceptive thresholds in rats were
found to be higher in females than males. However in humans, many studies have
reported that women have lower pain threshold (Racine, et al., 2012a).A number of PET
studies have shown sex differences in cortical activation in subjects receiving noxious
stimuli. For instance, in a PET study found that 50°C contact heat stimuli evoked greater
pain , and contralateral thalamus and anterior insula activation to a greater extent in
women than men (Paulson, et al., 1998). In another PET study, laser thermal stimuli were
administered with equal subjective pain intensity levels to the back of their right hands.
Compared to men, women required less laser energy and had greater activation of the
ACC while men had greater activation of the contralateral prefrontal cortex, S1, S2, and
insula (Derbyshire, et al., 2002). In another PET study involving 10 women and 10 men
responding to laser heat stimuli, men showed more activations in areas of the parietal
cortex, S2, prefrontal cortex and insula, whereas women had more activation in the
perigenual cortex – BA24 (Derbyshire, et al., 2002).
13
A number of fMRI studies have also shown sex differences in cortical activation in
subjects receiving noxious stimuli. For example, in an fMRI study involving 34 women
and 26 men, images were acquired during low (5/20 sensory scale) and high pain (15/20)
invoked by thermal stimulation to the right arm; results. The results revealed that men
had greater PAG-FC with left amygdala and left thalamus, left precentral gyrus, right
cuneus, right putamen, right inferior supplemental motor area (SMA), and right caudate
compared to women. On the contrary, women had greater PAG functional connectivity
(FC) with right superior SMA than men (Linnman, et al., 2012). In another fMRI study
examining intrinsic FC , women were found to have greater PAG-FC with MCC, and
men demonstrated greater PAG-FC with left medial orbital prefrontal cortex and uncus;
right insula and operculum; and PFC (Kong, et al., 2010b). In another fMRI study,
temperature levels corresponding to low (5/20 sensory scale) and high (15/20 sensory
scale) heat pain intensities were determined by individual subjects. During low pain
trials, men had stronger BOLD signals in left insula/operculum than women. During high
pain trials, men had stronger BOLD signals in the insula/operculum, S2, thalamus, ACC,
left brainstem, MPFC, and right DLPFC. Women did not show stronger BOLD signals in
any trials than men, and both sexes demonstrated similar cortical deactivations (Kong, et
al., 2010a). In another fMRI study involving 18 women and 18 men, the subjects received
electrical stimulation in their left index finger tips at four different intensities: below
perceivable threshold; perceivable and innocuous; mildly painful; and moderately painful
(Straube, et al., 2009). In the last intensity level, women demonstrated greater activation
in the pregenual MPFC than men. Compared to men, women showed significantly lower
threshold for noxious stimulations in terms of electrical current level (Straube, et al.,
2009). Furthermore an fMRI study involving 17 women and 11 men who received
noxious heat stimuli in the dorsum of their left foot, found that men had greater BOLD
signal amplitude than women in the S1 and DLPFC (Moulton, et al., 2006). These studies
suggest a sex difference in the cortical circuitry for processing incoming nociceptive
information.
14
2.1.6.2 Sex difference in somatosensation and pain
2.1.6.2.1 Sensory discriminations
There have been many studies that reported women’s superior ability than men to
discriminate thermal sensory information. In a study involving 42 women and 29 men,
two thermostimulators were set against the dorsum of the left feet of the subjects, who
were asked to determine whether one thermostimulator is warmer or colder than the
other. The study found that women had lower thermal discrimination thresholds in terms
of temperature difference than men (Doeland, et al., 1989), indicating women’s greater
ability to discriminate temperature differences. Another study involving 20 women and
20 men who rated their pain level using a visual analog scale (VAS), the subjects
received two sets of noxious heat stimuli: 45-49°C and 46-50°C. The results revealed that
women were significantly better at discriminating between the sets of noxious stimuli
between sessions than men (Feine, et al., 1991).
There have also been several studies demonstrating women’ superior ability to
discriminate mechanical sensory information than men. For instance, in a study involving
20 women and 28 men, brush stimulation was applied to subjects’ skin covering their
inferior mandible. Compared to men, women had greater peak sensitivity, ability to
discern the direction of motion, and motion velocity of the brush stimulation (Essick, et
al., 1988).
2.1.6.2.2 Hormonal effects
Sex differences also exist in the central nervous system and may be influenced by sex
hormones such as testosterone and estrogen. An immunocytochemistry study in rats
showed that estrogen could significantly reduce the endorphine – an endogenous opioid
peptide - levels in the hypothalamus, anterior pituitary, and pituitary neurointermediate
lobe (Forman and Estilow, 1986). Another immunocytochemistry study in rats showed
that the intermediate region of the locus ceruleus nucleus (LC) – a region that plays a role
15
in pain modulation and arousal- is larger in females than males (Luque, et al., 1992).
Such volume difference in LC disappears when female rats were treated with testosterone
(Guillamon, et al., 1988). In rat studies using opioid stressor (intermittent cold-water
swims) and nonopioid stressor (continuous cold-water swims), the induced analgesia
were decreased by gonadectomy, but the analgesia returns when testosterone was injected
into the animals (Bodnar, et al., 1988).
A number of studies have also been done to show the effect of female hormones in pain
modulation. In rats, administration of progesterone increased the luteinizing hormone
level, which hindered morphine antinociception (Berglund, et al., 1988). Another rat
study showed that the luteinizing hormone-release hormone could avert morphine
antinociception in females (Ratka and Simpkins, 1990). It has been hypothesized that
sensory thresholds decrease in menses, which is supported by the finding that pain
threshold is lowest in the luteal phase and highest in the follicular phase (Aloisi, 2000).
These findings may provide insight into the finding that migraine is associated with high
progesterone and estrogen levels (Epstein, et al., 1975) and lessens with menopause
(Aloisi, 2000). In a rat study, the animals were systematically-injected with estrogen for
ten days, resulting the enlargement of the receptive field of the rats’ mechanoreceptive
trigeminal neurons (Bereiter, et al., 1980). This implies that females may have larger
mechanosensory receptive field than males due to the presence of estrogen.
2.1.6.2.3 Opiate action differences
Sex differences also exist in opiate responses (Apkarian, et al., 2005). Animal studies
have shown that, in male rats, morphine lead to greater analgesic effects than female rats
(Baamonde, et al., 1989; Badillo-Martinez, et al., 1984; Kavaliers and Innes, 1987).
Furthermore, female rats have higher opiate–receptor binding in the hypothalamic
preoptic area than male rats (Hammer, 1984).
2.1.6.2.4 Key Neurotransmitter effects
16
Compared to men, women have greater density of mu opioid receptors in the brain. For
instance, in a PET study involving 18 women and 12 men, women demonstrated greater
mu opioid binding than men in ACC, PFC, parietal cortex, temporal cortex, amygdala,
thalamus, caudate, pons, and cerebellum (Zubieta, et al., 1999). In the same study, post-
menopausal women demonstrated lower mu receptor densities in regions including
amygdala and thalamus than men. In a clinical study of 27 women and 57 men, the
former sex group was found to be more sensitive to morphine than the latter sex group
(Zacny, 2001).
In the pain pathway, primary afferent neurons can potentiate and increase the excitability
of second order neurons when the latter are depolarized and their N-Methyl-D-aspartic
acid receptor (NMDAR) receives glutamate (Glu) - a neurotransmitter from the primary
neuron. Depolarization and Glu binding open and activate the NMDAR, leading to a
cellular cascade that increases the secondary neuron excitability, sensitizing it. Rat
studies have shown that NMDAR activation in females generate 2.8 times larger currents
than in males, making central sensitization easier in females (McRoberts, et al., 2007).
Serotonin plays an important role in descending pain inhibition in the central nervous
system (CNS). It is pronociceptive and mediates inflammation. Rat studies indicate that
reduction of serotonin deprivation in the forebrain due to midbrain lesions could decrease
morphine-induced analgesia (Samanin, et al., 1970). Serotonin levels are inversely related
to estrogen levels (Marcus, 1995), which may suggest that females may have reduced
morphine-induced analgesia because of their higher estrogen and lower serotonin levels
than males .
2.2 Subgenual anterior cingulate cortex
2.2.2 Anatomy and connectivity
The anterior portion of the corpus callosum curves inferiorly and posteriorly, forming a
knee (genu) shape. The part of the ACC that surrounds the genu is known as the
17
perigenual ACC, and is divided into two subregions: pregenual ACC (pgACC) and
subgenual ACC (sgACC). While pgACC is the portion of ACC anterior to the genu,
sgACC is located inferior to the genu. The sgACC consists of BA25 as well as the
subgenual parts of BA32 and of BA24 (Johansen-Berg, et al., 2008; Vogt, 2009).
A number of anatomical tracer studies in monkeys have shown sgACC SC with various
cortical (e.g., BA9) and subcortical (PAG, hypothalamus, raphe nucleus, amygdala,
striatum, and putamen) regions. For instance, retrograde and anterograde tracing
determined that the sgACC was reciprocally connected to the orbitofrontal cortex
(Carmichael and Price, 1996). Another study revealed inputs to area BA25 from regions
in the frontal lobe (BA46, 9, OFC (BA11, 14)) and other regions of the cingulate cortex
(BA24b, 24c, 23b), as well as from the amygdala lateral (LB) and accessory basal (AB)
nuclei (Vogt and Pandya, 1987). Also, BA24 received input from other cingulate regions
including B23 and BA25), frontal lobe (BA9-13, 46), LB, AB, insula, and posterior
hippocampal cortex (Vogt and Pandya, 1987). A tracer study also found BA25 efferents
terminating in the hypothalamus, whereas another set of projections terminated in the
brainstem, including the parabrachial nucleus, raphe nuclei, and PAG (Barbas, et al.,
2003). Moreover, BA25 were found to project to the PAG, lateral parabrachial nucleus,
hypothalamus, and stria terminalis (Freedman, et al., 2000). In another study on macaque
monkeys, BA25 and 32 were reported to project densely to hypothalamus (Ongur, et al.,
1998). A retrograde tracer study on rhesus monkeys, BA24a, 24b, 25 were found to
project to striatum, nucleus accumbens (Kunishio and Haber, 1994). In further study on
macaque monkeys, BA25 was found to project to caudate, accumbens nucleus, and
ventromedial putamen; BA24 was found to project to striatum (Ferry, et al., 2000).
Another tracer study on macaque monkeys showed that BA25 projects to the medial
ventral striatum (Haber, et al., 1995). There is also evidence that the MPFC network
including BA25 and 32 projected to and terminated in the dorsal column of PAG whereas
axons from BA24b and 9 terminated in the lateral column of PAG (An, et al., 1998).
In humans, MRI studies have also revealed sgACC connectivity to various brain regions.
In a DTI study involving 11 male and 6 female healthy subjects, probabilistic
tractography showed a significant number of streamline samples reaching from the
18
sgACC to amygdala, nucleus, accumbens, hypothalamus, and OFC (Johansen-Berg, et
al., 2008). Another DTI study involving 13 healthy subjects revealed ipsilateral sgACC-
connection to medial frontal cortex, ACC, PCC, medial temporal lobe, MD thalamus,
hypothalamus, nucleus accumbens, and dorsal brain stem (Gutman, et al., 2009). In a
resting-state fMRI study involving 15 male and 9 female healthy subjects, the sgACC
was found to be functionally connected to the striatum, OBF, PCC (Margulies, et al.,
2007).
2.2.3 sgACC function
The sgACC has been associated with pain processing and modulation. For example, in a
PET study involving healthy male subjects, sgACC was shown to increase mu opioid
binding during pain evoked by hypertonic saline infusion into jaw muscles and during
placebo analgesia (Zubieta, et al., 2005). Further, in an fMRI study of four women and 15
men, who received laser stimuli on the dorsum of their hands, sgACC was activated
during analgesia induced by placebo pain-relieving cream (Bingel, et al., 2006). In
another fMRI study sgACC was activated when the subjects reported less noxious heat-
evoked pain as a result of distracting task (Bantick, et al., 2002). Thus, the sgACC may
be a cortical node regulating pain habituation. In support of this, is an fMRI study in 20
healthy men that found sgACC increased activity when there was pain habituation
(Bingel, et al., 2007) that persisted for up to 1 year (Bingel, et al., 2008).
The sgACC has also been implicated in sad emotions. For instance, tasks of emotional or
affective content activate sgACC (Allman, et al., 2001) and especially the BA32, which
plays an important role in emotional events (Lane, 1997). sgACC activity is associated
with sadness. Recently, neuronal recordings from the human sgACC have identified cells
responsive to emotional stimuli in emotional categories (e.g., disturbing, sad, happy,
exhilarating) (Laxton, et al., 2013). Furthermore, PET data also indicated that the sgACC
responds to various emotions expressed by sad and happy faces and by recalling
memories associated with these states (George, et al., 1995). In another PET study,
sadness, invoked by having healthy subjects recall sad experiences, increased blood flow
19
to sgACC and blood flow decreased with improvement of clinical depression (Mayberg,
et al., 1999). In a similar PET study involving eight healthy women, whose sadness state
were induced by rehearsing their sad autobiographic scripts, contrasted with neutral
emotional state in subjects, the sad emotional state was associated with activation of the
sgACC and deactivation of right PFC (BA9) (Liotti, et al., 2000). In another clinical PET
study involving 13 unipolar depressed patients, decrease of blood in sgACC was
associated with depression improvement due to paroxetine treatment (Goldapple, et al.,
2004).
It has been proposed that negative memories are stored in sgACC (Vogt, 2009), assigning
it an important role in mediating depression symptoms. For instance, mood disorder
patients and sad healthy subjects have hyperperfusion in sgACC and anterior insula
(Liotti, et al., 2002). Deep brain stimulation (DBS) in the sgACC effectively treats
treatment-resistant depression by reducing sgACC activity (Johansen-Berg, et al., 2008).
In 2008, it was shown that DBS of the sgACC was beneficial in six treatment-resistant
depression patients. As a result, the clinical trial has added 14 more depression patient,
who will receive similar DBS therapy for 12 months (Lozano, et al., 2008). DBS
targeting sgACC has been evaluated, and the results show no adverse effect on cognitive
function in the treated patients, supporting its cognitive safety (McNeely, et al., 2008).
sgACC deactivation has been implicated with anticipation of noxious stimuli. For
instance, in an fMRI study involving 26 subjects who received painful subcutaneous
ascorbic acid injection, sgACC was deactivated when the subjects were anticipating the
noxious stimuli (Porro, et al., 2002). In a PET study involving 16 subjects who received
noxious electrical stimuli in their index and middle fingers, pain anticipation lead to the
reduction of blood flow to the sgACC (Simpson, et al., 2001).
2.3 Resting state BOLD-fMRI
Functional connectivity (FC) refers to the correlation of neuronal activity with respect to
time in structurally distinct brain areas (Aertsen, et al., 1989; Friston, et al., 1993). The
20
neuronal activity is measured in terms of BOLD signal and oscillates slowly at 0.01-0.1
Hz. FC measures the synchrony of blood flow fluctuations, which indirectly reflect
neuronal activity, in different cortical regions. A high synchrony among cortical regions
presumably suggests that the regions work together. Resting state fMRI (rs-fMRI) is a
tool that can be used to examine spontaneous FC in subjects during rest, during which the
subjects are told to think of nothing specifically and to just relax. One way to use rs-fMRI
is the seed method, which depends on a pre-determined model. This method exploits the
FC between a seed, a brain region of interest (ROI), and the rest of the brain. Such
analysis usually concludes with a FC map - a statistical map, which shows the strength of
FC at different brain regions (van den Heuvel and Hulshoff Pol, 2010). FC maps for
individual subjects can be aggregated to form group level or second level FC maps,
which can be used to compare for group differences. Another way to use rs-fMRI is the
model-free method, in which a pre-defined seed is unnecessary. This method explores the
FC patterns across the brain in a data-driven fashion. Under the umbrella of the model-
free methods, a number of techniques can be used, including the independent component
analysis (ICA), principal component analysis (PCA), normalized cut clustering,
Laplacian method, and hierarchical method.
2.3.2 MRI
The nucleus of an element can spin with the spin axis passing through the centre of the
nucleus. Such spin is called the nuclear spin and occurs in all elements in the periodic
table excluding argon and cerium (Brown, 2003). The nuclei consist of positive protons
and neutral neutrons, rendering it a net positive charge. The nuclear spin of a nucleus
generates a nuclear magnetic (dipole) moment at its two opposite ends. As long as an
element has an odd atomic number and weight, its nuclear magnetic dipole will be non-
zero. Elements with non-zero dipoles can be influenced by external magnetic field and
detected in magnetic resonance imaging (MRI) (Brown, 2003). During MRI imaging, a
strong magnetic field (B0: 1.5 ~ 4 T) is applied through a subject; it can force the
precession axes of hydrogen atoms to align parallel (low energy, predominating state) or
21
anti-parallel (high energy state) with the field. The human body mainly consists of fat and
water, which contains hydrogen ions or protons.
At certain a resonant radio frequency or the Larmor frequency, a radio pulse (B1) can
provide just the right amount of energy to flip the nuclear spin of protons from a low
energy state to high energy state, leading to a phenomenon, called the ―nuclear magnetic
resonance‖. This process is called ―excitation‖ or ―transmission‖ (Huettel, et al., 2004).
After the excitation, the proton magnetic moments in the transverse plane will start to go
out of sync (dephase) because protons in different environment spin at slightly different
rates, reducing the magnetization in the transverse plane. To rephrase these magnetic
moments, another radio frequency pulse is applied to flip the net magnetization by 180°.
Finally, as the protons returns to their original spin orientation, they release photons,
which is the MR signal detected by the MRI machine in the process of reception or
detection. As such, the machine can collect MR signals from all the protons in a subject’s
body. The MR signals can then be analyzed to generate a 3D image of the subject.
In addition to the magnetic that provides a continuous B0, there are also gradient magnets
in the x, y, and z plane. These gradient coils generate magnetic field gradients, which
results in a gradient of frequency in the photons released by the subject. This frequency
gradient can then be used to locate of the x, y, and z coordinates of the photon signals
from the subject. During an MRI acquisition, the gradient coils continuously vary the
orientation of their magnetic field gradient, allowing the image acquisition in different
directions. As electrical current flows through the coil, the coil generates a magnetic
field, which creates a magnetic forces that the coil windings and mountings. These
motions create the banging sound during MRI scans.
2.3.3 Decay of MR Signal
After the initial radio frequency excitation, an MR signal – generated by the magnetized
sample - is created and decays over time. The decay process consists of two phenomena:
longitudinal relaxation and transverse relaxation. Longitudinal relaxation (a.k.a. spin-
lattice relaxation or T1 recovery) describes the process of the hydrogen spin from anti-
parallel to parallel state. It is the period, which takes the net magnetization to go from the
22
transverse plane to the longitudinal axis. On the other hand, transverse relaxation consists
of T2 and T2*decay. T2 describes how fast the transverse magnetization decays or
dephases as a result of phase difference due to spin-spin interactions or interactions from
neighbour protons. Although similar to T2, T2* additionally considers the external
magnetic field inhomogeneities and is always faster than T2. T1-weighted images are
depicted indirectly using inversed T1 values; in the MRI scans, short T1 appear bright
while long T1 appears dark. Analogous logic applies to T2* weighted images, which are
used in BOLD-fMRI experiments (Huettel, et al., 2004).
While repetition time is the time interval between excitation pulses, echo time is the
period from the start of excitation pulse to the maximum of the signal (Hornak, 2011).
According to Equation 2-1, TE and TR can be used to control the contrast between
different tissue types, thereby manipulating tissue contrast in MRI scans. Further, the
inversion time (TI) describes the period between the excitation pulse and the rephasing
pulse.
2.3.4 BOLD signal
The BOLD signal is attributed to the magnetic properties of hemoglobin (Hb) and the
relationship between blood flow and neuronal activity. Oxygenated Hb has negligible
effect on the magnetic field of an MRI scanner. On the contrary, deoxygenated Hb
disrupts the magnetic field in proportion to the amount of oxygen dissociated from Hb.
When there is an increase in neuronal activity, the associated increase in oxygenated
blood supply surpasses the oxygen demand in the active region. Thus, there is an increase
in the ratio of oxygenated Hb to deoxygenated Hb, which increases BOLD signal.
Imaging signals are extracted from parcelations of brain volume or voxels; each in fMRI
is about 3.125mm x 3.125mm x 4mm large. Each voxel reflects the signal and activity of
approximately 105 neurons (de Courten-Myers, 1999).
23
2.3.5 Aliasing
According to Nyquist sample theorem (Equation 2-2), the sampling frequency (fs) should
be at least twice the highest frequency contained in a signal (fc) to resolve the signal.
Otherwise, there will be aliasing, which means ―different or another‖ in Latin. Aliasing
occurs when a signal is too undersampled to detect its changes (Olshausen, 2000).
Temporal aliasing occurs when a signal is sampled too slowly to resolve its changes. For
instance, if a 1 Hz signal wave (Figure 2-1) is only observed or sampled at 1 Hz from the
start, only the peaks of the wave will be detected, leading to the incorrect conclusion that
the signal is near the peak all the time. On the other hand, if the signal is observed at a
frequency of 2 Hz, it becomes plausible to see both the peak and the trough of the wave,
leading to the correct conclusion that the wave is varying periodically from peak to
trough. Indeed, to correctly observe changes of a signal, it must be sampled at minimum
twice the signal frequency. In fMRI, studies are mostly focused on neuronal activities
instead of cardiac or respiratory activity, so signals arisen from the latter activities should
be account as a regressor of non-interest. However, since a human’s heart rate is about 1
Hz, the sample rate of an MR scanner (TR: 0.5 Hz) is too slow to resolve the heart beat,
which becomes a confounding variable that remains unaccounted for and that is aliased
into the fMRI data. Respiration activity is aliased info fMRI data for similar reasons.
2.3.6 Smoothing
In order to bring MRI images from individual subjects into a common space for analysis,
these images are smoothed or blurred. This blends the signals of every voxel, especially
near its edge, with the counterpart of its neighbours. Smoothing increases the overlap and
smoothes the spatial transitions between voxels. Smoothing is done by convolving an
imaging with a Gaussian function before examined (right side of Figure 2-2). Spatial
smoothing involves convolution, in which the original signal in voxels are varied slightly
by the work of a Gaussian kernel.
Equation 2-4 shows a Gaussian kernel in the x-axis (2D), which is proportional to the
probability density function of a normal distribution. The greater the variance of the
24
function, the greater breadth or radius of the spatial smoothing. In 2D spatial smoothing,
Equation 2-3 is used as the Gaussian kernel; in 3D spatial smoothing, Equation 2-4 is
used. Equation 2-5 presents the relationship between the variance of the Gaussian kernel
and the full width at half max (FWHM).
25
Equation 2-1. Contrast of MRI as a function of TE and TR. CAB: contract between tissue
A and B; M0A: magnetization of tissue A; T1A & T2A: T1 and T2 values for tissue A.
(Huettel, et al., 2004)
Equation 2-2. Nyquist sample theorem. fs: sampling frequency; fc: highest frequency
contained in the sample.
Equation 2-3. 2D Gaussian kernel. xi: mean; x: some distance away from xi; σx2: variance
or the spread of the function.
Equation 2-4. 3D Gaussian kernel. xi: mean; x: some distance away from xi; σx2: variance
or the spread of the function. While x represents the Gaussian kernel in the y-z plane, y
and z variables represent analogous variables for kernels in x-z and x-y plane,
respectively.
Equation 2-5. Full width at half max (FWHM) as a function of Gaussian kernel variance
26
Figure 2-1. A sine wave with 1 second period (Olshausen, 2000)
Figure 2-2. Gaussian kernel distribution and full width at half max (FWHM)
27
2.4 Diffusion tensor imaging
Diffusion or Brownian motion is defined as random motion caused by temperature above
absolute zero (Callaghan, 2011). Random diffusion in all directions is known as isotropic
diffusion, but, when there is a barrier or boundary that restricts or channels diffusion,
there is restricted or anisotropic diffusion – a type of diffusion that is different in different
directions. The boundaries of interest could be created by tissue, membranes, or
microstructures. For instance, anisotropic diffusion occurs in white matter, while
isotropic diffusion occurs in grey matter and CSF (Beaulieu, 2011). The direction of
anisotropic diffusion is generally assumed to equate the direction of nerve fibres. In MRI,
the magnitude of water diffusion is represented by the apparent diffusion coefficient
(ADC), and the diffusion within a tissue follows the Gaussian distribution (Ackerman
and Neil, 2011).
In diffusion-weighted imaging, the MRI scanner captures images as it changes the
magnetic field gradient. For each voxel, the MRI signal is represented by Equation 2-6, in
which the b-factor is the combined magnetic field gradient parameter. Diffusion gradients
are applied right before and after a rephrasing radio frequency pulse. The first gradient
dephases the spins of immobile water, while the second gradient rephases them. The time
interval between the gradients is the diffusion time (Δ) (Figure 2-3). Equation 2-7
describes the relationship between b-value, gradient strength, and the diffusion time.
Different from stationary water, mobile and diffusing water is not rephrased by the
second gradient; instead, they dephase and attenuate the magnetic field, which is detected
and represented by the diffusion tensor (Equation 2-6). The diffusion tensor is a 3 by 3
matrix, with its diagonal elements being the diffusion displacement variances
(eigenvalues) or the ADCs and with its off-diagonal elements being proportional the
covariances of the displacements (Basser and Ozarslan, 2011). The variance of the
eigenvalues represents the fractional anisotropy (FA), while the mean of the eigenvalues
represents the mean diffusivity (MD).
For each gradient direction, the MRI signal is attenuated when water diffusion occurs
parallel to the gradient direction. Since the ventricles contain much water and provide an
28
isotropic environment, they are often the region of signal attenuation, making them dark
in the DTI images. DTI presumes that directional water diffusion indicates the existence
of white matter tracts or axon bundles, which restrain water movement. As such, DTI can
be used to map cortical SC in terms of white matter tracts. Each voxel in a DTI image
(about 2.4 mm wide) contains about 104 – 10
8 axons (0.1-10 μm in diameter (Filley,
2011)). Even so, white matter fibres are organized into large bundles of hundreds to
thousands of axons connecting different cortical areas, which make tractography adept in
measure macroscopic connections within the brain.
One way to utilize DTI and examine the SC of brain areas is tractography, which models
and reconstructs white matter tracts based on the diffusion parameters extracted from
DTI. There are two main types of tractography: probabilistic and streamline tractography.
In probabilistic tractography, a probability map is calculated per voxel to approximate the
likelihood of water diffusion in each direction in that voxel. Then a number (5000 by
default) of streamline samples are sent out in many directions from a seed ROI to the rest
of the brain, based on the directional probability distribution of each voxel. In streamline
tractography, only one streamline sample is projected from the seed ROI instead of 5000.
This sample will follow the direction of the primary eigenvector in the diffusion tensor,
producing in a single tract. Users can define the curvature angle threshold for the tract.
Because this thesis examined the existence of SC between certain regions in the brain,
tractography was used in spite of other analysis techniques, e.g., tract-based-spatial
statistics, which are more frequently used to characterize white matter tracts, which have
already been shown to exist.
29
Equation 2-6. Diffusion tensor model
sj: measured signal after applying gradient j; s0: measured signal without diffusion
gradient; D: diffusion tensor; x: vector with direction of gradient j; bj: gradient b-factor
sj = s0 exp( -bj xjT D xj )
Figure 2-3. Measuring diffusion with MRI
Δ: diffusion time; A: diffusion gradient strength; 90: 90° radiofrequency pulse; 180: 180°
radiofrequency pulse
Equation 2-7. b-factor
The b-factor is the combined magnetic field gradient parameter. Δ: diffusion time; A:
diffusion gradient strength
30
3 METHODS
3.1 Participants
A total of 80 healthy subjects were previously recruited for study by Nathalie Erpelding
(Erpelding, et al., 2012) who obtained informed written consent to experimental
procedures, which were approved by the University Health Network Research Ethics
Board. The subject pool consisted of 40 females and 40 males who were all right-handed
and fluent in English. They ranged in age from 19 to 36 years (mean
age ± SD = 24.5 ± 4.9 years). Subjects were screened for for the presence of neurological
and psychiatric conditions and other standard exclusion criteria for MR imaging
(potential pregnancy, claustrophobia, metal fragments, etc.). Specific exclusion criteria
based on self-report were: 1) current or regular pain (other than menstrual cramps) in the
last 6 months (e.g., headache, toothache, etc.), 2) pain lasting more than 3 months in the
last year, 3) any current or previous diagnosis of a psychiatric disorder (e.g., depression,
ADHD, etc.), 4) any chronic illness, 5) claustrophobia, 6) braces or metal in their body,
7) possibility of pregnancy, 8) medication/drug use at the dose, frequency and duration
potentially impacting pain or cognitive function.
3.2 Brain Imaging Acquisition
All imaging data were obtained on a 3T MRI scanner (GE Medical Systems, Milwaukee,
WI, USA) fitted with an 8-channel phased-array head coil). Subjects were instructed to
relax and to lie still for all scans. For each subject, the imaging session consisted of the
following scans:
1) Anatomical scan. High-resolution whole brain structural images were obtained with a
T1-weighted inversion recovery prepped, 3-dimensional fast spoiled gradient echo (IR-
FSGPR) sequence (flip angle = 15°, TE = 3 ms, TR = 7.8 ms, TI = 450 ms, 256x256
31
matrix, 1-mm slice thickness, 25.6 cm field of view – producing 1mm x 1mm x 1mm
voxels).
2) A non-task (resting state) fMRI scan. For this scan, subjects were instructed to lie still,
not think of anything in particular and to keep their eyes closed. This BOLD fMRI scan
was acquired from 40 4mm thick transverse whole-brain slices (interleaved EPI
sequence; T2*-weighted images; TR = 2000 ms; TE = 30 ms; 64x64 matrix; and 20 cm
field of view yielding 3.125mm x 3.125mm x 4mm voxels, 308 s).
3) Diffusion weighted images. Two diffusion weighted scans were obtained using the
following parameters: 96x96 matrix; 2.4mm x 2.4mm x 2.4mm voxels; 64 transverse
slices; 60 isotropic and non-collinear directions; TR = 17 s; TE = 83.3 ms; 23 cm field of
view; b = 1000 s/mm2. In addition, ten non-diffusion weighted images (b = 0 s/mm
2)
were obtained.
3.2.2 Pre-processing and correlation analysis
In the sgACC seed region, there was substantial signal dropout of the BOLD signal
(BOLD signal intensity below 65% of the mean intensity within non-zero intensity
voxels) in 10 males and 14 females and so these subjects were excluded from the rs-fMRI
analysis. Thus, the analysis of resting state fMRI data was done for 30 males and 26
females (18-37 years old, mean SD age 24.6 5.1).
Seed-to-voxel correlational analyses were carried out by the functional connectivity
(CONN) toolbox Ver 13i
(http://web.mit.edu.myaccess.library.utoronto.ca/swg/software.htm) and SPM 8. The pre-
processing pipeline of the functional images consisted of the following steps:
1) Co-registered to structural images
2) Realigned for motion in six axes: 3 translations and 3 rotational axes
3) Spatially normalized to the Montreal neurological Institute (MNI) template
32
4) Smoothed with a Gaussian kernel of 6 mm
5) Band-pass filtering from 0.01 Hz - 0.1 Hz
After these pre-processing steps, the CompCor strategy (Behzadi, et al., 2007) was
implemented, which extracted signal noise from WM and CSF by the principal
component analysis (PCA). The analyses did not include global signal regression to avoid
the potential introduction of false anticorrelations to the results. For discussion of this
issue, see (Murphy, et al., 2009). Further, noise from CSF and WM as well as the six
realignment parameters were removed as confounds from the functional data via
regression.
3.2.3 Subject level statistical analyses
A first level analysis was done using the CONN toolbox, which applied SPM functions to
perform spatial statistical analyses in each subject. A general linear model (GLM) was
applied to examine significant BOLD signal correlation with respect to time between
each seed and each voxel. The resulting correlation coefficients were Fisher transformed
to standard scores (Z-scores), which were then input into t-tests.
3.2.3.1 Definition of Seeds
A total of 6 bilateral spherical seeds in the sgACC were defined based on locations
previously reported to be involved in pain habituation (Bingel, et al., 2008; Bingel, et al.,
2007). Spheres have been commonly used as a seed shape in fMRI analyses (Chang and
Glover, 2010; Grova, et al., 2006; Kong, et al., 2010b; Margulies, et al., 2007; Zotev, et
al., 2011). The seeds were drawn as sphere of radii 3-mm centred at [5, 25, -10], [-5, 25, -
10], [5, 34, -9], [-5, 34, -9], [5, 34, -4], [-5, 34, -4], [6, 27, -10], [-6, 27, -10], [6, 30, -9], [-
6, 30, -9], [6, 33, -9], and [-6, 33, -9] (Figure 3-1).
33
3.2.4 Group level statistical analyses
A second level analysis used the CONN toolbox, including t-tests to examine sex
differences. To compensate for multiple comparisons, the second level results were
thresholded at a corrected p < 0.05 based on a Monte Carlo simulation implemented in
AlphaSim (http:// afni.nimh.nih.gov/afni/doc/manual/AlphaSim) by applying a
combination of uncorrected p values and cluster size thresholds (Table 3-1). For instance,
for small subcortical areas, the threshold combination consisted of a small cluster size
and a low uncorrected p value threshold, whereas, when thresholding large cortical
regions, a larger cluster size and higher uncorrected p value were used.
34
Figure 3-1. sgACC seeds for rs-fMRI
Each seed (red) is assigned a letter on the top right corner of the brain. A total of nine
different seed locations, each 3mm in radius, were used for the rs-fMRI analysis. L: left;
R: right. The X, Y, Z coordinates of the seeds are: Seed A: [-5, 25, -10]; B: [5, 25, -10];
C: [-5, 34, -9]; D: [5, 34, -9]; E: [-6, 33, -9]; F: [6, 33, -9]; G: [-5, 34, -4]; H: [5, 34, -4]; I:
[-6, 27, 10]; J: [6, 27, -10]; K [-6, 30, -9]; N: [6, 30, -9]
35
Table 3-1. Combined thresholding
Per-voxel p-value and cluster size were thresholded to achieve a corrected p<0.05 as
validated by a Monte Carlo simulation implemented in AlphaSim (http://
afni.nimh.nih.gov/afni/doc/manual/AlphaSim). The thresholding combination was chosen
to minimize the cluster size and to identify brain regions most precisely.
Cluster size threshold (MNI152 space)
Masked with gray matter No mask (whole brain)
Corrected p value <
Per voxel p value threshold voxel mm
3 voxel mm
3
0.05 0.001 32 256 54 432
0.05 0.0005 24 192 42 336
0.05 0.0001 13 104 26 208
0.05 0.00005 10 80 21 168
0.05 0.00001 5 40 14 112
36
3.3 Probabilistic tractography
3.3.2 Pre-processing
Due to signal dropout (DWI signal intensity below 32% of mean intensity within non-
zero intensity voxels) at the sgACC seed regions, data from one male and two females
were excluded from the DWI analysis. Therefore, the final structural analysis was done
for a total of 39 males and 38 females (18 to 37 years old, mean SD age 24.5 5.0).
Diffusion data were pre-processed by FMRIB’s Diffusion Toolbox (Behrens, et al., 2003;
Smith, et al., 2004) (www.fmrib.ox.ac.uk/fsl). The pre-processing steps were as follows:
1) Format conversion. MRI image files were converted in batch from DICOM to NIFTI
in Linux.
2) Eddy current correction. Changing the magnetic field in a conductor induces a current,
known as the Eddy current, which creates shears and stretches in the diffusion weighted
images. Such distortions varied depending on the different gradient directions. Thus,
Eddy current corrections were done on the NITFTI images.
3) Brain extraction. The brain extraction tool, BET, (Smith, 2002) was used to extract the
brain from the surrounding tissues for the diffusion and structural images.
4) Motion correction and linear registration tool. Following motion correction, FMRIB’s
linear image registration tool. FLIRT, was used to create transformation matrices among
the diffusion, structural, and standard spaces to allow cross-spatial image registration.
5) Identification of crossing WM fibres. FMRIB’s diffusion toolbox (FDT) was used to
identify crossing fibres. The FDT includes a utility – BEDPOSTX (Bayesian estimation
of diffusion parameters obtained using sampling techniques for modeling crossing
fibres). BEDPOSTX carries out Markov Chain Monte Carlo sampling to calculate
diffusion parameters for each voxel in a DTI image file. Then, a multi-fibre diffusion
model (Behrens, et al., 2007) was fitted to the diffusion data to approximate the
probability distributions of diffusion directions for each voxel.
37
3.3.3 Subject level statistical analyses
Probabilistic tractography was conducted by tracing streamline samples from each seed
voxel with the trace paths being constrained by the probability distribution. As such, a
total of 5000 streamline samples were sent out from every seed voxel. The number of
times, for which the streamline samples passed a brain voxel, correlates to the anatomical
connectivity between the voxel and the seed ROI. This allowed the mapping of an
anatomical connectivity distribution from the seed ROI to the remaining brain.
Termination and waypoint tractography was conducted to obtain tractograms and view
SC qualitatively; this type of tractography did not output numbers needed for statistical
analyses. Therefore, classification tractography was used complementarily to obtain the
numerical data for quantitative analyses, e.g., t-tests.
With classification probabilistic tractography, 5000 streamline samples were sent out
from each seed voxel. Data were collected on how many times the streamline samples
(Jones, et al., 2013) reached a specific target from a seed voxel (Figure 3-2). These
numbers were called the voxel-level seed-target anatomical connectivity, SCindiv-vox.
Further, after thresholding the connectivity values at 2, non-zero SCindiv-vox were averaged
for each seed region to yield SCindiv, which are called the individual level seed-target
connectivity.
3.3.3.1 Seeds & targets definition
The first analysis used three seeds - A, N, and H (Figure 3-1) - based on the resting state
fMRI results. The targets were defined in the bilateral TPJ, aMCC, and PAG (Table 3-2
& Figure 3-3).
In a second analysis, larger seed ROIs (Figure 3-4) were manually defined to include the
following regions: [-9, 30, -12] (Bingel, et al., 2008), [-6, 30, -9] (Bingel, et al., 2007), [3,
36, -12] (Bingel, et al., 2007), and brain areas, which were previously defined as the
sgACC (Johansen-Berg, et al., 2008). The following regions were chosen as targets:
38
bilateral aINS, bilateral Th, PAG, bilateral hypothalamus, bilateral amygdala, and NRM
(Table 3-2 & Figure 3-4). Minimum intensity thresholds were applied to shrink targets
that were too large.
3.3.4 Group level statistical analyses
SCindiv were pooled for all individual subjects within a group to make up the group-level
seed-target connectivity, or SCgroup-mean, with standard error, SCgroup-SE. t tests (2 tailed)
were then used to test for sex differences between the male and female groups (ACfemale-
mean and SCmale-mean).
39
Table 3-2. Probabilistic tractography seed and target definition
Targets Bilateral/left/right(B/L/R) Target definition source
Minimum
intensity
threshold
PAG B FSLView & Duvernoy's Atlas 2009 -
NRM B FSLView & Duvernoy's Atlas 2009 -
Amygdala
L
FSLView & Harvard-Oxford Subcortical
Structural Atlas 0.9
R
FSLView & Harvard-Oxford Subcortical
Structural Atlas 0.9
Hypothalamus
L FSLView & Duvernoy's Atlas 2009 -
R FSLView & Duvernoy's Atlas 2009 -
Thalamus
L (medial)
FSLView & Harvard-Oxford Subcortical
Structural Atlas & Talairach's Atlas &
Netter's Neurology 2E) 0.9
L (lateral)
FSLView & Harvard-Oxford Subcortical
Structural Atlas & Talairach's Atlas &
Netter's Neurology 2E) 0.9
R (medial)
FSLView & Harvard-Oxford Subcortical
Structural Atlas & Talairach's Atlas &
Netter's Neurology 2E) 0.9
R (lateral)
FSLView & Harvard-Oxford Subcortical
Structural Atlas & Talairach's Atlas &
Netter's Neurology 2E) 0.9
Anterior insula
L
FSLView & Freesurfer
lh.aparc.a2009s.annot -
R
FSLView & Freesurfer
rh.aparc.a2009s.annot -
TPJ
L Secondary seeds (Kucyi, et al., 2012) -
R Secondary seeds (Kucyi, et al., 2012) -
aMCC
L
Freesurfer lh.aparc.a2009s.annot &
FSLView -
R
Freesurfer rh.aparc.a2009s.annot &
FSLView -
40
Figure 3-2. Classification probabilistic tractography method
(A) An example of the probabilistic tractography method is shown for a right sgACC
seed (red) and a right hypothalamus target (blue). (B) In the seed, each voxel sends out
5000 sample projections in random directions. Each time a projection reaches a target
voxel the connectivity value in the seed voxel increases by 1. (C) With this logic, if a
seed voxel’s projections hit the target 3 times out of 5000, it will have a connectivity
value of 3. (D) Similar events occur to other seed voxels. This voxel has 2 (out of 5000)
samples hitting the target, giving it a connectivity value of 2. (E) Analogous processes
apply to the remaining seed voxels, which will each have a connectivity value.
41
Figure 3-3. Probabilistic tractography – analysis 1
sgACC seeds (3mm radii, red) and targets (blue), projected to the MNI152 standard brain
(2mm, T1) image provided in FSL. For seed letter assignment, see Figure 3-1. Slice
numbers are in voxel coordinates. L: left; R: right; sgACC: subgenual anterior cingulate
cortex; aMCC: anterior middle cingulate cortex; TPJ: temporoparietal junction; PAG:
periaqueductal grey.
42
Figure 3-4. Probabilistic tractography – analysis 2
sgACC seeds (red) and targets (blue & yellow) projected to the MNI152 standard brain
(2mm, T1) image provided in FSL. Yellow: lateral thalami. L: left; R: right; sgACC:
subgenual anterior cingulate cortex; NRM: nucleus raphe magnus; Th: thalamus; Amy:
amygdala; Hy: hypothalamus; PAG: periaqueductal gray
43
4 RESULTS
4.1 Resting-state fMRI
4.1.2 Overview of findings
The mean resting state FC of the 6 left and 6 right sgACC seeds was determined for 1) all
subjects, 2) females, 3) males, 4) the contrast of female>male, and 5) the contrast of
male>female. These analyses revealed that the sgACC show FC (as exhibited by
statistically significant correlations in resting state activity) to targets in multiple cortical
and subcortical regions. For some targets, correlations were found across the entire
combined subject pool, while for other targets, the correlations were found only in the
female or male group. Furthermore, there was stronger connectivity of the sgACC with
some targets in females (MD thalamus, raphe, PAG, aMCC) and in other targets in the
male (TPJ, anterior insula) group (see below, Figure 4-1and Table 4-1). It should also be
noted that findings varied somewhat for the different seed locations within the sgACC,
although many of the main findings were found for multiple sgACC seed locations.
Examples of the time-series resting state activity in representative individual subjects are
shown in Figure 4-2. These examples illustrate some of the key sex differences in the
synchronicity of the time course of sgACC brain activity with the activity in target
regions. Details of the findings are presented below for all statistically significant
findings (p<0.05 corrected as validated by a Monte Carlo simulation implemented in
AlphaSim [http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim]). Data from regions of
interest are shown in Table 4-2, and additional findings are listed in Table 4-3.
4.1.3 sgACC functional connectivity: group findings
A group analysis of all subjects (Figure 4-3), revealed that the sgACC had significant FC
with MCC, insula, PCC, and TPJ. There was also significant sgACC FC with several
other areas targets, e.g., regions of the fusiform gyrus, parahippocampal gyrus, middle
44
temporal gyrus (BA21), ventral tegmental area, middle frontal gyrus (BA8), precuneus,
superior medial frontal lobe, and inferior temporal pole. Table 4-2 and Table 4-3 provide
the details of the location each significant seed-target pair.
For some seed regions, there was sgACC connectivity with specific parts within a target
that were detected in only the male group or only the female group. Thus, an analysis of
sgACC FC in the female group alone (Figure 4-4) revealed significant connectivity with
regions within the aMCC, raphe nucleus, regions within the inferior temporal gyrus,
middle temporal pole, parahippocampal gyrus, cerebellum, medial frontal gyrus, and
amygdala.
In the male only group analysis, significant sgACC FC (Figure 4-5, Table 4-2, Table 4-3)
was found for the aMCC; anterior insula; posterior insula; PCC; regions in the inferior,
middle and superior temporal lobe; BA6, 8, 9, 45 and 47; parahippocampal gyrus;
precuneus; fusiform gyrus; and amygdala.
4.1.4 Sex differences in sgACC functional connectivity
Sex differences in the sgACC connectivity were investigated based on the contrasts of
Females> Males and Males>Females (p<0.05 corrected as validated by a Monte Carlo
simulation implemented in AlphaSim
[http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim]: per voxel p ≤ 0.001; cluster size
32 voxels). This analysis indicated greater connectivity in females than males (Figure
4-6) in the aMCC, raphe nucleus, MD thalamus, and PAG. Additionally, there was
greater sgACC connectivity in the females within regions of the cuneus, cerebellum,
fusiform gyrus, parahippocampal gyrus, and pontine nuclei. However, the males
exhibited greater sgACC-FC than females (Figure 4-7) in the anterior insula; TPJ; and
areas of BA45 and BA47 (Figure 4-7).
There are four scenarios in which a group contrast can result in sex differences in sgACC
connectivity. For example: 1) Both females and males could have connectivity but there
is greater connectivity in females; 2) Females may have connectivity but males do not; 3)
45
Females have no connectivity, but males have a negative connectivity; 4) Females and
males both have negative connectivity but females have less negative connectivity.
Therefore, plots of connectivity strength (Z scores) were constructed to examine how the
above sex difference findings arose. In the main findings (Figure 4-8, Figure 4-9), the sex
difference in aMCC arose from significant positive FC in females and non-significant
positive FC in males. The sex difference in the raphe nucleus arose from significant
positive FC in females and negative FC in males. The sex difference in MD and PAG
arose from non-significant positive FC in females and negative FC in males. The sex
difference in TPJ arose from non-significant positive FC in males and negative FC in
females. The sex difference in anterior insula arose from significant positive FC in males
and negative FC in females.
With respect to the seed locations within sgACC, the anterior sgACC demonstrated
greater FC with aMCC and raphe in women, and with aINS in men than the opposite sex.
On the other hand, the posterior sgACC showed stronger FC with PAG and MD thalamus
in women, and with TPJ in men than the opposite sex. With regard to laterality of the
results, the peak coordinates of aMCC – only reported in women - resided in the left side
of the brain hemisphere for both ipsilateral and contralateral sgACC seed locations.
Posterior insula demonstrated bilateral FC with a number of sgACC seeds in male,
female, and both sex groups while orbitofrontal regions (BA47, BA45, anterior insula)
showed left lateralized FC with a number of sgACC seeds and in male group.
46
Figure 4-1. Resting-state group FC to sgACC: summary of main findings in sex
differences.
In females, sgACC exhibited greater FC to targets (pink): MD, PAG, raphe nucleus, and
aMCC than males. In males, sgACC exhibited greater FC to targets (blue): TPJ and aINS
than females. Left sgACC seeds are shown in purple; right seeds are shown in green. FC:
functional connectivity; sgACC: subgenual anterior cingulate; MD: medial dorsal
thalamic nucleus; PAG: periaqueductal grey; TPJ: temporoparietal junction; aMCC:
anterior midcingulate; aINS: anterior insula. Brain outline image adapted from
(http://www2.le.ac.uk/departments/gradschool/training/events/caferesearch/cafe-brain).
47
Figure 4-2. Representative individual subject examples of the time series of resting
state activity within seed-target pairs of regions that show sex differences.
A: FC between sgACC (seed H) and aMCC is stronger in this female subject (r = 0.56)
than a male subject (r = 0.03). B: FC between sgACC (seed H) and raphe nucleus is
stronger in this female subject (r = 0.82) than the male subject (r = 0.18) shown. C: FC
between sgACC (seed E) and anterior insula is stronger in this male subject (r = 0.40)
than the female subject (r = 0.10). sgACC: subgenual anterior cingulate cortex; aINS:
anterior insula; aMCC: anterior midcingulate cortex; FC: functional connectivity.
Norm
aliz
ed f
MR
I sig
nal
-4
-2
0
2
4
6sgACC(M)
Raphe nucleus(M)
-3
-2
-1
0
1
2
3sgACC (F)
Raphe nucleus (F)
-4-3-2-10123
sgACC(M)
aMCC(M)
-4
-2
0
2
4
6
8
sgACC (F)
aMCC (F)
A
B
C
X Data
-3-2-101234
sgACC(F)
aINS(F)
Time (s)
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
-3-2-101234
sgACC
aINS(M)
48
Figure 4-3. Resting-state female & male group FC with sgACC seed A, C, E, H
(p<0.05 corrected as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim) Statistical maps are projected to a
T1 brain image provided in SPM and xjview. MCC: midcingulate cortex; pINS: posterior
insula; TPJ: temporoparietal junction; FC: functional connectivity; sgACC: subgenual
anterior cingulate; Sup: superior; R: right; L: left.
49
Figure 4-4. Resting-state female group FC with sgACC seed H
(p<0.05 corrected as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim) Statistical maps are projected to a
T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate;
aMCC: anterior midcingulate cortex; FC: functional connectivity; R: right; L: left.
50
Figure 4-5. Resting-state male group FC with sgACC seed B, C, D, E, F, J
(p<0.05 corrected as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim) Statistical maps are projected to a
T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate; Hy:
hypothalamus; aINS: anterior insula; pINS: posterior insula; Hi: hippocampus; Sup:
superior; FC: functional connectivity; R: right; L: left.
51
Figure 4-6. Stronger resting-state functional connectivity with sgACC seeds A, N,
and H in female group than male group
(p<0.05 corrected as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim) Statistical maps are projected to a
T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate; PAG:
periaqueductal grey; MD: medial dorsal nucleus; aMCC: anterior cingulate cortex; R:
right; L: left.
52
Figure 4-7. Stronger resting-state FC with sgACC seeds A, B, C, D, E, F, and J in
male group than female group
(p<0.05 corrected as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim) Statistical maps are projected to a
T1 brain image provided in SPM and xjview. sgACC: subgenual anterior cingulate;
aINS: anterior insula; TPJ: temporoparietal junction; FC: functional connectivity; R:
right; L: left.
53
Figure 4-8. Main regions of stronger sgACC FC in females compared to males
A: sgACC (seed H) and aMCC; B: sgACC (seed H) and raphe nucleus; and C: sgACC
(seed A) and MD; D: sgACC (seed N) and PAG (*P < 0.05 corrected). aMCC: anterior
midcingulate cortex; MD: medial dorsal nucleus; PAG: periaqueductal grey; sgACC:
subgenual cingulate cortex; FC: functional connectivity.
54
Figure 4-9. Main regions of stronger sgACC FC in males compared to females
A: sgACC (seed A) and TPJ; B: sgACC (seed E) and aINS (*P < 0.05 corrected). aINS:
anterior insula; TPJ: temporoparietal junction; sgACC: subgenual cingulate cortex; FC:
functional connectivity.
55
Table 4-1. Resting-state group FC to sgACC: summary of main sex differences findings
Target F M F>M M>F
PAG x x √ X
Raphe nucleus √ x √ X
MD x x √ X
aMCC √ √ √ X
TPJ x x x √
aINS x √ x √
√: significant functional connectivity - corrected p<0.05 as validated by a Monte Carlo
simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim)
x: non-significant functional connectivity
F: female; M: male; aMCC: anterior midcingulate cortex; aINS: anterior insula; pINS:
posterior insula; PAG: periaqueductal grey; TPJ: temporoparietal junction; MCC:
midcingulate cortex; MD: medial dorsal nucleus; Amy: amygdala; sgACC: subgenual
cingulate cortex; FC: functional connectivity.
56
Table 4-2. Resting-state group FC to sgACC: main findings of interest
MNI coordinate (mm)
See
d
R/
L
Contras
t
Region x y z Voxel-level
(Z)
A L
F > M MD -4 -16 14 4.35 *
*
F & M pINS 42 -12 0 4.99 ffi
pINS -40 -16 0 4.80 ffi
M > F aINS; BA47; BA45 -42 32 2 3.96 *
TPJ (BA39) -50 -48 10 4.98 †
C L
F pINS 44 -12 2 5.66 ffi
M pINS 48 -6 0 5.44 ffi
aINS -36 28 2 5.53 ffi
Amy 34 0 -22 5.20 ffi
F & M MCC 4 6 30 5.13 ffi
MCC 4 -16 38 4.78 ffi
D R
F aMCC 12 26 26 6.17 ffi
M BA47 -42 32 -4 4.50 *
aMCC -10 42 30 5.74 ffi
M > F BA47 -42 32 -4 4.41 *
E L
M aINS; BA47; BA45 -40 22 -4 5.02 *
F & M pINS 50 -8 0 5.59 ffi
pINS -40 -20 12 4.95 ffi
MCC 4 8 30 4.89 ffi
M > F aINS; BA47; BA45 -40 22 -4 4.27 *
F R
F Amy 22 -2 -18 5.67 ffi
aMCC -4 28 30 5.07 †
† M BA47 -44 32 -2 3.61 *
Amy 38 0 -26 5.53 ffi
M > F BA47 -44 -32 -2 3.88 *
G L
F pINS -42 -8 -4 7.88 ffi
M pINS 42 0 -6 5.88 ffi
pINS -46 -10 0 6.46 ffi
F & M MCC -2 -20 38 5.42 ffi
57
H R
F aMCC -2 18 24 6.88 †
†
Raphe nucleus -4 -18 -36 4.03 *
MCC 0 4 36 6.26 ffi
M pINS -40 -10 0 5.83 ffi
pINS 48 -8 2 5.86 ffi
F & M TPJ 52 -54 20 4.86 ffi
F > M aMCC -2 18 24 4.97 †
†
Raphe nucleus -4 -18 -36 4.04 *
I L
M pINS -46 -10 0 6.46 ffi
pINS 42 0 -6 5.88 ffi
J R
M BA47 -38 34 -2 3.77 *
M > F BA47 -38 34 -2 4.26 *
K L
F pINS -42 -8 -4 7.88 ffi
N R
F aMCC -12 34 22 5.88 ffi
F > M PAG -4 -28 -22 3.49 *
F: female; M: male; aMCC: anterior midcingulate cortex; aINS: anterior insula; pINS:
posterior insula; PAG: periaqueductal grey; TPJ: temporoparietal junction; MCC:
midcingulate cortex; MD: medial dorsal nucleus; Amy: amygdala; sgACC: subgenual
cingulate cortex; FC: functional connectivity.
Negative X coordinates signify left brain regions; Positive X coordinates signify right
brain regions
Corrected p<0.05 as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim)
*per voxel p < 0.001; cluster size threshold: 256mm3
**per voxel p < 0.0005; cluster size threshold: 192mm3
† per voxel p < 0.0001; cluster size threshold: 104mm3
††per voxel p < 0.00005; cluster size threshold: 80mm3
‡ per voxel p < 0.00001; cluster size threshold: 40mm3
58
Table 4-3. Resting-state group FC to sgACC seeds: additional findings
MNI coordinate (mm)
Seed R/L Contrast Region x y z Voxel-level (Z)
A L
M Inferior temporal lobe (BA20) 34 10 -48 6.54 ffi
F & M Parahippocampal gyrus -16 -14 -28 5.67 ffi
Middle temporal gyrus (BA21) -66 -16 -8 5.04 ffi
B R
F Inferior temporal lobe -50 2 -38 5.84 ffi
Cerebellum -10 -58 -36 6.04 ffi
Inferior temporal lobe (BA20) 64 -20 -20 6.09 ffi
M Inferior temporal lobe -42 12 -40 5.92 ffi
F & M Fusiform gyrus 24 -36 -16 5.44 ffi
Middle temporal gyrus 62 -12 -14 4.89 ffi
C L
F Inferior temporal gyrus 48 0 -40 8.97 ffi
Parahippocampal gyrus 24 -22 -24 6.10 ffi
Inferior temporal lobe -58 -16 -18 7.70 ffi
M Parahippocampal gyrus 26 -10 -34 6.59 ffi
Parahippocampal gyrus -22 -24 -22 6.82 ffi
Amy 34 0 -22 5.20 ffi
Precuneous -16 -50 -2 5.79 ffi
F & M Precuneous 8 -52 14 6.81 ffi
Middle frontal lobe -40 30 48 5.60 ffi
F > M Cuneus (BA7) -8 -78 32 3.61 *
D R
F Middle temporal pole -42 16 -40 6.96 ffi
Parahippocampal gyrus -18 -6 -20 5.87 ffi
Parahippocampal gyrus 22 -2 -20 5.59 ffi
M Inferior temporal gyrus 56 -6 -34 6.01 ffi
Middle temporal pole 48 10 -30 5.36 ffi
Superior temporal gyrus 46 10 -20 5.45 ffi
BA47 -40 30 -12 7.30 ffi
BA47 50 30 -10 6.02 ffi
Middle temporal gyrus (BA21) 62 -20 -8 5.46 ffi
Middle temporal gyrus (BA21) -64 -10 -12 5.82 ffi
Superior frontal gyrus -20 50 28 5.69 ffi
Superior frontal gyrus 14 52 42 5.78 ffi
59
F & M Parahippocampal gyrus 26 -18 -24 5.50 ffi
Fusiform gyrus -24 -42 -14 5.25 ffi
Middle temporal gyrus (BA21) -64 -10 -12 6.16 ffi
Middle frontal gyrus (BA8) 28 38 46 4.81 ffi
E L
F Inferior temporal pole 48 0 -40 8.14 ffi
Inferior temporal pole -60 -10 -26 7.57 ffi
Parahippocampal gyrus 32 -28 -20 5.67 ffi
M Inferior temporal pole -34 10 -46 8.68 ffi
Parahippocampal gyrus -22 -24 -22 5.73 ffi
Parahippocampal gyrus 20 -6 -18 6.34 ffi
F & M Parahippocampal gyrus -22 -24 -24 5.99 ffi
Precuneus 8 -54 14 5.83 ffi
Precuneus -6 -56 16 4.93 ffi
F > M Occipital lobe -8 -78 30 4.27 *
F R
F Inferior temporal pole -42 18 -38 5.86 ffi
Inferior temporal pole 44 8 -40 5.70 ffi
Parahippocampus 18 -8 -32 5.96 ffi
Middle temporal pole 60 -6 -22 5.11 ffi
Amy 22 -2 -18 5.67 ffi
M Inferior temporal pole 56 -8 -36 6.28 ffi
Amy 38 0 -26 5.53 ffi
Superior temporal gyrus 46 6 -22 5.82 ffi
Middle temporal lobe 64 -18 -8 6.24 ffi
Medial frontal lobe 12 52 30 5.53 ffi
Medial frontal lobe 2 56 44 5.52 ffi
F & M Middle temporal lobe -64 -10 -12 5.48 ffi
Medial superior frontal lobe 2 34 64 5.44 ffi
F > M Medial cerebellum -4 -62 -24 4.09 ffi
G L
F Inferior temporal lobe 42 -2 -50 7.47 ffi
Parahippocampal gyrus 20 -10 -34 6.77 ffi
Parahippocampal gyrus -26 -28 -22 6.55 ffi
Middle temporal gyrus 66 -14 -14 5.73 ffi
M Superior temporal gyrus -34 14 -44 7.40 ffi
Inferior temporal gyrus 36 -6 -44 5.62 ffi
Middle temporal gyrus 60 -4 -22 5.55 ffi
60
Superior temporal gyrus -46 -2 -14 5.41 ffi
F & M Parahippocampal gyrus 16 -32 -10 4.87 ffi
F > M Cerebellum 16 -70 -42 4.06 *
Parahippocampal gyrus 42 -30 -16 3.93 *
Parahippocampal gyrus -40 -16 -22 4.36 *
H R
F Middle temporal pole 38 18 -42 5.75 ffi
Anterior cerebellum -14 -54 -26 6.25 ffi
Medial cerebellum -8 -64 -20 6.55 ffi
BA42 -58 -14 10 6.87 ffi
Middle temporal pole 52 6 -24 6.02 ffi
M Middle temporal pole 38 14 -38 7.73 ffi
Inferior temporal gyrus 56 -6 -42 5.97 ffi
Fusiform gyrus -22 -42 -16 5.55 ffi
Middle temporal gyrus
(BA21)
-64 -24 -6 5.92 ffi
Superior temporal gyrus -48 -18 6 5.29 ffi
Frontal superior medial
Gyrus
16 36 60 6.94 ffi
F & M Ventral tegmental area 0 -10 -12 5.35 ffi
PCC -6 -56 16 5.25 ffi
Middle temporal gyrus
(BA21)
-56 -70 20 6.15 ffi
Superior medial frontal lobe 30 38 48 4.72 ffi
F > M Medial cerebellum -10 -54 -20 5.00 ffi
I L
F Inferior temporal gyrus 50 0 -38 5.77 ffi
Middle temporal gyrus 60 -4 -22 5.55 ffi
F & M PCC -2 -20 38 5.42 ffi
F > M Cerebellum 16 -70 -42 4.06 *
Fusiform gyrus -40 -16 -22 4.36 *
Fusiform gyrus 42 -30 -16 3.93 *
J R
F Inferior temporal pole -50 2 -38 5.97 ffi
Middle temporal lobe 60 -16 -18 6.76 ffi
M Inferior temporal pole 32 12 -46 6.52 ffi
Inferior temporal pole -40 12 -38 5.48 ffi
F & M Inferior temporal pole 58 -8 -34 4.98 ffi
K L
61
F Parahippocampal gyrus -26 -28 -22 6.55 ffi
Parahippocampal gyrus 26 -42 -8 6.18 ffi
Inferior temporal gyrus 16 -64 -12 6.33 ffi
M PCC 2 -14 36 4.31 ffi
Superior frontal lobe -16 24 56 3.70 ffi
F & M PCC -2 -20 38 5.42 ffi
F > M Fusiform gyrus -40 -16 -22 4.36 *
Pontine nucleus -6 -18 -34 3.66 *
Fusiform gyrus 42 -30 -16 3.93 *
M > F Middle frontal lobe -38 22 40 3.48 *
Superior frontal lobe 20 62 12 3.54 *
N R
F Inferior temporal lobe (BA20) -50 -2 -32 6.80 ffi
Parahippocampal gyrus 20 -10 -32 5.46 ffi
Middle temporal lobe 64 -14 -18 6.44 ffi
Medial frontal gyrus 12 54 12 6.35 ffi
Medial frontal gyrus -18 50 26 5.61 ffi
M Inferior temporal pole 34 16 -44 7.27 ffi
Fusiform gyrus 34 -4 -30 5.48 ffi
Middle temporal lobe -40 -2 -20 5.83 ffi
Medial frontal lobe 10 60 16 6.04 ffi
F & M Medial frontal lobe 4 36 62 5.30 ffi
F: female; M: male; PCC: posterior cingulate cortex; aMCC: anterior midcingulate
cortex; aINS: anterior insula; pINS: posterior insula; PAG: periaqueductal grey; TPJ:
temporoparietal junction; MCC: midcingulate cortex; MD: medial dorsal nucleus; Amy:
amygdala; sgACC: subgenual cingulate cortex; FC: functional connectivity.
Negative X coordinates signify left brain regions; Positive X coordinates signify right
brain regions
Corrected p<0.05 as validated by a Monte Carlo simulation implemented in AlphaSim
(http://afni.nimh.nih.gov/afni/doc/manual/AlphaSim)
*per voxel p < 0.001; cluster size threshold: 256mm3
**per voxel p < 0.0005; cluster size threshold: 192mm3
† per voxel p < 0.0001; cluster size threshold: 104mm3
††per voxel p < 0.00005; cluster size threshold: 80mm3
‡ per voxel p < 0.00001; cluster size threshold: 40mm3
62
4.2 Probabilistic tractography
4.2.2 Overview of findings
For the purposes of this thesis, ―anatomical/structural connectivity‖ was quantified and
defined as the number of streamline samples (out of 5000) on average that reached from a
seed to a target. Two analyses were conducted, and the SC of sgACC seeds was
determined for 1) all subjects, 2) females, 3) males, 4) the contrast of female>male, and
5) the contrast of male>female. Based on the hypotheses and sex differences found for
the RS analysis, the first analysis examined sex differences in anatomical connectivity
between sgACC and targets including PAG, TPJ, and aMCC (Figure 3-3). In the second
analysis, sex differences were examined for larger sgACC seeds and targets including
anterior insula, Th, amygdala, raphe nucleus, hypothalamus, and PAG (Figure 3-4). In
addition, both analyses calculated the common connectivity, defined as the percentage of
subjects that exhibited significant SC between the sgACC and a specific target. While
low common connectivity indicates the lack of SC in some targets, high common
connectivity suggests strong SC in other targets (Table 4-4, Table 4-5). Sex difference (p
< 0.05 Bonferroni corrected) was detected among targets of strong SC with the sgACC
(see below for details).
4.2.3 sgACC anatomical connectivity in males and females
Figure 4-10 schematically shows the overall findings of sgACC anatomical connectivity.
In the two tractography analyses, the group of all subjects showed SC between sgACC
and targets including aMCC, PAG, anterior insula, thalamus, hypothalamus, and
amygdala without indication of laterality (Figure 4-14). In the first analysis, the sgACC
were anatomically connected to aMCC and PAG. From the second analysis, the sgACC
was anatomically connected to the following regions: anterior insula, PAG,
hypothalamus, amygdala, lateral and medial thalamus. Furthermore, the sgACC was also
weakly, anatomically connected to the raphe. The appendix contains slice-by-slice
tractograms of sgACC in female, male, and both sex groups.
63
4.2.4 Sex differences in sgACC anatomical connectivity
Using the number of streamlines that successfully reached from a seed ROI to a target
ROI as output by classification tractography for each subject, the data was compiled into
male group and female group. T-tests were then conducted to compare for sex differences
between the groups in each hypothesized seed-target tract. Compared to females, males
had stronger SC between left sgACC and left hypothalamus (p < 0.05 Bonferroni
corrected) (Figure 4-11). Sex differences were examined for sgACC-SC to a total of 29
targets. In other words, there were a total of 29 t-tests conducted between male and
female groups. In order to correct for multiple comparison via Bonferroni correction, the
allowable probability of type I error (p = 0.05) was divided by 29, resulting in p ≤ 0.0017.
Thus, for one of the 29 comparisons to be significant, its p value needed to be smaller
than or equal to 0.0017. Moreover, SC between the right sgACC and the right
hypothalamus, between the left hypothalamus and right amygdala, as well as between the
left sgACC and left amygdala was stronger in men than women at p< 0.05 uncorrected
but these findings did not reach statistical significance at a corrected p of 0.05.
64
Figure 4-10. Summary – sgACC structural connectivity in the group of all subjects
sgACC exhibited substantial SC (black and blue lines) to aMCC, Th, aINS, Hy, Amy,
and PAG. Males showed greater sgACC-SC than females to Hy (p < 0.05 Bonferroni
corrected) (blue lines). aMCC: anterior midcingulate; Amy: amygdala; PAG:
periaqueductal grey; aINS: anterior insula; Th: thalamus; Hy: hypothalamus; sgACC:
subgenual anterior cingulate; SC: structural connectivity. Brain outline image adapted
from (http://www2.le.ac.uk/departments/gradschool/training/events/caferesearch/cafe-
brain).
65
Figure 4-11. Stronger anatomical connectivity between left sgACC and left Hy in
male group than female group
A: Left sgACC-Hy anatomical connection in males (blue) and females (orange)
thresholded at 95% group common connectivity (35/39 subjects in male group; 34/38
subjects in female group). Tractograms are projected to the MNI152 standard brain
(2mm, T1) image provided in FSL. B: Paired t-test for anatomical connectivity (*P <
0.05 Bonferroni corrected). Hy: hypothalamus; sgACC: subgenual anterior cingulate
cortex; R: right; L: left.
66
Figure 4-12. Female group anatomical connectivity between sgACC seeds and
targets The absolute lower threshold for common connectivity was 50% or 19/38 subjects. In
some cases, this threshold was increased for display purposes. Tractograms are projected
to the MNI152 standard brain (2mm, T1) image provided in FSL. aMCC: anterior
midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior insula; Th:
thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; R: right; L: left.
67
Figure 4-13. Male group anatomical connectivity between sgACC seeds and targets
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. In
some cases, this threshold was increased for display purposes. Tractograms are projected
to the MNI152 standard brain (2mm, T1) image provided in FSL. aMCC: anterior
midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior insula; Th:
thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; R: right; L: left.
68
Figure 4-14. Female and male group anatomical connectivity between sgACC seeds
and targets
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. In
some cases, this threshold was increased for display purposes . Tractograms are projected
to the MNI152 standard brain (2mm, T1) image provided in FSL. aMCC: anterior
midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior insula; Th:
thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; R: right; L: left.
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Table 4-4. Group SC to sgACC: summary of main findings
Target SC M>F
TPJ x x
aMCC √ x
PAG √ x
aINS √ x
Th √ x
Hy √ √
Raphe nucleus x x
Amy √ x
√: significant structural connectivity – common connectivity >50% of total subjects
x: non-significant structural connectivity
aMCC: anterior midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior
insula; Th: thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; SC:
structural connectivity; M: male; F: female
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Table 4-5. sgACC anatomical connectivity in females and males Seed-target anatomical connectivity
F M F & M
Seed Target Mean SE Mean SE Mean SE % CC
Analysis 1
A R TPJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00
A left TPJ 0.00 0.00 0.00 0.00 0.00 0.00 0.00
H L aMCC 90.39 14.53 88.08 9.22 89.22 8.50 100.00
H R aMCC 413.61 69.47 379.63 46.22 396.40 41.28 100.00
N PAG 3.55 0.47 3.28 0.44 3.41 0.32 79.22
Analysis 2
R L aINS 35.10 5.96 21.61 6.23 28.27 4.36 90.91
R L lateral Th 8.12 1.36 6.96 0.86 7.53 0.80 94.81
R PAG 4.42 0.49 4.53 0.51 4.47 0.35 92.21
R R Hy 49.83 19.24 142.82 40.95 96.93 23.28 100.00 *
R L Amy 13.76 2.42 16.90 3.40 15.35 2.09 93.51
R L medial Th 7.61 1.25 7.15 1.04 7.38 0.81 93.51
R R aINS 37.74 5.89 28.29 6.46 32.95 4.38 96.10
R R lateral Th 10.41 1.38 10.31 1.45 10.36 0.99 98.70
R L Hy 19.92 4.90 48.57 10.37 34.43 5.98 98.70 *
R Raphe nucleus 0.50 0.16 0.58 0.19 0.54 0.13 20.78
R R Amy 27.24 5.11 59.63 11.17 43.65 6.43 96.10 *
R R medial Th 12.25 2.66 12.17 2.05 12.21 1.66 98.70
L L aINS 65.58 10.19 58.34 13.86 61.91 8.59 100.00
L L lateral Th 12.45 2.43 15.78 2.70 14.14 1.82 98.70
L PAG 5.31 0.86 5.36 0.59 5.34 0.52 94.81
L R Hy 17.08 5.27 49.14 19.58 33.32 10.35 96.10
L L Amy 43.28 11.62 102.74 22.17 73.39 12.98 96.10 *
L L medial Th 12.54 3.29 15.90 2.45 14.24 2.04 98.70
L R aINS 36.02 10.81 17.55 6.42 26.67 6.30 85.71
L R lateral Th 9.49 1.32 7.36 1.15 8.41 0.88 93.51
L L Hy 49.51 17.75 250.82 53.98 151.47 30.77 100.00 **
L Raphe nucleus 0.71 0.22 0.62 0.25 0.66 0.16 22.08
L R Amy 11.41 2.30 10.03 2.09 10.71 1.54 83.12
L R medial Th 9.09 1.44 6.07 0.94 7.56 0.87 90.91
aMCC: anterior midcingulate; Amy: amygdala; PAG: periaqueductal grey; aINS: anterior
insula; Th: thalamus; Hy: hypothalamus; sgACC: subgenual anterior cingulate; TPJ:
temporoparietal junction; M: male; F: female; SE: standard error; CC: common
connectivity; R: right; L: left.
Anatomical connectivity: streamline samples (out of 5000) reached from a seed to target
Paired t-tests (Male > Female):
*p<0.05 uncorrected
** p<0.05 Bonferroni corrected
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5 DISCUSSION
5.1 SUMMARY OF MAIN FINDINGS
This thesis used two different brain imaging techniques to determine how the sgACC is
connected functionally and structurally to other brain regions previously implicated in
pain modulation and assessed whether these connectivities show sex differences. The
main findings are shown schematically in (Figure 4-1, Figure 4-10). The probabilistic
tractography findings were that the sgACC is structurally connected to the PAG,
amygdala, hypothalamus, MD thalamus, aMCC, and the anterior insula, amongst other
areas. Furthermore, the sgACC SC with the hypothalamus was found to be greater in men
than in women. The main findings from resting state fMRI revealed that the sgACC was
functionally connected to the PAG, raphe, MD thalamus, aMCC as well as to the TPJ and
anterior insula, among other areas. This analysis further demonstrated that women had
stronger sgACC FC than men with nodes of the descending antinociceptive and affective
system, namely the PAG, raphe, MD thalamus, and aMCC. In contrast, men showed
greater sgACC connectivity than women with the regions of the salience network,
namely the TPJ and anterior insula. These findings conform with the a priori hypothesis.
Given that the previous findings of greater pain habituation in women than men (Hashmi
and Davis, 2009) and association between long term habituation and sgACC activation
(Bingel, et al., 2007; Bingel and Tracey, 2008), the current findings of greater FC in
women with the descending pain modulatory pathway may reflect more effective pain
habituation, whereas stronger FC in men with regions in the salience and attention
network may reflect sustained attention to pain that hinders habituation. Given the role of
the hypothalamus in endorphin-mediated antinociception, the stronger sgACC-
hypothalamus SC in men could provide them a greater reliance on the hypothalamus-
mediated descending modulation pathway than in women. In contrast, women may more
strongly rely on the classic PAG-mediated descending pathway for modulation (Figure
5-3).
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This thesis used both FC and SC approaches to evaluate sex differences that provide a
framework to understand how pain is experienced and modulated differently in men and
women. The findings from this thesis confirm and expand upon previous studies that
have examined the general connectivity of the sgACC that did not consider sex
differences (Beckmann, et al., 2009; Torta and Cauda, 2011; Yu, et al., 2011). For
instance, a previous tractography study in a mixed sample of men and women reported
SC between sgACC and regions including hypothalamus, orbitofrontal cortex, and
amygdala (Beckmann, et al., 2009), which were reproduced in the results of this thesis.
Other studies that had mixed samples of men and women, found FC between sgACC and
the orbitofrontal cortex (Torta and Cauda, 2011), (Margulies, et al., 2007), and Yu’s
group (Yu, et al., 2011), additionally found sgACC FC with temporal pole and medial
prefrontal cortex. A metaanalysis also revealed sgACC FC with the ventromedial PFC
and posterior insula (Torta and Cauda, 2011), which concurs with the findings of this
thesis. However, there has been a lack of experiments examining sex differences within
FC and SC, which was the motivation and novelty for this thesis project.
The sgACC is implicated in a variety of functions (see Literature Review) beyond the
scope of this thesis and so this discussion will focus on the findings in the context of pain
and antinociceptive systems. The axonal projections in the descending modulation
pathway are summarized in Figure 5-2. The anatomical connectivity from sgACC and its
associated cortical regions subregions are illustrated in Figure 5-1.
5.2 DELINEATION OF PAIN PATHWAYS WITH MRI-BASED
CONNECTIVITY TECHNIQUES: ADVANTAGES AND
LIMITATIONS
In this thesis, two approaches – rs-fMRI and probabilistic tractography - were used to
examine the SC and FC of sgACC, respectively. The SC analysis delineates the
anatomical framework of a system but FC analysis provides a different type of
framework that delineates brain regions that may be working together but not require
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monosynaptic or polysynaptic connections. In this thesis, seeding in the sgACC region,
the rs-fMRI analysis examined the temporal correlation of neuronal oscillations in
sgACC with the rest of the brain. As such, the technique identified brain nodes whose
neuronal activities oscillated almost in synchrony with neuronal activities in sgACC. The
neuronal activities between these nodes and sgACC are highly correlated and are said to
be functionally connected, which suggests that neurons within this network work and
oscillate together.
In this thesis, the general concept of probabilistic tractography is the calculation of the
probability of water diffusion in each direction per voxel, forming a number of diffusion
probability distributions. These probability distributions were then used to approximate
the likelihood that water in sgACC (seed) could diffuse to another brain region (target),
thereby mapping white matter tracts. This likelihood was estimated by counting number
of 5000 streamline samples that successfully reached the target from the seed after being
projected in random directions and after being guided by the probability distribution. The
number of successful projections reflected the strength of anatomical connectivity. Taken
together, SC reveals and outlines the general network nodes involving sgACC. In
contrast, FC characterizes how network nodes work with each other.
Technical limitations exist in both rs-fMRI and probabilistic tractography techniques.
First, in order to examine FC of sgACC, six bilateral seeds were used to cover the entire
sgACC region. This was done because the region is very large and comprises three
Brodmann areas that may have different connectivities. Although these multiple seeds
may increase the likelihood of false positive error, correcting for multiple seeds may be
extremely stringent for seed-based fMRI analyses. This is demonstrated by studies using
24 seeds (Zhang, et al., 2012), 16 seeds (Margulies, et al., 2007), 18 seeds (Adelstein, et
al., 2011), 4 seeds (Habas, 2010), and 14 seeds (Yu, et al., 2011). However, future studies
should consider multiple seed correction although, before this step, false discovery rate
(FDR) may be used for correcting for multiple voxels as opposed to the more stringent
family-wise error correction (FWE), which was used in this study.
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Second, in rs-fMRI, the MRI images were acquired every two seconds, equivalent to a
sampling rate of 0.5 Hz. This can only detect cortical activity that varies at or below 0.25
Hz. In addition, the rs- fMRI method used in this study assumes synchronous neuronal
oscillations at these low frequencies operating as a network for a common purpose.
Third, the fMRI technique is also limited by spatial resolution. Each fMRI voxel is about
3.125mm x 3.125mm x 4mm in volume, which contains a very large number of neurons
of various types; the signal of specific types of neurons can not be further delineated due
to the limited fMRI spatial resolution. However, SC offers anatomical framework to
support the functional network resulted from fMRI studies.
Fourth, estrous cycle may affect women’s pain perception (Section 2.1.5), and the data
used in this thesis was collected without controlling for estrous cycle in women.
However, the large women subject number used in this study should compensate and
account for the variations in the data due to estrous cycle.
Fifth, in order to compile individual subject results into group results, the former had to
be spatially blurred and registered to the standard space to compensate for individual
brain differences in size and shape. These processes are close to but not perfect, possibly
resulting in slight discrepancies between the result output and where the BOLD actually
occurred in the brain. For instance, the results may show grey matter activation areas,
which also spans some white matter regions. This is due to registration and spatial
blurring – a limitation of most MRI techniques.
Probabilistic tractography is limited by its spatial resolution. Tractography defines its
termination point when it reaches a region of high isotropy or when it reaches the brain
boundary. As such, the termination points do not necessarily reflect the location of axonal
terminations or synapses (Jbabdi and Johansen-Berg, 2011). In addition, it is also difficult
for tractography to differentiate branching fibres from kissing or merging fibres, which
could lead to possible false positive or false negative tracts. Moreover, tractography is
incapable of discerning 1) high curvature fibres and 2) multiple fibres that cross and form
patterns of ―T‖, ―L‖, ―+‖, and ―W‖. This creates confounds in the tractography results
(Jones, et al., 2013). Other limitations of tractography include its inability to discern fibre
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polarity, e.g., efferent vs afferent fibres, or to distinguish efferent from afferent axonal
projects; to detect precise axonal collaterals; and to track where white matter tract
terminate in a cortical layer, e.g., radial accuracy (Jbabdi and Johansen-Berg, 2011).
Finally, compared to analysis I, analysis II of the tractography used larger ROIs, which
included more voxels, to increase the sensitivity of the method. These larger ROIs reduce
the multiple comparison problem because, although composed of many voxels, each ROI
was still regarded as single entity. The ROIs’ greater volume was balanced with their
lower spatial specificity, thus not affecting the statistical stringency. Although each seed
ROI spanned a few Brodmann areas with slightly different connectivity, these areas are
all interconnected (Figure 5-1). Thus, the tractography results should be similar with the
seed ROIs either combined or separated although separating the seed ROIs unnecessarily
decreases the experimental sensitivity for detecting tracts.
5.2.2 PAG and descending modulation pathway
The rs-fMRI analysis revealed for the first time that, compared to men, women had
stronger FC between ventrolateral PAG and the sgACC, seed N which is centred in
subgenual BA32. This suggests that women may activate the descending pain modulation
pathway involving PAG more effectively than men.
A number of animal studies provide evidence for the anatomical connection between
BA32 and PAG and possible functions of this connection. For example, in monkey
studies, by using retrograde tracers in PAG and anterograde tracers in PFC, BA32 and 25
were found to project to the PAG, comprising part of the medial prefrontal network (An,
et al., 1998). The density of projections to PAG increased towards the more rostral
cingulate gyrus, specifically layer V, where pryamidal neurons and mu-opioid receptors
predominate (Vogt and Vogt, 2009). Mu-opioid receptors were believed to coordinate
emotional motor systems in the amygdala, ACC, and PAG, thereby modulating emotion-
related motor outputs (Vogt and Vogt, 2009).
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The ventrolateral PAG is responsible for facilitating opioid-mediated analgesia as
demonstrated by descending pain modulation models (Behbehani, 1995; Lovick, 1993).
For instance, in a rat study, kainic acid injection excited the ventrolateral PAG and
resulted in antinociception, which was reproduced by morphine microinjection (Morgan,
et al., 1998). In another rat study, morphine injection in the ventrolateral PAG revealed
antinociception effects in terms of pinch withdrawal latency. However, the
antinociception response was not reproduced when morphine was injected in the
dorsolateral PAG (Yaksh, et al., 1976; Yeung, et al., 1977). Further support for the
existence of an opioid system in ventral PAG, comes from the finding that the opioid
antagonist naloxone was effective in blocking antinociception only when administered in
ventral PAG (Cannon, et al., 1982).
Tracer studies have found that the PAG is heavily connected to the raphe. For example,
in cat, the ventrolateral PAG projects to the raphe (Abols and Basbaum, 1981). In rat, the
ventrolateral PAG was found to preferentially project to the caudal part of the lateral
paragigantocellular nucleus, the rostroventrolateral reticular nucleus, and raphe magnus
(Cameron, et al., 1995b). In another retrograde tracer study in rats, the ventrolateral PAG
was found to project to the RVM (Henderson, et al., 1998). Animal tracer studies have
shown that spinal cord dorsal horn neurons project contralaterally to the ventrolateral
PAG (Bandler and Keay, 1996; Keay, et al., 1997; Wiberg, et al., 1987; Yezierski, 1988)
and so the PAG is engaged both by ascending projections from the spinal cord and by
descending projections from the cortex.
5.2.3 Raphe and descending modulation pathway
The rs-fMRI analysis showed for the first time that, compared to men, women had
stronger FC between raphe and the sgACC, in seed H, which is centred in subgenual
BA24. This suggests that women may activate the descending pain modulation pathway
involving raphe more effectively than men. Anterograde and retrograde studies in
monkey have revealed the reciprocal axonal interconnection among BA24, 25, and 32
(Barbas, et al., 1999; Barbas and Pandya, 1989; Pandya, et al., 1981; Vogt and Pandya,
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1987). BA25 and 32 directly project axons to the medial and dorsal raphe as shown by
tracer studies in monkey (Chiba, et al., 2001; Freedman, et al., 2000). Thus, BA24 may
use BA25 and 32 as a relay to reach the raphe.
The raphe is an important intermediary between the spinal cord and PAG in mediating
analgesia, which has been supported by structural and functional studies in animals. First,
animal tracer studies have shown that the ventrolateral PAG sends a large number of
axons to the raphe (Beitz, et al., 1983a; Beitz, et al., 1983b; Lakos and Basbaum, 1988;
Li, et al., 1990). Second, neuropharmcological and electrophysiology studies have shown
that the resting activity and the evoked response of raphe are strongly influenced by PAG
stimulation (Behbehani, 1981; Behbehani, 1982; Behbehani and Fields, 1979; Behbehani,
et al., 1981; Pomeroy and Behbehani, 1979; Shah and Dostrovsky, 1980). Third, lesions
in the raphe were found to avert analgesia induced by electrical or morphine stimulation
in PAG (Gebhart, et al., 1983; Prieto, et al., 1983; Proudfit and Anderson, 1975;
Sandkuhler and Gebhart, 1984b). Fourth, in rat studies, electrical stimulation of the raphe
inhibited the tail flick spinal nociceptive response (Sandkuhler and Gebhart, 1984a;
Sandkuhler, et al., 1988). Fifth, retrograde tracer studies have shown that the majority of
the axons in the dorsolateral funiculus – a descending opiate tract from the brainstem -
originated from raphe and the paragigantocelluaris (Abols and Basbaum, 1981; Basbaum
and Fields, 1979; Lakos and Basbaum, 1988; Sim and Joseph, 1992).
5.2.4 MD thalamus and medial system
The rs-fMRI analysis showed for the first time that compared to men, women had
stronger FC between MD thalamus and the sgACC, in (seed A, which is centred in
subgenual BA25, the most posterior part of the sgACC. This suggests that, compared to
men, women may modulate the medial affective system involving MD thalamus more
effectively, thereby habituate to pain more efficiently. In retrograde tracer studies of
rhesus monkeys, the magnocellular part of MD thalamus as well as the entorhinal cortex
were found to project to BA25 (Bachevalier, et al., 1997). In another study, the dorsal
parvicellular part of the MD thalamus was found to project to BA25 (Vogt, et al., 1987).
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Studies in monkey also showed that BA25 projects to anterior hypothalamus, PAG,
amygdala, and MD thalamus (Freedman, et al., 2000).
Animal literature has indicated the involvement of submedial nucleus of rats, which is
developmentally related to the human MD thalamus, and its surrounding cortical regions
in pain modulation. In a study using 46 Sprague-Dawley adult rats, injection of glutamate
into the submedial nucleus decreased heat-evoked tail flick - a nocifensive response. This
response returned after injection of GABA into the ventrolateral orbital cortex and PAG.
Further, the GABA injection rendered subsequent glutamate injection in submedial
nucleus ineffective for antinociception. This suggests that submedial nucleus in rats or
MD thalamus in humans may modulate pain via a network involving the ventrolateral
orbital cortex and PAG (Zhang, et al., 1998). Adjacent to MD, the habenula is a cortical
region that contains a large amount of opiate receptors (Atweh and Kuhar, 1977). In a rat
study , morphine injection or electrical stimulation in the habenula led to analgesia after
formalin treatment, revealing habenula’s role in antinociception (Cohen and Melzack,
1993).
The MDvc is thought be involved in pain affect. In MDvc, third order neurons receive
neuronal projections from lamina I via the STT and send axons to ACC (Dostrovsky and
Craig, 2006). Both ACC and MDvc were grouped within a network associated with the
affective processing of pain. Findings from primate studies further led to the concept that
the cortical pain processing system was divided into two sub-systems: the lateral (SI, SII,
VPL, VPM, VPI) and medial system (MDvc, insula, ACC, VMpo, Pf, CL). While the
lateral system is believed to play a larger role in the sensory-discriminative pain
dimension, the medial system is thought to be more important in the affective-
motivational pain dimension (for review see (Treede, et al., 1999)).
A number of studies also found that MD thalamus partly plays a role in the sensory-
discriminative pain dimension. In an electrophysiology study using cat, thermoreceptive
and nociceptive neurons were found to project from lamina I to submedial nucleus of the
cat, or the human equivalent of MD, providing evidence for the role of MD thalamus in
sensory-discrimination pain dimension (Craig and Dostrovsky, 2001). Moreover, in male
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rhesus monkeys who trained to discern noxious heat stimuli, small increases in the
noxious heat stimuli increased the firing rate of neurons in the medial thalamus. This
suggests that MD thalamus may also contribute to the sensory-discriminative dimension
of pain (Bushnell and Duncan, 1989).
5.2.5 Salience and attention network
Human studies have shown that anterior insula, aMCC, and TPJ are involved in the
salience network, which becomes activated as one perceives an attention-grabbing
stimulus. For instance, in an fMRI study of six males and four females, the subjects
received visual, auditory, and tactile stimulations (Downar, et al., 2000). The visual
stimuli were abstract object images with different shapes and colours. The auditory
stimuli were the sound recording of running water and of frog croaking. The tactile
stimulus was the brushing of subjects’ right lower legs using a shower brush. Bilateral
TPJ, left SMA/CMA, and right anterior insula were activated in subjects in response to a
mix of three sensory cues, which suggests these brain regions as part of the attention
directing or salience network (Downar, et al., 2000). In another fMRI study involving
five males and five females, subjects were given visual and auditory stimuli and were
told to respond to one of the stimulus, which was defined as the behaviourally relevant
stimulus, by raising their index finger whenever the behaviourally relevant stimulus
changes. During both behaviourally relevant and irrelevant stimuli, bilateral TPJ, left
SMA/CMA, and bilateral anterior insula activation was significant. Specifically, the left
anterior insula was activated more strongly in response to behaviourally relevant stimulus
than behaviourally irrelevant stimulus (Downar, et al., 2001). This suggests the role of
left anterior insula in salience detection under both behaviourally relevant and irrelevant
contexts. In a follow-up fMRI study, the subjects were presented with baseline visual,
auditory, and tactile stimuli separately. In each condition, the baseline (familiar) stimulus
was intermittently interrupted by a novel stimulus. As a result, the right anterior insula
was activated more strongly during the administration of the novel stimulus than familiar
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stimulus. This implicates the role of right anterior insula in salience processing of novel
stimulus (Downar, et al., 2002).
The aMCC, TPJ, and anterior insula have also been implicated in the ventral attention
network (VAN) and so the two networks can be thought of being more less the same
network. The VAN is typically described as including the anterior insula, middle frontal
gyrus, frontal operculum, and inferior frontal gyrus, and TPJ (Corbetta, et al., 2008). In
an fMRI study of nine females and 11 males, subjects received auditory, tactile, and
visual stimuli and were asked to respond as soon as possible by pushing a button. During
control condition, subjects received the stimuli passively without the need to respond.
Results showed that TPJ, anterior insula, and aMCC were activated more strongly during
the experimental condition than control condition, revealing their role in directing
attention (Langner, et al., 2012). An fMRI study in patients who had suffered a stroke
reported the association between the VAN disruption and spatial neglect, providing
clinical evidence for the role of aMCC, TPJ, and anterior insula in salience detection and
attention (He, et al., 2007). Tractography studies revealed SC between the anterior insula
and TPJ (Umarova, et al., 2010), providing anatomical evidence for their FC in the VAN.
This thesis reports that sgACC is functionally connected to chief nodes of the salience
and attention network: anterior insula, aMCC, and TPJ. The sgACC connectivity with
these nodes will be discussed in the following sections.
5.2.5.1 Anterior insula and salience
The rs-fMRI analysis showed for the first time that, compared to women, men had
stronger FC between anterior insula and the sgACC, in seed B and E regions which are
centred at subgenual BA25 and subgenual BA24, respectively. Given that the anterior
insula is part of the attention/salience network, this result suggests that, compared to
women, men’s stronger activation of the attention network could sustain their attention to
pain, thereby hindering pain habituation.
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The FC result is supported by SC revealed by tracer studies. BA24, 25, and 32 are
strongly and reciprocally interconnected with the orbitofrontal cortex including the insula
(Morecraft and Tanji, 2009). Retrograde tracer studies in monkeys have shown that the
insula projects to BA24 and 32 (Mesulam and Mufson, 1982; Vogt, et al., 1987).
Specifically, in monkeys, retrograde tracers introduced in BA24 revealed that it received
input from dysgranula area and granular area of insula (Morecraft and Van Hoesen,
1998). Anterograde tracer studies in monkeys revealed that ACC projects mainly to the
mid-insula – dysgranular area of insula (Mufson and Mesulam, 1982; Pandya, et al.,
1981).
5.2.5.2 aMCC and salience
The rs-fMRI analysis showed that, compared to women, men had stronger FC between
MCC and seed C/E/H, which are centred at subgenual BA24. This concurs with monkey
tracer studies in that the MCC strongly interconnects with BA24, 25, and 32; and
orbitofrontal cortex including dysgranular and agranular insula, for review see (Morecraft
and Tanji, 2009). Given that the aMCC is part of the attention/salience network, this
result suggests that, compared to women, men’s stronger activation of the attention
network could sustain their attention to pain, thereby hindering pain habituation.
5.2.5.3 TPJ and salience
The rs-fMRI analysis showed that, compared to women, men had stronger FC between
TPJ and seed H, which is centred at subgenual BA24. Given that the aMCC is part of the
attention/salience network, this result suggests that, compared to women, men’s stronger
activation of the attention network could sustain their attention to pain and hinder pain
habituation. Lesion studies in monkey and tractography studies have shown SC between
TPJ and anterior insula (Pandya and Kuypers, 1969; Saur, et al., 2008; Schmahmann,
2006; Umarova, et al., 2010).
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The TPJ is located at the intersection of three cortical regions: inferior parietal lobe,
lateral occipital cortex, and superior temporal sulcus (Corbetta, et al., 2008). The role of
TPJ is pronounced not only in detecting innocuous stimuli but also in noxious stimuli.
For example, in an fMRI study in which subjects received noxious and innocuous stimuli,
the TPJ activation was activated throughout the noxious stimulation but was only
responsive to the onset and offset of the innocuous stimuli. These findings implicated the
TPJ with stimulus salience and pain (Downar, et al., 2003). Other fMRI studies in
humans have also demonstrated the role of TPJ in reorienting attention as driven by
visual stimuli (Indovina and Macaluso, 2007; Serences, et al., 2005)
5.2.6 Hypothalamus and descending modulation pathway
The probabilistic tractography analysis showed that, compared to women, men had
stronger SC between left hypothalamus and left sgACC, which spanned the entire left
sgACC. This suggests that women and men may use different descending pain
modulation systems to greater or lesser degrees (Lovick, 1993) that involve sgACC.
Specifically, women may more heavily use the sgACC-ventrolateral PAG-raphe system,
whereas men may more heavily use the hypothalamus-lateral PAG-RVM system.
Animal studies have revealed a number of evidence to support the sgACC-hypothalamus
connectivity. First, electrophysiological studies in rat reported reciprocal axonal
projections between the lateral hypothalamus and sgACC including (BA24, 25) using
such that electrical stimulation in the sgACC area could lead to either excitatory or
inhibitory effect in the lateral hypothalamus (Kita and Oomura, 1981). Second, in cat,
tracing study showed efferent projections from sgACC (BA25, 32) to dorsal and lateral
hypothalamus (Room, et al., 1985). Third, in monkeys, tracer study showed sgACC
(BA25, 32) projection to nucleus accumbens, amygdala, and ventromedial hypothalamus
(Chiba, et al., 2001). These sgACC-lateral hypothalamic projections were later found to
use glutamate as neurotransmitter in a retrograde tracer study (Csaki, et al., 2000). A
tracing study reported that the lateral hypothalamus projects to the regions including
central nucleus of amygdala and paraventricular nucleus. The latter then sends axons to
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brainstem regions including PAG, raphe, dorsolateral tegmental nucleus of pons, and
parabrachial nucleus (Berk and Finkelstein, 1982). This sgACC-hypothalamus-brainstem
axis provides evidence for the axonal connectivity, which is used in the descending
modulation.
The male-predominant connectivity of the hypothalamus in this thesis may relate to
clinical studies that have shown hypothalamic activity associated with the onset of cluster
headaches, a chronic pain disorder predominantly in men. For instance, using PET and
MR angiography, activation of hypothalamus, ACC, anterior frontal lobe, and both
insulae was found to correlate significantly with onset of cluster headache (induced by
nitroglycerin inhalation) in a study of 18 cluster headache patients (May, et al., 2000). In
addition, right hypothalamus and ACC activity correlated with the onset of acute
spontaneous cluster headache in a resting state fMRI study involving 12 male cluster
headache patients and 12 male healthy controls (Qiu, et al., 2013). Thus, the
hypothalamus was targeted in cluster headache treatment. In 2004, DBS targeting the
hypothalamus was proven effective in eliminating cluster headache attacks in a patient
with bilateral chronic intractable cluster headache (Leone, et al., 2004). Subsequently,
DBS in the posterior hypothalamus was proven to have the effect of abolishing nocturnal
cluster headache in a clinical trial of three patients having drug-resistant chronic cluster
headache (Vetrugno, et al., 2007).
Animal studies have shown that the hypothalamus contains mu opioid receptors, which
bind to endomorphins and lead to analgesia. Endomorphin-1 and 2 are tetrapeptide
opioids, which selectively bind mu receptors to facilitate analgesia (Goldberg, et al.,
1998). For an example, in an immunocytochemistry study using rats,
immunofluorescence of endomorphin-2 was observed in a number of cortical regions
containing mu-opioid receptor; these regions included spinal cord dorsal horn, septum,
amygdala, locus coeruleus, midline thalamic nuclei, nucleus accumbens, hypothalamus,
and PAG. These pain processing regions specifically bind to endomorphin-2 and no other
opioid peptides, implicating the significance of endomorphin-2 in pain processing
(Schreff, et al., 1998).
84
Endomorphins released from hypothalamus at the spinal cord can cause analgesia. For
instance, in an electrophysiological study of 18 rats, endomorphin-1 decreased the
activity of C-fibre and A beta-fibre while endomorphin-2 reduced only C-fibre activity
(Chapman, et al., 1997). Moreover, in a molecular study targeting rat dorsal root ganglia
via in situ hybridization, mu receptors were expressed in 90% of neurons, which were
expressing substance P precursors (Minami, et al., 1995), suggesting a link between mu
receptors and substance P in analgesia. Another immunocytochemistry study in rats also
found co-localization of endomorphin-2 and substance P in the primary afferent terminals
at laminae I and II. A mechanism was proposed in which substance P is regulated by
endomorphin-2-induced mu autoreceptor activity (Sanderson Nydahl, et al., 2004).
In addition to endomorphin, beta endorphin can also bind and activate mu receptors. Beta
endorphin has low affinity for K opioid receptors and high affinity for mu and delta
opioid receptors (Fields, et al., 1999). Animal studies have provided anatomical evidence
that the hypothalamus uses beta endorphin in facilitating descending pain modulation.
For example, using beta endorphin as neurotransmitter, hypothalamic arcuate nucleus and
nucleus tractus solitaries send monosynpatic projections to the spinal cord dorsal horn
(Fields, et al., 1999; Millan, 2002). The arcuate nucleus also projects a significant amount
of axons to the PAG, where beta-endorphin is released as neurotransmitter, thereby
facilitating descending pain inhibition (Mansour, et al., 1995; Millan, 1986).
The analgesic effect of beta endorphin has also been elucidated in research. For instance,
in male rat studies, the injection of rabbit antiserum against beta endorphin caused an
increase of nocifensive behaviour – licking and flinching - in response to formalin
injection (Porro, et al., 1999; Wu, et al., 2001). In another rat study, induced by radio
frequency electrical stimulation, lesion in the hypothalamic arcuate nucleus attenuated
antinociception, which was measured in tail-flick latencies in response to focused light
beam. This antinociception attenuation was found to correlate to beta endorphin reduction
(Millan, et al., 1986). In another rat study involving 28 animals, a beta endorphin
precursor protein was truncated via site-directed genetic mutagenesis, which attenuated
analgesia from mild swim stress (Rubinstein, et al., 1996). These studies suggest the
importance of hypothalamic beta endorphin in facilitating analgesia.
85
5.2.6.1 Spinohypothalamic tract
The spinohypothalamic tract (SHT) passes afferent signals directly from the spinal cord
to the hypothalamus. Its neuronal projections have been revealed by a number of animal
electrophysiological, tracer, and lesion studies (Burstein, et al., 1987; Burstein, et al.,
1996; Cliffer, et al., 1991). For instance, in rats, electrical stimulation of the lateral
hypothalamus antidromically activated the spinal cord dorsal horn lamina I (Burstein, et
al., 1987). SHT has been postulated to participate in autonomic as well as endocrine
regulation, and the affective pain processing (Burstein, et al., 1996; Dostrovsky and
Craig, 2006). In animal studies, electrical stimulation in the Hy elicited cardiovascular
variations (Abrahams, et al., 1960; Mancia, et al., 1972). In a electrophysiology study
done on cats, stimulation of the Hy was followed by cardiovascular changes as well as by
inhibition of the nociceptive afferent signals in the spinal cord dorsal horn (Morton and
Duggan, 1986). Thus, the Hy may be involved in both pain perception and the associated
stress responses.
Sex differences have been observed in autonomic regulation. The autonomic system
regulates blood pressure via modulation of cardiac output (Charkoudian, et al., 2005) and
peripheral resistance (Burt, et al., 1995; Charkoudian, et al., 2005; Charkoudian, et al.,
2006; Wiinberg, et al., 1995). The latter occurs from vasodilation or vasoconstriction
mediated by sympathetic nerve activity (SNA) via α-adrenergic mechanisms.
Specifically, the hypothalamus participates in the sympathomedullary (SAM) pathway, in
which, it activates the adrenal medulla, causing it to secrete adrenalin, leading to a
sympathetic response. In a 2010 study, blockage of α-adrenergic receptors led to greater
blood pressure drop in men than women, implying the stronger dependence of SNA in
men for blood pressure regulation than in women (Schmitt, et al., 2010). This conforms
with the tractography results of this thesis. Specifically, at rest, the finding that men had
stronger sgACC-SC with the hypothalamus suggests a more efficient and stronger
facilitation of the sympathetic response than in women. This mechanism also supports the
existing descending modulation model (Lovick, 1993). Compared to women, men’s
86
stronger sgACC-SC with the hypothalamus also suggests that their greater preference for
hypothalamus-mediated descending controls, which can be inhibited by a number of
regions, e.g., caudal ventrolateral medulla and raphe nucleus (Lovick, 1993), making this
pathway prone to inhibition. On the other hand, women’s preferred, ventrolateral PAG-
mediated descending pathway is uninhibited (Lovick, 1993), possibly making this
pathways more efficient than men’s preferred pathway, providing women with more
effective antinociception and pain habituation. Thus, the sex difference in the preference
of descending modulation pathway provides an anatomical mechanism for the sex
difference in pain habituation.
5.3 FUTURE DIRECTIONS
This finding from thesis can be further understood in future studies. For example,
graphical analysis could be used to further characterize how the regions identified in this
thesis interact with each other. These brain areas could also be used as seeds in another
fMRI analysis while masking the brain with the exception of sgACC. By applying the
mask, the statistical threshold will be lower than a whole brain analysis, which was done
in this thesis. The lower threshold will likely yield more precise results in the sgACC
subregions, whose functions could then be further characterized. Another future study
could be to use the brain areas found to be structurally connected to the sgACC, as seeds
in another tractography analysis to further parcellate their structurally connectivity to
sgACC subregions. Another extension of this thesis would be to more directly link the
strength of the functional and structural connectivities in individual men and women to
their individual pain sensitivity and responsiveness in an in depth psychophysical study.
Finally, this thesis provides evidence for targeting sgACC in deep brain stimulation as a
potential treatment for chronic pain.
87
5.4 CONCLUSION
Taken together, this thesis provides neural basis for sex difference in pain habituation.
First, women’s stronger sgACC FC with the descending pain modulation areas (raphe,
PAG) likely contributes to their greater efficacy in pain modulation than men. Second,
their greater sgACC FC with the MD thalamus may enhance their modulation of the
affective dimension of pain. Third, men’s stronger sgACC FC with the salience/attention
network (anterior insula, aMCC) may heighten and sustain their attention to pain. Fourth,
men’s stronger sgACC SC with the hypothalamus suggests their greater preference for
using the possibly slower hypothalamus-mediated descending modulation pathway than
the arcuate-mediated descending modulation pathway in women. These findings
implicate a mechanism for pain habituation and its associated sex differences.
88
Figure 5-1.Structural connections from sgACC (green lines) or from sgACC-
associated regions (purple lines) reviewed from structural studies
sgACC consists of BA25, s24, and s32. Arrowed connections: from tracer studies; double
arrow: reciprocal connection. Non-arrowed connections: from tractography studies.
sgACC: subgenual anterior cingulate; 25: BA25; s24: subgenual BA24; s32: subgenual
BA32; Hy: hypothalamus; Amy: amygdala; MD: medial dorsal thalamic nucleus; PAG:
periaqueductal grey; TPJ: temporoparietal junction; aMCC: anterior midcingulate; aINS:
anterior insula. Adapted from (An, et al., 1998; Bachevalier, et al., 1997; Barbas, et al.,
1999; Barbas and Pandya, 1989; Berk and Finkelstein, 1982; Chiba, et al., 2001;
Freedman, et al., 2000; Kita and Oomura, 1981; Morecraft and Tanji, 2009; Morecraft
and Van Hoesen, 1998; Mufson and Mesulam, 1982; Pandya and Kuypers, 1969; Pandya,
et al., 1981; Room, et al., 1985; Saur, et al., 2008; Schmahmann, 2006; Umarova, et al.,
2010; Vogt and Pandya, 1987; Vogt, et al., 1987). Brain outline image adapted from
(http://www2.le.ac.uk/departments/gradschool/training/events/caferesearch/cafe-brain).
89
Figure 5-2. Descending modulation axonal projections
Black lines: descending projection. Red lines: ascending projections. Lam: lamina; DLF:
dorsolateral funiculus; NACs: nucleus accumbens; Hy: hypothalamus; Amy: amygdala;
aINS: anterior insula; PAG: periaqueductal gray: RVM: rostroventral medulla; LC: locus
coeruleus; PB: parabrachial nucleus; SC DH: spinal cord dorsal horn; RF: reticular
formation; NCF: nucleus cuneiformis; +: excitatory; -: inhibitory. Adapted from
(Basbaum and Fields, 1984; Fields, et al., 1999). Brain outline image adapted from
(http://www2.le.ac.uk/departments/gradschool/training/events/caferesearch/cafe-brain).
90
Figure 5-3. Descending pain modulation pathways
In achieving antinociception, men may prefer the hypothalamus-mediated pathway (blue)
while women may use the Arcuate-mediated pathway (pink) to a greater degree. Adapted
from (Lovick, 1993).
91
References
Abols IA, and Basbaum AI. Afferent connections of the rostral medulla of the cat: a neural
substrate for midbrain-medullary interactions in the modulation of pain. J Comp Neurol
1981: 201 (2): 285-97.
Abrahams VC, Hilton SM, and Zbrozyna A. Active muscle vasodilatation produced by
stimulation of the brain stem: its significance in the defence reaction. J Physiol 1960: 154
491-513.
Ackerman JJH, and Neil JJ. Biophysics of Diffusion in Cells. In: DK Jones. Diffusion MRI
Oxford: University Press, 2011. pp. 110-124.
Adams JE, Hosobuchi Y, and Fields HL. Stimulation of internal capsule for relief of chronic
pain. J Neurosurg 1974: 41 (6): 740-4.
Adelstein JS, Shehzad Z, Mennes M, Deyoung CG, Zuo XN, Kelly C, Margulies DS, Bloomfield
A, Gray JR, Castellanos FX, and Milham MP. Personality is reflected in the brain's
intrinsic functional architecture. PLoS One 2011: 6 (11): e27633.
Aertsen AM, Gerstein GL, Habib MK, and Palm G. Dynamics of neuronal firing correlation:
modulation of "effective connectivity". J Neurophysiol 1989: 61 (5): 900-17.
Aimone LD, and Gebhart GF. Stimulation-produced spinal inhibition from the midbrain in the
rat is mediated by an excitatory amino acid neurotransmitter in the medial medulla. J
Neurosci 1986: 6 (6): 1803-13.
Albe-Fessard D, Boivie J, Grant G, and Levante A. Labelling of cells in the medulla oblongata
and the spinal cord of the monkey after injections of horseradish peroxidase in the
thalamus. Neurosci Lett 1975: 1 (2): 75-80.
Allman JM, Hakeem A, Erwin JM, Nimchinsky E, and Hof P. The anterior cingulate cortex. The
evolution of an interface between emotion and cognition. Ann N Y Acad Sci 2001: 935
107-17.
Aloisi AM. Sensory Effects of Gonadal Hormones In: RB Fillingim. Sex, gender, and pain.
Seattle: IASP Press, 2000. pp. 18.
An X, Bandler R, Ongur D, and Price JL. Prefrontal cortical projections to longitudinal columns
in the midbrain periaqueductal gray in macaque monkeys. J Comp Neurol 1998: 401 (4):
455-79.
Apkarian AV, Bushnell MC, Treede RD, and Zubieta JK. Human brain mechanisms of pain
perception and regulation in health and disease. Eur J Pain 2005: 9 (4): 463-84.
Atweh SF, and Kuhar MJ. Autoradiographic localization of opiate receptors in rat brain. II. The
brain stem. Brain Res 1977: 129 (1): 1-12.
Azkue JJ, Mateos JM, Elezgarai I, Benitez R, Lazaro E, Streit P, and Grandes P. Glutamate-like
immunoreactivity in ascending spinofugal afferents to the rat periaqueductal grey. Brain
Res 1998: 790 (1-2): 74-81.
Baamonde AI, Hidalgo A, and Andres-Trelles F. Sex-related differences in the effects of
morphine and stress on visceral pain. Neuropharmacology 1989: 28 (9): 967-70.
Bachevalier J, Meunier M, Lu MX, and Ungerleider LG. Thalamic and temporal cortex input to
medial prefrontal cortex in rhesus monkeys. Exp Brain Res 1997: 115 (3): 430-44.
Badillo-Martinez D, Kirchgessner AL, Butler PD, and Bodnar RJ. Monosodium glutamate and
analgesia induced by morphine. Test-specific effects. Neuropharmacology 1984: 23 (10):
1141-9.
92
Bajic D, and Proudfit HK. Projections of neurons in the periaqueductal gray to pontine and
medullary catecholamine cell groups involved in the modulation of nociception. J Comp
Neurol 1999: 405 (3): 359-79.
Bandler R, and Keay KA. Columnar organization in the midbrain periaqueductal gray and the
integration of emotional expression. Prog Brain Res 1996: 107 285-300.
Bandler R, and Shipley MT. Columnar organization in the midbrain periaqueductal gray:
modules for emotional expression? Trends Neurosci 1994: 17 (9): 379-89.
Bantick SJ, Wise RG, Ploghaus A, Clare S, Smith SM, and Tracey I. Imaging how attention
modulates pain in humans using functional MRI. Brain 2002: 125 (Pt 2): 310-9.
Barbas H, Ghashghaei H, Dombrowski SM, and Rempel-Clower NL. Medial prefrontal cortices
are unified by common connections with superior temporal cortices and distinguished by
input from memory-related areas in the rhesus monkey. J Comp Neurol 1999: 410 (3):
343-67.
Barbas H, and Pandya DN. Architecture and intrinsic connections of the prefrontal cortex in the
rhesus monkey. J Comp Neurol 1989: 286 (3): 353-75.
Barbas H, Saha S, Rempel-Clower N, and Ghashghaei T. Serial pathways from primate
prefrontal cortex to autonomic areas may influence emotional expression. BMC Neurosci
2003: 4 25.
Basbaum AI, and Bushnell MC. Science of Pain. Elsevier Inc. 2009;
Basbaum AI, Clanton CH, and Fields HL. Three bulbospinal pathways from the rostral medulla
of the cat: an autoradiographic study of pain modulating systems. J Comp Neurol 1978:
178 (2): 209-24.
Basbaum AI, and Fields HL. Endogenous pain control mechanisms: review and hypothesis. Ann
Neurol 1978: 4 (5): 451-62.
Basbaum AI, and Fields HL. The origin of descending pathways in the dorsolateral funiculus of
the spinal cord of the cat and rat: further studies on the anatomy of pain modulation. J
Comp Neurol 1979: 187 (3): 513-31.
Basbaum AI, and Fields HL. Endogenous pain control systems: brainstem spinal pathways and
endorphin circuitry. Annu Rev Neurosci 1984: 7 309-38.
Basser PJ, and Ozarslan E. Anisotropic Diffusion: From the Apparent Diffusion Coefficient to
the Apparent Diffusion Tensor. In: DK Jones. Diffusion MRI Oxford: University Press,
2011. pp. 79-91.
Beaulieu C. What Makes Diffusion Anisotropic in the Nervous System? In: DK Jones. Diffusion
MRI Oxford: University Press, 2011. pp. 91-119.
Beckmann M, Johansen-Berg H, and Rushworth MF. Connectivity-based parcellation of human
cingulate cortex and its relation to functional specialization. J Neurosci 2009: 29 (4):
1175-90.
Beecher HK. Pain in Men Wounded in Battle. Ann Surg 1946: 123 (1): 96-105.
Behbehani MM. Effect of chronic morphine treatment on the interaction between the
periaqueductal grey and the nucleus raphe magnus of the rat. Neuropharmacology 1981:
20 (6): 581-6.
Behbehani MM. The role of acetylcholine in the function of the nucleus raphe magnus and in the
interaction of this nucleus with the periaqueductal gray. Brain Res 1982: 252 (2): 299-
307.
Behbehani MM. Functional characteristics of the midbrain periaqueductal gray. Prog Neurobiol
1995: 46 (6): 575-605.
93
Behbehani MM, and Fields HL. Evidence that an excitatory connection between the
periaqueductal gray and nucleus raphe magnus mediates stimulation produced analgesia.
Brain Res 1979: 170 (1): 85-93.
Behbehani MM, Pomeroy SL, and Mack CE. Interaction between central gray and nucleus raphe
magnus: role of norepinephrine. Brain Res Bull 1981: 6 (5): 361-4.
Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, and Woolrich MW. Probabilistic diffusion
tractography with multiple fibre orientations: What can we gain? Neuroimage 2007: 34
(1): 144-55.
Behrens TE, Woolrich MW, Jenkinson M, Johansen-Berg H, Nunes RG, Clare S, Matthews PM,
Brady JM, and Smith SM. Characterization and propagation of uncertainty in diffusion-
weighted MR imaging. Magn Reson Med 2003: 50 (5): 1077-88.
Behzadi Y, Restom K, Liau J, and Liu TT. A component based noise correction method
(CompCor) for BOLD and perfusion based fMRI. Neuroimage 2007: 37 (1): 90-101.
Beitz AJ. The organization of afferent projections to the midbrain periaqueductal gray of the rat.
Neuroscience 1982a: 7 (1): 133-59.
Beitz AJ. The sites of origin brain stem neurotensin and serotonin projections to the rodent
nucleus raphe magnus. J Neurosci 1982b: 2 (7): 829-42.
Beitz AJ, Mullett MA, and Weiner LL. The periaqueductal gray projections to the rat spinal
trigeminal, raphe magnus, gigantocellular pars alpha and paragigantocellular nuclei arise
from separate neurons. Brain Res 1983a: 288 (1-2): 307-14.
Beitz AJ, Shepard RD, and Wells WE. The periaqueductal gray-raphe magnus projection
contains somatostatin, neurotensin and serotonin but not cholecystokinin. Brain Res
1983b: 261 (1): 132-7.
Bereiter DA, Stanford LR, and Barker DJ. Hormone-induced enlargement of receptive fields in
trigeminal mechanoreceptive neurons. II. Possible mechanisms. Brain Res 1980: 184 (2):
411-23.
Berglund LA, Derendorf H, and Simpkins JW. Desensitization of brain opiate receptor
mechanisms by gonadal steroid treatments that stimulate luteinizing hormone secretion.
Endocrinology 1988: 122 (6): 2718-26.
Berk ML, and Finkelstein JA. Efferent connections of the lateral hypothalamic area of the rat: an
autoradiographic investigation. Brain Res Bull 1982: 8 (5): 511-26.
Berkley KJ. Sex differences in pain. Behav Brain Sci 1997: 20 (3): 371-80; discussion 435-513.
Bingel U, Herken W, Teutsch S, and May A. Habituation to painful stimulation involves the
antinociceptive system--a 1-year follow-up of 10 participants. Pain 2008: 140 (2): 393-4.
Bingel U, Lorenz J, Schoell E, Weiller C, and Buchel C. Mechanisms of placebo analgesia:
rACC recruitment of a subcortical antinociceptive network. Pain 2006: 120 (1-2): 8-15.
Bingel U, Schoell E, Herken W, Buchel C, and May A. Habituation to painful stimulation
involves the antinociceptive system. Pain 2007: 131 (1-2): 21-30.
Bingel U, and Tracey I. Imaging CNS modulation of pain in humans. Physiology (Bethesda)
2008: 23 371-80.
Bittar RG, Kar-Purkayastha I, Owen SL, Bear RE, Green A, Wang S, and Aziz TZ. Deep brain
stimulation for pain relief: a meta-analysis. J Clin Neurosci 2005: 12 (5): 515-9.
Bodnar RJ, Romero MT, and Kramer E. Organismic variables and pain inhibition: roles of
gender and aging. Brain Res Bull 1988: 21 (6): 947-53.
Bouhassira D, Bing Z, and Le Bars D. Studies of the brain structures involved in diffuse noxious
inhibitory controls: the mesencephalon. J Neurophysiol 1990: 64 (6): 1712-23.
94
Bouhassira D, Chitour D, Villanueva L, and Le Bars D. Morphine and diffuse noxious inhibitory
controls in the rat: effects of lesions of the rostral ventromedial medulla. Eur J Pharmacol
1993: 232 (2-3): 207-15.
Brighina F, Piazza A, Vitello G, Aloisio A, Palermo A, Daniele O, and Fierro B. rTMS of the
prefrontal cortex in the treatment of chronic migraine: a pilot study. J Neurol Sci 2004:
227 (1): 67-71.
Brown MA. MRI: Basic Principles and Applications. Wiley 2003;
Burstein R, Cliffer KD, and Giesler GJ, Jr. Direct somatosensory projections from the spinal
cord to the hypothalamus and telencephalon. J Neurosci 1987: 7 (12): 4159-64.
Burstein R, Falkowsky O, Borsook D, and Strassman A. Distinct lateral and medial projections
of the spinohypothalamic tract of the rat. J Comp Neurol 1996: 373 (4): 549-74.
Burt VL, Whelton P, Roccella EJ, Brown C, Cutler JA, Higgins M, Horan MJ, and Labarthe D.
Prevalence of hypertension in the US adult population. Results from the Third National
Health and Nutrition Examination Survey, 1988-1991. Hypertension 1995: 25 (3): 305-
13.
Bushnell MC, and Duncan GH. Sensory and affective aspects of pain perception: is medial
thalamus restricted to emotional issues? Exp Brain Res 1989: 78 (2): 415-8.
Cadden SW, Villanueva L, Chitour D, and Le Bars D. Depression of activities of dorsal horn
convergent neurones by propriospinal mechanisms triggered by noxious inputs;
comparison with diffuse noxious inhibitory controls (DNIC). Brain Res 1983: 275 (1): 1-
11.
Callaghan PT. Physics of Diffusion. In: DK Jones. Diffusion MRI Oxford: University Press,
2011. pp. 45-56.
Cameron AA, Khan IA, Westlund KN, Cliffer KD, and Willis WD. The efferent projections of
the periaqueductal gray in the rat: a Phaseolus vulgaris-leucoagglutinin study. I.
Ascending projections. J Comp Neurol 1995a: 351 (4): 568-84.
Cameron AA, Khan IA, Westlund KN, and Willis WD. The efferent projections of the
periaqueductal gray in the rat: a Phaseolus vulgaris-leucoagglutinin study. II. Descending
projections. J Comp Neurol 1995b: 351 (4): 585-601.
Campbell CM, France CR, Robinson ME, Logan HL, Geffken GR, and Fillingim RB. Ethnic
differences in diffuse noxious inhibitory controls. J Pain 2008: 9 (8): 759-66.
Cannon JT, Prieto GJ, Lee A, and Liebeskind JC. Evidence for opioid and non-opioid forms of
stimulation-produced analgesia in the rat. Brain Res 1982: 243 (2): 315-21.
Carmichael ST, and Price JL. Connectional networks within the orbital and medial prefrontal
cortex of macaque monkeys. J Comp Neurol 1996: 371 (2): 179-207.
Chang C, and Glover GH. Time-frequency dynamics of resting-state brain connectivity
measured with fMRI. Neuroimage 2010: 50 (1): 81-98.
Chapman V, Diaz A, and Dickenson AH. Distinct inhibitory effects of spinal endomorphin-1 and
endomorphin-2 on evoked dorsal horn neuronal responses in the rat. Br J Pharmacol
1997: 122 (8): 1537-9.
Charkoudian N, Joyner MJ, Johnson CP, Eisenach JH, Dietz NM, and Wallin BG. Balance
between cardiac output and sympathetic nerve activity in resting humans: role in arterial
pressure regulation. J Physiol 2005: 568 (Pt 1): 315-21.
Charkoudian N, Joyner MJ, Sokolnicki LA, Johnson CP, Eisenach JH, Dietz NM, Curry TB, and
Wallin BG. Vascular adrenergic responsiveness is inversely related to tonic activity of
sympathetic vasoconstrictor nerves in humans. J Physiol 2006: 572 (Pt 3): 821-7.
95
Chiba T, Kayahara T, and Nakano K. Efferent projections of infralimbic and prelimbic areas of
the medial prefrontal cortex in the Japanese monkey, Macaca fuscata. Brain Res 2001:
888 (1): 83-101.
Cliffer KD, Burstein R, and Giesler GJ, Jr. Distributions of spinothalamic, spinohypothalamic,
and spinotelencephalic fibers revealed by anterograde transport of PHA-L in rats. J
Neurosci 1991: 11 (3): 852-68.
Coffield JA, Bowen KK, and Miletic V. Retrograde tracing of projections between the nucleus
submedius, the ventrolateral orbital cortex, and the midbrain in the rat. J Comp Neurol
1992: 321 (3): 488-99.
Cohen SR, and Melzack R. The habenula and pain: repeated electrical stimulation produces
prolonged analgesia but lesions have no effect on formalin pain or morphine analgesia.
Behav Brain Res 1993: 54 (2): 171-8.
Corbetta M, Patel G, and Shulman GL. The reorienting system of the human brain: from
environment to theory of mind. Neuron 2008: 58 (3): 306-24.
Craig AD. Pain mechanisms: labeled lines versus convergence in central processing. Annu Rev
Neurosci 2003: 26 1-30.
Craig AD, and Dostrovsky JO. Differential projections of thermoreceptive and nociceptive
lamina I trigeminothalamic and spinothalamic neurons in the cat. J Neurophysiol 2001:
86 (2): 856-70.
Csaki A, Kocsis K, Halasz B, and Kiss J. Localization of glutamatergic/aspartatergic neurons
projecting to the hypothalamic paraventricular nucleus studied by retrograde transport of
[3H]D-aspartate autoradiography. Neuroscience 2000: 101 (3): 637-55.
de Courten-Myers GM. The human cerebral cortex: gender differences in structure and function.
J Neuropathol Exp Neurol 1999: 58 (3): 217-26.
Derbyshire SW, Nichols TE, Firestone L, Townsend DW, and Jones AK. Gender differences in
patterns of cerebral activation during equal experience of painful laser stimulation. J Pain
2002: 3 (5): 401-11.
Dieckmann G, and Witzmann A. Initial and long-term results of deep brain stimulation for
chronic intractable pain. Appl Neurophysiol 1982: 45 (1-2): 167-72.
Doeland HJ, Nauta JJ, van Zandbergen JB, van der Eerden HA, van Diemen NG, Bertelsmann
FW, and Heimans JJ. The relationship of cold and warmth cutaneous sensation to age and
gender. Muscle Nerve 1989: 12 (9): 712-5.
Dostrovsky JO, and Craig AD. Ascending projection systems. In: SB McMahon and M
Koltzenburg. Wall and Melzack's Textbook of Pain. UK: Elsevier Churchill Livingstone,
2006. pp. 187-193.
Downar J, Crawley AP, Mikulis DJ, and Davis KD. A multimodal cortical network for the
detection of changes in the sensory environment. Nat Neurosci 2000: 3 (3): 277-83.
Downar J, Crawley AP, Mikulis DJ, and Davis KD. The effect of task relevance on the cortical
response to changes in visual and auditory stimuli: an event-related fMRI study.
Neuroimage 2001: 14 (6): 1256-67.
Downar J, Crawley AP, Mikulis DJ, and Davis KD. A cortical network sensitive to stimulus
salience in a neutral behavioral context across multiple sensory modalities. J
Neurophysiol 2002: 87 (1): 615-20.
Downar J, Mikulis DJ, and Davis KD. Neural correlates of the prolonged salience of painful
stimulation. Neuroimage 2003: 20 (3): 1540-51.
96
Epstein MT, Hockaday JM, and Hockaday TD. Migraine and reporoductive hormones
throughout the menstrual cycle. Lancet 1975: 1 (7906): 543-8.
Erpelding N, Moayedi M, and Davis KD. Cortical thickness correlates of pain and temperature
sensitivity. Pain 2012: 153 (8): 1602-9.
Essick GK, Afferica T, Aldershof B, Nestor J, Kelly D, and Whitsel B. Human perioral
directional sensitivity. Exp Neurol 1988: 100 (3): 506-23.
Feine JS, Bushnell MC, Miron D, and Duncan GH. Sex differences in the perception of noxious
heat stimuli. Pain 1991: 44 (3): 255-62.
Ferry AT, Ongur D, An X, and Price JL. Prefrontal cortical projections to the striatum in
macaque monkeys: evidence for an organization related to prefrontal networks. J Comp
Neurol 2000: 425 (3): 447-70.
Fields HL, and Anderson SD. Evidence that raphe-spinal neurons mediate opiate and midbrain
stimulation-produced analgesias. Pain 1978: 5 (4): 333-49.
Fields HL, Basbaum AI, and Heinricher MM. Central Nervous System Mechanisms of Pain
Modulation. In: London Churchill Livingstone, 1999. pp. 309-329.
Fields HL, Basbaum AI, and Heinricher MM. Wall and Melzack's Textbook of Pain. In: SB
McMahon and M Koltzenburg. Wall and Melzack's Textbook of Pain. UK: Elsevier
Churchill Livingstone, 2006. pp. 125-130.
Fields HL, Heinricher MM, and Mason P. Neurotransmitters in nociceptive modulatory circuits.
Annu Rev Neurosci 1991: 14 219-45.
Filley CM. Neurobiology of White Matter Disorders. In: DK Jones. Diffusion MRI Oxford:
University Press, 2011. pp. 19-30.
Fillingim RB, King CD, Ribeiro-Dasilva MC, Rahim-Williams B, and Riley JL, 3rd. Sex,
gender, and pain: a review of recent clinical and experimental findings. J Pain 2009: 10
(5): 447-85.
Flor H, Diers M, and Birbaumer N. Peripheral and electrocortical responses to painful and non-
painful stimulation in chronic pain patients, tension headache patients and healthy
controls. Neurosci Lett 2004: 361 (1-3): 147-50.
Floyd NS, Price JL, Ferry AT, Keay KA, and Bandler R. Orbitomedial prefrontal cortical
projections to distinct longitudinal columns of the periaqueductal gray in the rat. J Comp
Neurol 2000: 422 (4): 556-78.
Forman LJ, and Estilow S. Neurotransmitters and estrogen interact to affect beta-endorphin
levels in castrated female rats. Peptides 1986: 7 (5): 775-81.
France CR, and Suchowiecki S. A comparison of diffuse noxious inhibitory controls in men and
women. Pain 1999: 81 (1-2): 77-84.
Freedman LJ, Insel TR, and Smith Y. Subcortical projections of area 25 (subgenual cortex) of
the macaque monkey. J Comp Neurol 2000: 421 (2): 172-88.
Friston KJ, Frith CD, Liddle PF, and Frackowiak RS. Functional connectivity: the principal-
component analysis of large (PET) data sets. J Cereb Blood Flow Metab 1993: 13 (1): 5-
14.
Gallager DW, and Pert A. Afferents to brain stem nuclei (brain stem raphe, nucleus reticularis
pontis caudalis and nucleus gigantocellularis) in the rat as demonstrated by
microiontophoretically applied horseradish peroxidase. Brain Res 1978: 144 (2): 257-75.
Gebhart GF, Sandkuhler J, Thalhammer JG, and Zimmermann M. Inhibition of spinal
nociceptive information by stimulation in midbrain of the cat is blocked by lidocaine
97
microinjected in nucleus raphe magnus and medullary reticular formation. J
Neurophysiol 1983: 50 (6): 1446-59.
George MS, Ketter TA, Parekh PI, Horwitz B, Herscovitch P, and Post RM. Brain activity
during transient sadness and happiness in healthy women. Am J Psychiatry 1995: 152 (3):
341-51.
Glaser EM, and Whittow GC. Evidence for a non-specific mechanism of habituation. J Physiol
1953: 122 (Suppl): 43-4P.
Goldapple K, Segal Z, Garson C, Lau M, Bieling P, Kennedy S, and Mayberg H. Modulation of
cortical-limbic pathways in major depression: treatment-specific effects of cognitive
behavior therapy. Arch Gen Psychiatry 2004: 61 (1): 34-41.
Goldberg IE, Rossi GC, Letchworth SR, Mathis JP, Ryan-Moro J, Leventhal L, Su W, Emmel D,
Bolan EA, and Pasternak GW. Pharmacological characterization of endomorphin-1 and
endomorphin-2 in mouse brain. J Pharmacol Exp Ther 1998: 286 (2): 1007-13.
Greene LC, and Hardy JD. Adaptation of thermal pain in the skin. J Appl Physiol 1962: 17 693-
6.
Grova C, Makni S, Flandin G, Ciuciu P, Gotman J, and Poline JB. Anatomically informed
interpolation of fMRI data on the cortical surface. Neuroimage 2006: 31 (4): 1475-86.
Guillamon A, de Blas MR, and Segovia S. Effects of sex steroids on the development of the
locus coeruleus in the rat. Brain Res 1988: 468 (2): 306-10.
Gutman DA, Holtzheimer PE, Behrens TE, Johansen-Berg H, and Mayberg HS. A tractography
analysis of two deep brain stimulation white matter targets for depression. Biol
Psychiatry 2009: 65 (4): 276-82.
Gybels J, and Kupers R. Central and peripheral electrical stimulation of the nervous system in
the treatment of chronic pain. Acta Neurochir Suppl (Wien) 1987: 38 64-75.
Habas C. Functional connectivity of the human rostral and caudal cingulate motor areas in the
brain resting state at 3T. Neuroradiology 2010: 52 (1): 47-59.
Haber SN, Kunishio K, Mizobuchi M, and Lynd-Balta E. The orbital and medial prefrontal
circuit through the primate basal ganglia. J Neurosci 1995: 15 (7 Pt 1): 4851-67.
Hadjipavlou G, Dunckley P, Behrens TE, and Tracey I. Determining anatomical connectivities
between cortical and brainstem pain processing regions in humans: a diffusion tensor
imaging study in healthy controls. Pain 2006: 123 (1-2): 169-78.
Hammer RP, Jr. The sexually dimorphic region of the preoptic area in rats contains denser opiate
receptor binding sites in females. Brain Res 1984: 308 (1): 172-6.
Hashmi JA, and Davis KD. Women experience greater heat pain adaptation and habituation than
men. Pain 2009: 145 (3): 350-7.
Hashmi JA, and Davis KD. Effects of temperature on heat pain adaptation and habituation in
men and women. Pain 2010: 151 (3): 737-43.
He BJ, Snyder AZ, Vincent JL, Epstein A, Shulman GL, and Corbetta M. Breakdown of
functional connectivity in frontoparietal networks underlies behavioral deficits in spatial
neglect. Neuron 2007: 53 (6): 905-18.
Heinricher MM, Barbaro NM, and Fields HL. Putative nociceptive modulating neurons in the
rostral ventromedial medulla of the rat: firing of on- and off-cells is related to nociceptive
responsiveness. Somatosens Mot Res 1989: 6 (4): 427-39.
Heinricher MM, and Ingram SL. The brainstem and Nociceptive Modulation. In: AI Basbaum
and MC Bushnell. Science of Pain. Slovenia: Elsevier Inc. , 2009. pp. 593-626.
98
Helmstetter FJ, Tershner SA, Poore LH, and Bellgowan PS. Antinociception following opioid
stimulation of the basolateral amygdala is expressed through the periaqueductal gray and
rostral ventromedial medulla. Brain Res 1998: 779 (1-2): 104-18.
Henderson LA, Keay KA, and Bandler R. The ventrolateral periaqueductal gray projects to
caudal brainstem depressor regions: a functional-anatomical and physiological study.
Neuroscience 1998: 82 (1): 201-21.
Hermann DM, Luppi PH, Peyron C, Hinckel P, and Jouvet M. Afferent projections to the rat
nuclei raphe magnus, raphe pallidus and reticularis gigantocellularis pars alpha
demonstrated by iontophoretic application of choleratoxin (subunit b). J Chem Neuroanat
1997: 13 (1): 1-21.
Hosobuchi Y. Subcortical electrical stimulation for control of intractable pain in humans. Report
of 122 cases (1970-1984). J Neurosurg 1986: 64 (4): 543-53.
Hosobuchi Y, Adams JE, and Linchitz R. Pain relief by electrical stimulation of the central gray
matter in humans and its reversal by naloxone. Science 1977: 197 (4299): 183-6.
Hosobuchi Y, Adams JE, and Rutkin B. Chronic thalamic and internal capsule stimulation for
the control of central pain. Surg Neurol 1975: 4 (1): 91-2.
Huettel SA, Song AW, and McCarthy G. Functional Magnetic Resonance Imaging Sinauer
Associates, Inc 2004;
Hutchison WD, Davis KD, Lozano AM, Tasker RR, and Dostrovsky JO. Pain-related neurons in
the human cingulate cortex. Nat Neurosci 1999: 2 (5): 403-5.
Hylden JL, Hayashi H, Dubner R, and Bennett GJ. Physiology and morphology of the lamina I
spinomesencephalic projection. J Comp Neurol 1986: 247 (4): 505-15.
Indovina I, and Macaluso E. Dissociation of stimulus relevance and saliency factors during shifts
of visuospatial attention. Cereb Cortex 2007: 17 (7): 1701-11.
Jacquet YF, and Lajtha A. The periaqueductal gray: site of morphine analgesia and tolerance as
shown by 2-way cross tolerance between systemic and intracerebral injections. Brain Res
1976: 103 (3): 501-13.
Jbabdi S, and Johansen-Berg H. Tractography: where do we go from here? Brain Connect 2011:
1 (3): 169-83.
Johansen-Berg H, Gutman DA, Behrens TE, Matthews PM, Rushworth MF, Katz E, Lozano
AM, and Mayberg HS. Anatomical connectivity of the subgenual cingulate region
targeted with deep brain stimulation for treatment-resistant depression. Cereb Cortex
2008: 18 (6): 1374-83.
Jones DK, Knosche TR, and Turner R. White matter integrity, fiber count, and other fallacies:
the do's and don'ts of diffusion MRI. Neuroimage 2013: 73 239-54.
Kakigi R. Diffuse noxious inhibitory control. Reappraisal by pain-related somatosensory evoked
potentials following CO2 laser stimulation. J Neurol Sci 1994: 125 (2): 198-205.
Kavaliers M, and Innes DG. Sex and day-night differences in opiate-induced responses of insular
wild deer mice, Peromyscus maniculatus triangularis. Pharmacol Biochem Behav 1987:
27 (3): 477-82.
Keay KA, Feil K, Gordon BD, Herbert H, and Bandler R. Spinal afferents to functionally distinct
periaqueductal gray columns in the rat: an anterograde and retrograde tracing study. J
Comp Neurol 1997: 385 (2): 207-29.
Kita H, and Oomura Y. Reciprocal connections between the lateral hypothalamus and the frontal
complex in the rat: electrophysiological and anatomical observations. Brain Res 1981:
213 (1): 1-16.
99
Kong J, Loggia ML, Zyloney C, Tu P, Laviolette P, and Gollub RL. Exploring the brain in pain:
activations, deactivations and their relation. Pain 2010a: 148 (2): 257-67.
Kong J, Tu PC, Zyloney C, and Su TP. Intrinsic functional connectivity of the periaqueductal
gray, a resting fMRI study. Behav Brain Res 2010b: 211 (2): 215-9.
Kosek E, and Ordeberg G. Lack of pressure pain modulation by heterotopic noxious
conditioning stimulation in patients with painful osteoarthritis before, but not following,
surgical pain relief. Pain 2000: 88 (1): 69-78.
Kucyi A, Moayedi M, Weissman-Fogel I, Hodaie M, and Davis KD. Hemispheric asymmetry in
white matter connectivity of the temporoparietal junction with the insula and prefrontal
cortex. PLoS One 2012: 7 (4): e35589.
Kumar K, Toth C, and Nath RK. Deep brain stimulation for intractable pain: a 15-year
experience. Neurosurgery 1997: 40 (4): 736-46; discussion 746-7.
Kunishio K, and Haber SN. Primate cingulostriatal projection: limbic striatal versus
sensorimotor striatal input. J Comp Neurol 1994: 350 (3): 337-56.
Kupers R, Faymonville ME, and Laureys S. The cognitive modulation of pain: hypnosis- and
placebo-induced analgesia. Prog Brain Res 2005: 150 251-69.
LaBuda CJ, and Fuchs PN. Attenuation of negative pain affect produced by unilateral spinal
nerve injury in the rat following anterior cingulate cortex activation. Neuroscience 2005:
136 (1): 311-22.
Lakos S, and Basbaum AI. An ultrastructural study of the projections from the midbrain
periaqueductal gray to spinally projecting, serotonin-immunoreactive neurons of the
medullary nucleus raphe magnus in the rat. Brain Res 1988: 443 (1-2): 383-8.
LaMotte RH, and Campbell JN. Comparison of responses of warm and nociceptive C-fiber
afferents in monkey with human judgments of thermal pain. J Neurophysiol 1978: 41 (2):
509-28.
Lane RD, Fink, G.R., Chau, P.M.L., Dolan, R.J. Neural activation during selective attention to
subjective emotional responses. NeuroReport 1997: 8 3969-3972.
Langner R, Kellermann T, Eickhoff SB, Boers F, Chatterjee A, Willmes K, and Sturm W.
Staying responsive to the world: modality-specific and -nonspecific contributions to
speeded auditory, tactile, and visual stimulus detection. Hum Brain Mapp 2012: 33 (2):
398-418.
Laxton A, Neimat J, Davis K, Womelsdor fT, Hutchison W, Dostrovsky J, Hamani C, Mayberg
H, and Lozano A. Neuronal Coding of Implicit Emotion Categories in the Subcallosal
Cortex in Patients with Depression. Biological Psychiatry 2013:
Le Bars D, Dickenson AH, and Besson JM. Diffuse noxious inhibitory controls (DNIC). I.
Effects on dorsal horn convergent neurones in the rat. Pain 1979a: 6 (3): 283-304.
Le Bars D, Dickenson AH, and Besson JM. Diffuse noxious inhibitory controls (DNIC). II. Lack
of effect on non-convergent neurones, supraspinal involvement and theoretical
implications. Pain 1979b: 6 (3): 305-27.
Le Bars D, and Willer JC. Higher pain tolerance thresholds. Pain 1988: 32 (2): 259-61.
Leone M, Franzini A, Broggi G, May A, and Bussone G. Long-term follow-up of bilateral
hypothalamic stimulation for intractable cluster headache. Brain 2004: 127 (Pt 10): 2259-
64.
Levy RM, Lamb S, and Adams JE. Treatment of chronic pain by deep brain stimulation: long
term follow-up and review of the literature. Neurosurgery 1987: 21 (6): 885-93.
100
Lewis VA, and Gebhart GF. Evaluation of the periaqueductal central gray (PAG) as a morphine-
specific locus of action and examination of morphine-induced and stimulation-produced
analgesia at coincident PAG loci. Brain Res 1977: 124 (2): 283-303.
Li YQ, Rao ZR, and Shi JW. Collateral projections from the midbrain periaqueductal gray to the
nucleus raphe magnus and nucleus accumbens in the rat. A fluorescent retrograde double-
labelling study. Neurosci Lett 1990: 117 (3): 285-8.
Linnman C, Beucke JC, Jensen KB, Gollub RL, and Kong J. Sex similarities and differences in
pain-related periaqueductal gray connectivity. Pain 2012: 153 (2): 444-54.
Liotti M, Mayberg HS, Brannan SK, McGinnis S, Jerabek P, and Fox PT. Differential limbic--
cortical correlates of sadness and anxiety in healthy subjects: implications for affective
disorders. Biol Psychiatry 2000: 48 (1): 30-42.
Liotti M, Mayberg HS, McGinnis S, Brannan SL, and Jerabek P. Unmasking disease-specific
cerebral blood flow abnormalities: mood challenge in patients with remitted unipolar
depression. Am J Psychiatry 2002: 159 (11): 1830-40.
Lovick TA. Integrated activity of cardiovascular and pain regulatory systems: role in adaptive
behavioural responses. Prog Neurobiol 1993: 40 (5): 631-44.
Lozano AM, Mayberg HS, Giacobbe P, Hamani C, Craddock RC, and Kennedy SH. Subcallosal
cingulate gyrus deep brain stimulation for treatment-resistant depression. Biol Psychiatry
2008: 64 (6): 461-7.
Luque JM, de Blas MR, Segovia S, and Guillamon A. Sexual dimorphism of the dopamine-beta-
hydroxylase-immunoreactive neurons in the rat locus ceruleus. Brain Res Dev Brain Res
1992: 67 (2): 211-5.
Mancia G, Baccelli G, and Zanchetti A. Hemodynamic responses to different emotional stimuli
in the cat: patterns and mechanisms. Am J Physiol 1972: 223 (4): 925-33.
Manning BH, Merin NM, Meng ID, and Amaral DG. Reduction in opioid- and cannabinoid-
induced antinociception in rhesus monkeys after bilateral lesions of the amygdaloid
complex. J Neurosci 2001: 21 (20): 8238-46.
Mansour A, Fox CA, Akil H, and Watson SJ. Opioid-receptor mRNA expression in the rat CNS:
anatomical and functional implications. Trends Neurosci 1995: 18 (1): 22-9.
Marcus DA. Interrelationships of neurochemicals, estrogen, and recurring headache. Pain 1995:
62 (2): 129-39.
Margulies DS, Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, and Milham MP. Mapping the
functional connectivity of anterior cingulate cortex. Neuroimage 2007: 37 (2): 579-88.
May A, Bahra A, Buchel C, Frackowiak RS, and Goadsby PJ. PET and MRA findings in cluster
headache and MRA in experimental pain. Neurology 2000: 55 (9): 1328-35.
Mayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA, Silva JA, Tekell JL,
Martin CC, Lancaster JL, and Fox PT. Reciprocal limbic-cortical function and negative
mood: converging PET findings in depression and normal sadness. Am J Psychiatry
1999: 156 (5): 675-82.
Mazars GJ. Intermittent stimulation of nucleus ventralis posterolateralis for intractable pain. Surg
Neurol 1975: 4 (1): 93-5.
McNeely HE, Mayberg HS, Lozano AM, and Kennedy SH. Neuropsychological impact of Cg25
deep brain stimulation for treatment-resistant depression: preliminary results over 12
months. J Nerv Ment Dis 2008: 196 (5): 405-10.
101
McRoberts JA, Li J, Ennes HS, and Mayer EA. Sex-dependent differences in the activity and
modulation of N-methyl-d-aspartic acid receptors in rat dorsal root ganglia neurons.
Neuroscience 2007: 148 (4): 1015-20.
Melzack R. Prolonged relief of pain by brief, intense transcutaneous somatic stimulation. Pain
1975: 1 (4): 357-73.
Melzack R, and Casey KL. Sensory, motivational, and central control determinants of pain: a
new conceptual model. In: D Kenshalo. The Skin Senses. Springfield: C.C. Thomas,
1968. pp. 423-439.
Melzack R, and Wall PD. The Challenge of Pain. Penguin Books 1996;
Menetrey D, Chaouch A, Binder D, and Besson JM. The origin of the spinomesencephalic tract
in the rat: an anatomical study using the retrograde transport of horseradish peroxidase. J
Comp Neurol 1982: 206 (2): 193-207.
Merskey H, and Bogduk N. Classification of Chronic Pain. IASP Press 1994;
Mesulam MM, and Mufson EJ. Insula of the old world monkey. III: Efferent cortical output and
comments on function. J Comp Neurol 1982: 212 (1): 38-52.
Meyer RA, Ringkamp M, Campbell JN, and Raja SN. Peripheral mechanisms of cutaneous
nociception. In: SB McMahon and M Koltzenburg. Wall and Melzack's Textbook of
Pain. UK: Elsevier Churchill Livingstone, 2006. pp. 219-221.
Meyerson BA, Lindblom U, Linderoth B, Lind G, and Herregodts P. Motor cortex stimulation as
treatment of trigeminal neuropathic pain. Acta Neurochir Suppl (Wien) 1993: 58 150-3.
Millan MH, Millan MJ, and Herz A. Depletion of central beta-endorphin blocks midbrain
stimulation produced analgesia in the freely-moving rat. Neuroscience 1986: 18 (3): 641-
9.
Millan MJ. Multiple opioid systems and pain. Pain 1986: 27 (3): 303-47.
Millan MJ. Descending control of pain. Prog Neurobiol 2002: 66 (6): 355-474.
Minami M, Maekawa K, Yabuuchi K, and Satoh M. Double in situ hybridization study on
coexistence of mu-, delta- and kappa-opioid receptor mRNAs with preprotachykinin A
mRNA in the rat dorsal root ganglia. Brain Res Mol Brain Res 1995: 30 (2): 203-10.
Mogil JS. Sex differences in pain and pain inhibition: multiple explanations of a controversial
phenomenon. Nat Rev Neurosci 2012: 13 (12): 859-66.
Morecraft RJ, and Tanji J. Cingulofrontal Interactions and the Cingulate Motor Areas. In: BA
Vogt. Cingulate Neurobiology and Disease. Oxford: Oxford University Press, 2009. pp.
113-144.
Morecraft RJ, and Van Hoesen GW. Convergence of limbic input to the cingulate motor cortex
in the rhesus monkey. Brain Res Bull 1998: 45 (2): 209-32.
Morgan MM, Whitney PK, and Gold MS. Immobility and flight associated with antinociception
produced by activation of the ventral and lateral/dorsal regions of the rat periaqueductal
gray. Brain Res 1998: 804 (1): 159-66.
Morton CR, and Duggan AW. Inhibition of spinal nociceptive transmission accompanies
cardiovascular changes from stimulation in diencephalic 'defence' regions of cats. Behav
Brain Res 1986: 21 (3): 183-8.
Morton CR, Maisch B, and Zimmermann M. Diffuse noxious inhibitory controls of lumbar
spinal neurons involve a supraspinal loop in the cat. Brain Res 1987: 410 (2): 347-52.
Moulton EA, Keaser ML, Gullapalli RP, Maitra R, and Greenspan JD. Sex differences in the
cerebral BOLD signal response to painful heat stimuli. Am J Physiol Regul Integr Comp
Physiol 2006: 291 (2): R257-67.
102
Mufson EJ, and Mesulam MM. Insula of the old world monkey. II: Afferent cortical input and
comments on the claustrum. J Comp Neurol 1982: 212 (1): 23-37.
Murphy K, Birn RM, Handwerker DA, Jones TB, and Bandettini PA. The impact of global
signal regression on resting state correlations: are anti-correlated networks introduced?
Neuroimage 2009: 44 (3): 893-905.
Nguyen JP, Keravel Y, Feve A, Uchiyama T, Cesaro P, Le Guerinel C, and Pollin B. Treatment
of deafferentation pain by chronic stimulation of the motor cortex: report of a series of 20
cases. Acta Neurochir Suppl 1997: 68 54-60.
Nieuwenhuys R, Voogd J, and Huijzen CV. The Human Central Nervous System. In: New York:
Springer, 2008. pp. 204.
Ongur D, An X, and Price JL. Prefrontal cortical projections to the hypothalamus in macaque
monkeys. J Comp Neurol 1998: 401 (4): 480-505.
Pandya DN, and Kuypers HG. Cortico-cortical connections in the rhesus monkey. Brain Res
1969: 13 (1): 13-36.
Pandya DN, Van Hoesen GW, and Mesulam MM. Efferent connections of the cingulate gyrus in
the rhesus monkey. Exp Brain Res 1981: 42 (3-4): 319-30.
Parrent AG, Lozano AM, Dostrovsky JO, and Tasker RR. Central pain in the absence of
functional sensory thalamus. Stereotact Funct Neurosurg 1992: 59 (1-4): 9-14.
Paulson PE, Minoshima S, Morrow TJ, and Casey KL. Gender differences in pain perception and
patterns of cerebral activation during noxious heat stimulation in humans. Pain 1998: 76
(1-2): 223-9.
Peng YB, Ringkamp M, Meyer RA, and Campbell JN. Fatigue and paradoxical enhancement of
heat response in C-fiber nociceptors from cross-modal excitation. J Neurosci 2003: 23
(11): 4766-74.
Peters ML, Schmidt AJ, and Van den Hout MA. Chronic low back pain and the reaction to
repeated acute pain stimulation. Pain 1989: 39 (1): 69-76.
Petrovic P, Kalso E, Petersson KM, and Ingvar M. Placebo and opioid analgesia-- imaging a
shared neuronal network. Science 2002: 295 (5560): 1737-40.
Plotkin R. Results in 60 cases of deep brain stimulation for chronic intractable pain. Appl
Neurophysiol 1982: 45 (1-2): 173-8.
Pomeroy SL, and Behbehani MM. Physiologic evidence for a projection from periaqueductal
gray to nucleus raphe magnus in the rat. Brain Res 1979: 176 (1): 143-7.
Porro CA, Baraldi P, Pagnoni G, Serafini M, Facchin P, Maieron M, and Nichelli P. Does
anticipation of pain affect cortical nociceptive systems? J Neurosci 2002: 22 (8): 3206-
14.
Porro CA, Cavazzuti M, Baraldi P, Giuliani D, Panerai AE, and Corazza R. CNS pattern of
metabolic activity during tonic pain: evidence for modulation by beta-endorphin. Eur J
Neurosci 1999: 11 (3): 874-88.
Prieto GJ, Cannon JT, and Liebeskind JC. N. raphe magnus lesions disrupt stimulation-produced
analgesia from ventral but not dorsal midbrain areas in the rat. Brain Res 1983: 261 (1):
53-7.
Proietti Cecchini A, Sandrini G, Fokin IV, Moglia A, and Nappi G. Trigeminofacial reflexes in
primary headaches. Cephalalgia 2003: 23 Suppl 1 33-41.
Proudfit HK, and Anderson EG. Morphine analgesia: blockade by raphe magnus lesions. Brain
Res 1975: 98 (3): 612-18.
103
Qiu E, Wang Y, Ma L, Tian L, Liu R, Dong Z, Xu X, Zou Z, and Yu S. Abnormal brain
functional connectivity of the hypothalamus in cluster headaches. PLoS One 2013: 8 (2):
e57896.
Racine M, Tousignant-Laflamme Y, Kloda LA, Dion D, Dupuis G, and Choiniere M. A
systematic literature review of 10 years of research on sex/gender and experimental pain
perception - part 1: are there really differences between women and men? Pain 2012a:
153 (3): 602-18.
Racine M, Tousignant-Laflamme Y, Kloda LA, Dion D, Dupuis G, and Choiniere M. A
systematic literature review of 10 years of research on sex/gender and pain perception -
part 2: do biopsychosocial factors alter pain sensitivity differently in women and men?
Pain 2012b: 153 (3): 619-35.
Ratka A, and Simpkins JW. A modulatory role for luteinizing hormone-releasing hormone in
nociceptive responses of female rats. Endocrinology 1990: 127 (2): 667-73.
Rausell E, and Jones EG. Chemically distinct compartments of the thalamic VPM nucleus in
monkeys relay principal and spinal trigeminal pathways to different layers of the
somatosensory cortex. J Neurosci 1991: 11 (1): 226-37.
Ray JP, and Price JL. The organization of projections from the mediodorsal nucleus of the
thalamus to orbital and medial prefrontal cortex in macaque monkeys. J Comp Neurol
1993: 337 (1): 1-31.
Reynolds DV. Surgery in the rat during electrical analgesia induced by focal brain stimulation.
Science 1969: 164 (3878): 444-5.
Richardson DE, and Akil H. Pain reduction by electrical brain stimulation in man. Part 1: Acute
administration in periaqueductal and periventricular sites. J Neurosurg 1977: 47 (2): 178-
83.
Roby-Brami A, Bussel B, Willer JC, and Le Bars D. An electrophysiological investigation into
the pain-relieving effects of heterotopic nociceptive stimuli. Probable involvement of a
supraspinal loop. Brain 1987: 110 ( Pt 6) 1497-508.
Room P, Russchen FT, Groenewegen HJ, and Lohman AH. Efferent connections of the
prelimbic (area 32) and the infralimbic (area 25) cortices: an anterograde tracing study in
the cat. J Comp Neurol 1985: 242 (1): 40-55.
Rubinstein M, Mogil JS, Japon M, Chan EC, Allen RG, and Low MJ. Absence of opioid stress-
induced analgesia in mice lacking beta-endorphin by site-directed mutagenesis. Proc Natl
Acad Sci U S A 1996: 93 (9): 3995-4000.
Samanin R, Gumulka W, and Valzelli L. Reduced effect of morphine in midbrain raphe lesioned
rats. Eur J Pharmacol 1970: 10 (3): 339-43.
Sanderson Nydahl K, Skinner K, Julius D, and Basbaum AI. Co-localization of endomorphin-2
and substance P in primary afferent nociceptors and effects of injury: a light and electron
microscopic study in the rat. Eur J Neurosci 2004: 19 (7): 1789-99.
Sandkuhler J, and Gebhart GF. Characterization of inhibition of a spinal nociceptive reflex by
stimulation medially and laterally in the midbrain and medulla in the pentobarbital-
anesthetized rat. Brain Res 1984a: 305 (1): 67-76.
Sandkuhler J, and Gebhart GF. Relative contributions of the nucleus raphe magnus and adjacent
medullary reticular formation to the inhibition by stimulation in the periaqueductal gray
of a spinal nociceptive reflex in the pentobarbital-anesthetized rat. Brain Res 1984b: 305
(1): 77-87.
104
Sandkuhler J, Helmchen C, Fu QG, and Zimmermann M. Inhibition of spinal nociceptive
neurons by excitation of cell bodies or fibers of passage at various brainstem sites in the
cat. Neurosci Lett 1988: 93 (1): 67-72.
Saur D, Kreher BW, Schnell S, Kummerer D, Kellmeyer P, Vry MS, Umarova R, Musso M,
Glauche V, Abel S, Huber W, Rijntjes M, Hennig J, and Weiller C. Ventral and dorsal
pathways for language. Proc Natl Acad Sci U S A 2008: 105 (46): 18035-40.
Schmahmann JD. Fiber pathways of the brain. Oxford University Press 2006;
Schmitt JA, Joyner MJ, Charkoudian N, Wallin BG, and Hart EC. Sex differences in alpha-
adrenergic support of blood pressure. Clin Auton Res 2010: 20 (4): 271-5.
Schreff M, Schulz S, Wiborny D, and Hollt V. Immunofluorescent identification of
endomorphin-2-containing nerve fibers and terminals in the rat brain and spinal cord.
Neuroreport 1998: 9 (6): 1031-4.
Serences JT, Shomstein S, Leber AB, Golay X, Egeth HE, and Yantis S. Coordination of
voluntary and stimulus-driven attentional control in human cortex. Psychol Sci 2005: 16
(2): 114-22.
Serrao M, Rossi P, Sandrini G, Parisi L, Amabile GA, Nappi G, and Pierelli F. Effects of diffuse
noxious inhibitory controls on temporal summation of the RIII reflex in humans. Pain
2004: 112 (3): 353-60.
Shah Y, and Dostrovsky JO. Electrophysiological evidence for a projection of the periaqueductal
gray matter to nucleus raphe magnus in cat and rat. Brain Res 1980: 193 (2): 534-8.
Siegfried J, Lazorthes Y, and Sedan R. Indications and ethical considerations of deep brain
stimulation. Acta Neurochir Suppl (Wien) 1980: 30 269-74.
Sikes RW, Vogt LJ, and Vogt BA. Distribution and properties of visceral nociceptive neurons in
rabbit cingulate cortex. Pain 2008: 135 (1-2): 160-74.
Sim LJ, and Joseph SA. Efferent projections of the nucleus raphe magnus. Brain Res Bull 1992:
28 (5): 679-82.
Simpson JR, Jr., Drevets WC, Snyder AZ, Gusnard DA, and Raichle ME. Emotion-induced
changes in human medial prefrontal cortex: II. During anticipatory anxiety. Proc Natl
Acad Sci U S A 2001: 98 (2): 688-93.
Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002: 17 (3): 143-55.
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H,
Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J,
Zhang Y, De Stefano N, Brady JM, and Matthews PM. Advances in functional and
structural MR image analysis and implementation as FSL. Neuroimage 2004: 23 Suppl 1
S208-19.
Stamford JA. Descending control of pain. Br J Anaesth 1995: 75 (2): 217-27.
Straube T, Schmidt S, Weiss T, Mentzel HJ, and Miltner WH. Sex differences in brain activation
to anticipated and experienced pain in the medial prefrontal cortex. Hum Brain Mapp
2009: 30 (2): 689-98.
Tasker RR, and Vilela Filho O. Deep brain stimulation for neuropathic pain. Stereotact Funct
Neurosurg 1995: 65 (1-4): 122-4.
Teutsch S, Herken W, Bingel U, Schoell E, and May A. Changes in brain gray matter due to
repetitive painful stimulation. Neuroimage 2008: 42 (2): 845-9.
Todd AJ, Spike RC, Russell G, and Johnston HM. Immunohistochemical evidence that Met-
enkephalin and GABA coexist in some neurones in rat dorsal horn. Brain Res 1992: 584
(1-2): 149-56.
105
Torta DM, and Cauda F. Different functions in the cingulate cortex, a meta-analytic connectivity
modeling study. Neuroimage 2011: 56 (4): 2157-72.
Treede RD, Kenshalo DR, Gracely RH, and Jones AK. The cortical representation of pain. Pain
1999: 79 (2-3): 105-11.
Tsou K, and Jang CS. Studies on the Site of Analgesic Action of Morphine by Intracerebral
Micro-Injection. Sci Sin 1964: 13 1099-109.
Tsubokawa T, Katayama Y, Yamamoto T, Hirayama T, and Koyama S. Chronic motor cortex
stimulation for the treatment of central pain. Acta Neurochir Suppl (Wien) 1991a: 52
137-9.
Tsubokawa T, Katayama Y, Yamamoto T, Hirayama T, and Koyama S. Treatment of thalamic
pain by chronic motor cortex stimulation. Pacing Clin Electrophysiol 1991b: 14 (1): 131-
4.
Turnbull IM, Shulman R, and Woodhurst WB. Thalamic stimulation for neuropathic pain. J
Neurosurg 1980: 52 (4): 486-93.
Umarova RM, Saur D, Schnell S, Kaller CP, Vry MS, Glauche V, Rijntjes M, Hennig J, Kiselev
V, and Weiller C. Structural connectivity for visuospatial attention: significance of
ventral pathways. Cereb Cortex 2010: 20 (1): 121-9.
Valeriani M, de Tommaso M, Restuccia D, Le Pera D, Guido M, Iannetti GD, Libro G, Truini A,
Di Trapani G, Puca F, Tonali P, and Cruccu G. Reduced habituation to experimental pain
in migraine patients: a CO(2) laser evoked potential study. Pain 2003: 105 (1-2): 57-64.
Van Bockstaele EJ, Aston-Jones G, Pieribone VA, Ennis M, and Shipley MT. Subregions of the
periaqueductal gray topographically innervate the rostral ventral medulla in the rat. J
Comp Neurol 1991: 309 (3): 305-27.
van den Heuvel MP, and Hulshoff Pol HE. Exploring the brain network: a review on resting-
state fMRI functional connectivity. Eur Neuropsychopharmacol 2010: 20 (8): 519-34.
Vetrugno R, Pierangeli G, Leone M, Bussone G, Franzini A, Brogli G, D'Angelo R, Cortelli P,
and Montagna P. Effect on sleep of posterior hypothalamus stimulation in cluster
headache. Headache 2007: 47 (7): 1085-90.
Vigouret J, Teschemacher H, Albus K, and Herz A. Differentiation between spinal and
supraspinal sites of action of morphine when inhibiting the hindleg flexor reflex in
rabbits. Neuropharmacology 1973: 12 (2): 111-21.
Vogt BA. Regions and Subregions of the Cingulate Cortex. In: BA Vogt. Cingulate
Neurobiology and Disease. Oxford: Oxford University Press, 2009. pp. 3-30.
Vogt BA, and Pandya DN. Cingulate cortex of the rhesus monkey: II. Cortical afferents. J Comp
Neurol 1987: 262 (2): 271-89.
Vogt BA, Pandya DN, and Rosene DL. Cingulate cortex of the rhesus monkey: I.
Cytoarchitecture and thalamic afferents. J Comp Neurol 1987: 262 (2): 256-70.
Vogt BA, and Vogt LJ. Mu-opioid Receptors, Placebo Map, Descending Systems, and
Cingulate-Mediated Control of Vocalization and Pain. In: BA Vogt. Cingulate
Neurobiology and Disease. Oxford: Oxford University Press, 2009. pp. 339-364.
Wiberg M, Westman J, and Blomqvist A. Somatosensory projection to the mesencephalon: an
anatomical study in the monkey. J Comp Neurol 1987: 264 (1): 92-117.
Wiinberg N, Hoegholm A, Christensen HR, Bang LE, Mikkelsen KL, Nielsen PE, Svendsen TL,
Kampmann JP, Madsen NH, and Bentzon MW. 24-h ambulatory blood pressure in 352
normal Danish subjects, related to age and gender. Am J Hypertens 1995: 8 (10 Pt 1):
978-86.
106
Woolf CJ. Pain: moving from symptom control toward mechanism-specific pharmacologic
management. Ann Intern Med 2004: 140 (6): 441-51.
Wu H, Hung K, Ohsawa M, Mizoguchi H, and Tseng LF. Antisera against endogenous opioids
increase the nocifensive response to formalin: demonstration of inhibitory beta-
endorphinergic control. Eur J Pharmacol 2001: 421 (1): 39-43.
Yaksh TL, Yeung JC, and Rudy TA. Systematic examination in the rat of brain sites sensitive to
the direct application of morphine: observation of differential effects within the
periaqueductal gray. Brain Res 1976: 114 (1): 83-103.
Yang J, Chen JM, Yang Y, Liu WY, Song CY, and Lin BC. Investigating the role of
hypothalamic paraventricular nucleus in nociception of the rat. Int J Neurosci 2008: 118
(4): 473-85.
Yang J, Yuan H, Chu J, Yang Y, Xu H, Wang G, Liu WY, and Lin BC. Arginine vasopressin
antinociception in the rat nucleus raphe magnus is involved in the endogenous opiate
peptide and serotonin system. Peptides 2009a: 30 (7): 1355-61.
Yang J, Yuan H, Liu W, Song C, Xu H, Wang G, Ni N, Yang D, and Lin B. Arginine
vasopressin in hypothalamic paraventricular nucleus is transferred to the nucleus raphe
magnus to participate in pain modulation. Peptides 2009b: 30 (9): 1679-82.
Yarnitsky D. Conditioned pain modulation (the diffuse noxious inhibitory control-like effect): its
relevance for acute and chronic pain states. Curr Opin Anaesthesiol 2010: 23 (5): 611-5.
Yarnitsky D, Arendt-Nielsen L, Bouhassira D, Edwards RR, Fillingim RB, Granot M, Hansson
P, Lautenbacher S, Marchand S, and Wilder-Smith O. Recommendations on terminology
and practice of psychophysical DNIC testing. Eur J Pain 2010: 14 (4): 339.
Yeung JC, Yaksh TL, and Rudy TA. Concurrent mapping of brain sites for sensitivity to the
direct application of morphine and focal electrical stimulation in the production of
antinociception in the rat. Pain 1977: 4 (1): 23-40.
Yezierski RP. Spinomesencephalic tract: projections from the lumbosacral spinal cord of the rat,
cat, and monkey. J Comp Neurol 1988: 267 (1): 131-46.
Young RF, Kroening R, Fulton W, Feldman RA, and Chambi I. Electrical stimulation of the
brain in treatment of chronic pain. Experience over 5 years. J Neurosurg 1985: 62 (3):
389-96.
Yu C, Zhou Y, Liu Y, Jiang T, Dong H, Zhang Y, and Walter M. Functional segregation of the
human cingulate cortex is confirmed by functional connectivity based neuroanatomical
parcellation. Neuroimage 2011: 54 (4): 2571-81.
Zacny JP. Morphine responses in humans: a retrospective analysis of sex differences. Drug
Alcohol Depend 2001: 63 (1): 23-8.
Zhang S, Ide JS, and Li CS. Resting-state functional connectivity of the medial superior frontal
cortex. Cereb Cortex 2012: 22 (1): 99-111.
Zhang S, Tang JS, Yuan B, and Jia H. Inhibitory effects of glutamate-induced activation of
thalamic nucleus submedius are mediated by ventrolateral orbital cortex and
periaqueductal gray in rats. Eur J Pain 1998: 2 (2): 153-163.
Zotev V, Krueger F, Phillips R, Alvarez RP, Simmons WK, Bellgowan P, Drevets WC, and
Bodurka J. Self-regulation of amygdala activation using real-time FMRI neurofeedback.
PLoS One 2011: 6 (9): e24522.
Zubieta JK, Bueller JA, Jackson LR, Scott DJ, Xu Y, Koeppe RA, Nichols TE, and Stohler CS.
Placebo effects mediated by endogenous opioid activity on mu-opioid receptors. J
Neurosci 2005: 25 (34): 7754-62.
107
Zubieta JK, Dannals RF, and Frost JJ. Gender and age influences on human brain mu-opioid
receptor binding measured by PET. Am J Psychiatry 1999: 156 (6): 842-8.
108
Appendices
109
Appendix I: Tractograms in both Women and Men
Figure A.I-1. Structural connectivity between the left subgenual anterior cingulate
and periaqueductal gray in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.
110
Figure A.I-2. Structural connectivity between the left subgenual anterior cingulate
and left hypothalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
111
Figure A.I-3. Structural connectivity between the left subgenual anterior cingulate
and right hypothalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
112
Figure A.I-4. Structural connectivity between the left subgenual anterior cingulate
and left amygdala in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right. sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
113
Figure A.I-5. Structural connectivity between the left subgenual anterior cingulate
and right amygdala in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
114
Figure A.I-6. Structural connectivity between the left subgenual anterior cingulate
and left anterior insula in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
115
Figure A.I-7. Structural connectivity between the left subgenual anterior cingulate
and right anterior insula in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
116
Figure A.I-8. Structural connectivity between the left subgenual anterior cingulate
and left lateral thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
117
Figure A.I-9. Structural connectivity between the left subgenual anterior cingulate
and left medial thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
118
Figure A.I-10. Structural connectivity between the left subgenual anterior cingulate
and right medial thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
119
Figure A.I-11. Structural connectivity between the left subgenual anterior cingulate
and right lateral thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
120
Figure A.I-12. Structural connectivity between the right subgenual anterior
cingulate and periaqueductal grey in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.
121
Figure A.I-13. Structural connectivity between the right subgenual anterior
cingulate and left hypothalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
122
Figure A.I-14. Structural connectivity between the right subgenual anterior
cingulate and right hypothalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
123
Figure A.I-15. Structural connectivity between the right subgenual anterior
cingulate and left amygdala in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
124
Figure A.I-16. Structural connectivity between the right subgenual anterior
cingulate and right amygdala in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
125
Figure A.I-17. Structural connectivity between the right subgenual anterior
cingulate and right anterior insula in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
126
Figure A.I-18. Structural connectivity between the right subgenual anterior
cingulate and left anterior insula in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
127
Figure A.I-19. Structural connectivity between the right subgenual anterior
cingulate and left lateral thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
128
Figure A.I-20. Structural connectivity between the right subgenual anterior
cingulate and left medial thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
129
Figure A.I-21. Structural connectivity between the right subgenual anterior
cingulate and right medial thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
130
Figure A.I-22. Structural connectivity between the right subgenual anterior
cingulate and right lateral thalamus in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
131
Figure A.I-23. Structural connectivity between the right subgenual anterior
cingulate (seed H) and left anterior midcingulate in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.
132
Figure A.I-24. Structural connectivity between the right subgenual anterior
cingulate (seed H) and right anterior midcingulate in all subjects
The absolute lower threshold for common connectivity was 50% or 39/77 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.
133
Appendix II: Tractograms in Women
Figure A.II-1. Structural connectivity between the left subgenual anterior cingulate
and periaqueductal grey in women. The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.
134
Figure A.II-2. Structural connectivity between the left subgenual anterior cingulate
and left hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
135
Figure A.II-3. Structural connectivity between the left subgenual anterior cingulate
and right hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
136
Figure A.II-4. Structural connectivity between the left subgenual anterior cingulate
and left amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
137
Figure A.II-5. Structural connectivity between the left subgenual anterior cingulate
and right amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
138
Figure A.II-6. Structural connectivity between the left subgenual anterior cingulate
and left anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
139
Figure A.II-7. Structural connectivity between the left subgenual anterior cingulate
and right anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
140
Figure A.II-8. Structural connectivity between the left subgenual anterior cingulate
and left lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
141
Figure A.II-9. Structural connectivity between the left subgenual anterior cingulate
and left medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
142
Figure A.II-10. Structural connectivity between the left subgenual anterior cingulate
and right medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
143
Figure A.II-11. Structural connectivity between the left subgenual anterior cingulate
and right lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
144
Figure A.II-12. Structural connectivity between the right subgenual anterior
cingulate and periaqueductal grey in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.
145
Figure A.II-13. Structural connectivity between the right subgenual anterior
cingulate and left hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
146
Figure A.II-14. Structural connectivity between the right subgenual anterior
cingulate and right hypothalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
147
Figure A.II-15. Structural connectivity between the right subgenual anterior
cingulate and left amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
148
Figure A.II-16. Structural connectivity between the right subgenual anterior
cingulate and right amygdala in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
149
Figure A.II-17. Structural connectivity between the right subgenual anterior
cingulate and right anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
150
Figure A.II-18. Structural connectivity between the right subgenual anterior
cingulate and left anterior insula in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
151
Figure A.II-19. Structural connectivity between the right subgenual anterior
cingulate and left lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
152
Figure A.II-20. Structural connectivity between the right subgenual anterior
cingulate and lateral medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
153
Figure A.II-21. Structural connectivity between the right subgenual anterior
cingulate and right medial thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
154
Figure A.II-22. Structural connectivity between the right subgenual anterior
cingulate and right lateral thalamus in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
155
Figure A.II-23. Structural connectivity between the right subgenual anterior
cingulate (seed H) and left anterior midcingulate in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.
156
Figure A.II-24. Structural connectivity between the right subgenual anterior
cingulate (seed H) and right anterior midcingulate in women The absolute lower threshold for common connectivity was 50% or 19/38 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.
157
Appendix III: Tractograms in Men
Figure A.III-1. Structural connectivity between the left subgenual anterior cingulate
in men.
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.
158
Figure A.III-2. Structural connectivity between the left subgenual anterior cingulate
and left hypothalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
159
Figure A.III-3. Structural connectivity between the left subgenual anterior cingulate
and right hypothalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
160
Figure A.III-4. Structural connectivity between the left subgenual anterior cingulate
and left amygdala in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
161
Figure A.III-5. Structural connectivity between the left subgenual anterior cingulate
and right amygdala in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
162
Figure A.III-6. Structural connectivity between the left subgenual anterior cingulate
and left anterior insula in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
163
Figure A.III-7. Structural connectivity between the left subgenual anterior cingulate
and right anterior insula in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
164
Figure A.III-8. Structural connectivity between the left subgenual anterior cingulate
and left lateral thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
165
Figure A.III-9. Structural connectivity between the left subgenual anterior cingulate
and left medial thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
166
Figure A.III-10. Structural connectivity between the left subgenual anterior
cingulate and right medial thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
167
Figure A.III-11. Structural connectivity between the left subgenual anterior
cingulate and right lateral thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
168
Figure A.III-12. Structural connectivity between the right subgenual anterior
cingulate and periaqueductal grey in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; PAG: periaqueductal gray.
169
Figure A.III-13. Structural connectivity between the right subgenual anterior
cingulate and left hypothalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
170
Figure A.III-14. Structural connectivity between the right subgenual anterior
cingulate and right hypothalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; Hy: hypothalamus.
171
Figure A.III-15. Structural connectivity between the right subgenual anterior
cingulate and left amygdala in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
172
Figure A.III-16. Structural connectivity between the right subgenual anterior
cingulate and right amygdala in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Amy: amygdala.
173
Figure A.III-17. Structural connectivity between the right subgenual anterior
cingulate and right anterior insula in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
174
Figure A.III-18. Structural connectivity between the right subgenual anterior
cingulate and left anterior insula in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; aINS: anterior insula.
175
Figure A.III-19. Structural connectivity between the right subgenual anterior
cingulate and left lateral thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
176
Figure A.III-20. Structural connectivity between the right subgenual anterior
cingulate and left medial thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
177
Figure A.III-21. Structural connectivity between the right subgenual anterior
cingulate and right medial thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
178
Figure A.III-22. Structural connectivity between the right subgenual anterior
cingulate and right lateral thalamus in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map. L:
left; R: right; sgACC: subgenual anterior cingulate cortex; Th: thalamus.
179
Figure A.III-23. Structural connectivity between the subgenual anterior cingulate
(seed H) and left anterior midcingulate in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.
180
Figure A.III-24. Structural connectivity between the subgenual anterior cingulate
(seed H) and right anterior midcingulate in men
The absolute lower threshold for common connectivity was 50% or 20/39 subjects. This
threshold was sometimes increased to display tracts more clearly. Tractograms are
projected to the single subject brain template (T1) provided in the MatLab software -
xjView. The colour flare represents the number of subjects that contributed to the map.
sgACC: subgenual anterior cingulate cortex; aMCC: anterior midcingulate.