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Fear conditioning in frontotemporal lobardegeneration and Alzheimer’s disease
M. Hoefer,1,2 S. C. Allison,1,2 G. F. Schauer,1,2 J. M. Neuhaus,3 J. Hall,1,2 J. N. Dang,4 M. W. Weiner,1,5,6
B. L. Miller1,2 and H. J. Rosen1,2
1University of California at San Francisco Department of Neurology, 2USCF Memory and Aging Center, 3UCSF Department
of Epidemiology and Biostatistics, 4UCSF School of Pharmacy, 5San Francisco Veterans Affairs Hospital Magnetic Resonance
Imaging Unit and 6UCSF Department of Radiology, San Francisco, CA, USA
Correspondence to: Howard J. Rosen, UCSF Department of Neurology, Memory and Aging Center, 350 Parnassus Ave.,
Box 1207, Suite 706, San Francisco, CA 94143-1207, USA
E-mail: [email protected]
Emotional blunting and abnormal processing of rewards and punishments represent early features of fronto-
temporal lobar degeneration (FTLD). Better understanding of the physiological underpinnings of these
emotional changes can be facilitated by the use of classical psychology approaches. Fear conditioning (FC) isan extensively used paradigm for studying emotional processing that has rarely been applied to the study of
dementia.We studied FC in controls (n= 25), Alzheimer’s disease (n= 25) and FTLD (n= 25). A neutral stimulus
(coloured square on a computer screen) was repeatedly paired with a 1s burst of 100 db white noise. Change in
skin conductance response to the neutral stimulus was used to measure conditioning. Physiological^anatomical
correlations were examined using voxel-based morphometry (VBM). Both patient groups showed impaired
acquisition of conditioned responses. However, the basis for this deficit appeared to differ between groups.
In Alzheimer’s disease, impaired FC occurred despite normal electrodermal responses to the aversive stimulus.
In contrast, FTLD patients showed reduced skin conductance responses to the aversive stimulus, which contrib-
uted significantly to their FC deficit. VBM identified correlations with physiological reactivity in the amygdala,
anterior cingulate cortex, orbitofrontal cortex and insula. These data indicate that Alzheimer’s disease and
FTLD both show abnormalities in emotional learning, but they suggest that in FTLD this is associated with a
deficit in basic electrodermal response to aversive stimuli, consistent with the emotional blunting described
with this disorder. Deficits in responses to aversive stimuli could contribute to both the behavioural and cognitivefeatures of FTLD and Alzheimer’s disease. Further study of FC in humans and animal models of dementia could
provide a valuable window into these symptoms.
Keywords: frontotemporal lobar degeneration; Alzheimer’s disease; emotion; fear conditioning
Abbreviations: ACC = anterior cingulate cortex; CS = conditioned stimulus; FC = fear conditioning; FTD = frontotemporal
dementia; F TLD= frontotemporal lobar degeneration; GDS= Geriatric Depression Scale; OFC = orbitofrontal cortex;
ROI = regions of interest; SCL = skin conductance level; SCR = skin conductance response; SD= semantic dementia;
US = unconditioned stimulus; VBM = voxel-based morphometry
Received September 22, 2007. Revised March 4, 2008. Accepted April 11, 2008. Advance Access publication May 20, 2008
Introduction
Emotional problems are common in neurodegenerative
disease, with symptoms ranging from depression, apathy
and emotional blunting to anxiety, irritability and agitation
(Cummings, 1997). In some disorders, such as frontotem-
poral dementia (FTD), social and emotional problems repre-
sent early signs and core features of the illness for diagnosis
(Neary et al ., 1998; McKhann et al ., 2001). The diagnostic
implications of emotional problems in neurodegenerative
disease and their contribution to morbidity underscore the
need for a deeper understanding of their origins.Improved understanding of emotional dysfunction in
dementia may come from the study of processes whose
neurophysiological substrates are well-understood in ani-
mals, such as simple conditioning paradigms. The advan-
tages of conditioning are two-fold. Unlike verbally mediated
doi:10.1093/brain/awn082 Brain (2008), 131, 1646 ^1657
The Author (2008).Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected]
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neuropsychological testing, conditioning can be easily applied to animal models of dementia. Second, if abnormalin humans, conditioning paradigms can be studied andunderstood in animals at a physiological and molecular level.
Fear conditioning (FC) is one of the most extensively used models for studying emotional processing in animals.
This paradigm, in which neutral stimuli are made to elicitfear responses through repetitive pairing with inherently aversive stimuli, represents an elemental process by whichthe brain’s emotional systems learn to anticipate threats inthe environment. Successful FC is dependent on a discreteset of frontal and temporal regions, including the amygdala,insula and ventromedial pre-frontal cortex, and diencepha-lic and brainstem structures that mediate physiological andbehavioural responses (LeDoux, 1992). Several fMRI andlesion studies have demonstrated that FC in humans isdependent on these same structures (Bechara et al ., 1995,1999; LaBar et al ., 1995, 1998; Buchel and Dolan, 2000;Phelps et al ., 2004). Although FC has been proposed as a
model for understanding primary psychiatric disorders,particularly anxiety (Phillips et al ., 2003b; Rauch et al .,2006), it has received little attention as means of studyingdementia. Yet, the key FC structures are commonly affectedin neurodegenerative disease. Frontotemporal lobar degen-eration (FTLD) is associated with amygdala, insula andorbitofrontal tissue loss (Rosen et al ., 2002; Liu et al ., 2004;Boccardi et al ., 2005). In Alzheimer’s disease, the amygdalais also a site of early involvement after the hippocampusand entorhinal cortex (Braak and Braak, 1991) andamygdala volumes are accordingly reduced (Barnes et al .,2006; Basso et al ., 2006; Markesbery et al ., 2006).
FTLD and Alzheimer’s disease differ clinically, withemotional blunting, loss of empathy, impaired socialinteractions and compulsive behaviours being early featuresin FTLD, while interpersonal behaviour is relatively wellpreserved in Alzheimer’s disease, although irritability andagitation often develop (Cummings, 1997; Liu et al ., 2004).These clinical differences suggest different abnormalities inthe emotional system, which might be revealed throughdirect assessment of emotional processing.
FC depends on a number of component processes,including auditory and visual processing of incomingstimuli, and basic physiological reactivity to aversivestimuli-functions known to be altered in aging (Labar
et al ., 2004). One prior study indicated that FC is impairedin Alzheimer’s disease patients who had no deficit inelectrodermal response to aversive sounds (Hamann et al .,2002), but no studies have examined conditioning in FTLD.The emotional blunting in FTLD suggests that physiologicalreactivity to aversive stimuli might be altered in thesepatients. This would impact FC, and could also haveimplications for emotional processing in general becausealtered processing of aversive stimuli may change one’sbehaviour towards them. The goal of the current study wasto compare the effects of FTLD and Alzheimer’s disease onFC, taking into account the basic processing underlying FC.
Two variants of FTLD were included: FTD and semanticdementia (SD). Both of these variants are associated withsignificant behavioural abnormalities and tissue loss in
orbitofrontal cortex (OFC), cingulate and insular cortex (Bozeat et al ., 2000; Snowden et al ., 2001; Liu et al ., 2004;
Rosen et al ., 2006).
MethodsParticipantsSeventy-five consecutively recruited individuals, including 25
patients with probable Alzheimer’s disease [13 men, 12 women,
mean age= 61.8 years (range = 50–77)], 25 with FTLD [17 men,
8 women, mean age = 63 years (range= 49–83)] and 25 healthy
older individuals [normal controls, 10 men, 15 women, mean
age = 66.8 years (range= 51–79)] participated. There were no
significant differences across groups for age or sex (Table 1).
Patients were diagnosed using published criteria (McKhann
et al ., 1984; Neary et al ., 1998) after a comprehensive evaluation at
the UCSF Memory and Aging Center including neurological
history and examination, nursing evaluation, laboratory evaluation
and neuropsychological assessment of memory, executive function,
language and mood (see subsequently). Twenty-one of the normal
controls underwent the same evaluation, and the other five were
spouses of patients or other individuals who did not undergo
formal evaluation but had no cognitive complaints and no history
of neurological or psychiatric disease.
FTLD is comprised of three clinical syndromes: FTD, SD and
progressive non-fluent aphasia (Neary et al ., 1998). For this ex-
periment, patients with FTD (n = 15) and SD (n = 10) were included
because they also share many social/emotional behavioural abnorm-
alities (Bozeat et al ., 2000; Snowden et al ., 2001; Liu et al ., 2004)
whereas progressive non-fluent aphasia patients show a less
prominent behavioural disturbance (Rosen et al ., 2006).
Briefly, the neuropsychological evaluation consists of the Mini-
Mental State Examination (Folstein et al ., 1975), and tests of
working memory (digit span backwards), verbal episodic memory
[California Verbal Learning Test (Delis et al ., 2000)], visual
episodic memory (memory for details of a modified Rey-
Osterrieth figure), visual-spatial function (copy of a modified
Rey-Osterrieth figure), confrontational naming [15 items from the
Boston Naming Test (Kaplan et al ., 1983)], phonemic (words
beginning with the letter ‘D’), semantic (animals) and non-verbal
fluency [novel designs (Delis et al ., 2001)] and visual-motor
sequencing [a modified version of the ‘Trails B’ test (Reitan,
1958)]. Other variables of interest collected at our clinical visits
were the Clinical Dementia Rating Scale score (Morris, 1997) that
measures functional impairment and is often used as a surrogateof disease severity, the Geriatric Depression Scale [GDS (Yesavage
et al ., 1983)] and the Neuropsychiatric Inventory, which assesses
behavioural dysfunction (Cummings, 1997).
The research protocol was approved by the UCSF committee on
human research and all subjects gave informed consent before
participating.
Psychophysiologic testing
OverviewDuring the FC paradigm, two alternating neutral stimuli (coloured
squares on a computer monitor) were repetitively presented every
Conditioning in FTLD and Alzheimer’s disease Brain (2008), 131, 1646^1657 1647
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16s over an 15-min experiment. The experiment proceeded in
three phases: habituation, where each of the stimuli were initially
presented four times, acquisition, where one of the neutral stimuli
(the conditioned stimulus, or CS+), was immediately followed by
an aversive sound presented through headphones (the uncondi-
tioned stimulus, or US) and the other (the CS) was not and
extinction, where the CS+ and CS were again repetitively
presented unaccompanied by the US. Figure 1 illustrates the
organization of the experiment and the creation of variables for
analysis.
StimuliThe CS+ and CS were usually blue and orange rectangles,
of equal luminance that occupied the entire computer screen
for 2 s. For a few subjects, green and red stimuli were used because
these subjects were also participating in a related experiment.
The colours used for the CS+ and CS were counterbalan-
ced across participants (within diagnostic group). The USwas a 1-s burst of 100 db white noise presented through
headphones.
Fig. 1 Graphic illustration of the experiment and creation of physiological variables. SCRdiff hab, SCRdiff ecq and SCRdiff ext are graphed inFig. 2. Note that no trials with a US are used to examine conditioning. Responses to trials containing a US are analysed separately andgraphed in Fig. 3 (for each presentation prior to and during the experiment) and Fig. 4 (averaged across the experiment).
Table 1 Demographics and neuropsychological test results in controls, Alzheimer’s disease, FTD and SD
Overall ANOVA Controls Mean (SD) AD Mean (SD) FTD Mean (SD) SD Mean (SD)
Age F (3, 73) = 1.51 66.7 (8.6) 62.0 (9.2) 62.3 (8.7) 64.6 (7.9)Males/females NS (2) 10/15 13/12 11/4 6/4Clinical Dementia Rating Scale (sum of boxes) F (3, 56) = 23.2 0.04 (0.1) 6.9 (3.0) ¥ 7.7 (3.9) ¥ 4.9 (1.8) ¥
Mini-Mental State Examination (max= 30) F (3, 73) = 11.1 29.7 (0.5) 21.4 (6.4) ¥ 20.3 (9.6) ¥ 22.1 (6.0) ¥
CVLT 100 free recall (max= 9) F (3, 43) = 11.6 7.3 (1.0) 0.8 (1.2) ¥ ,‰ 3.5 (2.8) ¥ 2.0 (2.9) ¥
Mod. Rey-O Delay (max = 17) F (3, 52) = 13.1 13.0(3.1) 3.0 (4.2) ¥ 7.5 (5.9) ¥ 6.2 (6.2) ¥
Digit span backwards F (3, 56) = 5.6
5.2 (1.5) 3.0 (1.3)
¥
3.9 (2.2) 4.3 (1.4)Modified trails #lines/min F (3, 50)=4.25 28.8 (7.8) 9.5 (14.8) ¥ 16.6 (19.7) 18.2 (11.5)Stroop # correct/min F (3, 44) = 14.9 48.2 (16.3) 12.6 (11.2) ¥ ,‰ 32.9 (16.4) 30.0 (15.4) ¥
Phonemic fluency F (3, 54) = 19.4 16.6 (3.2) 8.1 (5.0) ¥ 7.2 (3.6) ¥ 4.8 (4.2) ¥
Semantic fluency F (3, 54) = 47.8 24.1 (5.7) 7.2 (3.6) ¥ 11.0 (5.8) ¥ 4.3 (4.0) ¥ ,‰
Abbreviated BNT (max = 15) F (3, 54) = 23.0 14.5 (3.2) 11.9 (3.6) 10.0 (5.0) ¥ 3.0 (2.4) ¥ ,y,‰
Mod. Rey-O copy (max = 17) F (3, 54) = 8.4 16.1(1.3) 9.6 (6.6) ¥ ,‰ 13.8 (3.5) 15.8 (0.9)y
Calculations (max = 5) F (3, 56) = 5.4 4.6 (0.6) 2.7 (0.8) ¥ 3.5 (1.5) 3.8 (1.5)GDS (max = 30, lower better) F (3, 48)=2.9 3.3 (2.9) 6.1 (4.8) 3.1 (2.7) 6.9 (4.3)
SD= standard deviation, NS= not signficant. P50.05 across groups (ANOVA). ¥ P50.05 versus Controls (post hoc usingTukey HSD). yP50.05versus Alzheimer’s disease (post hoc using Tukey HSD). ‰P50.05 versus FvFTD (post hoc using Tukey HSD).
1648 Brain (2008), 131, 1646 ^1657 M. Hoefer et al.
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ApparatusVisual stimuli were presented on a 17in. CRT computer monitor
attached to a custom-built PC running the STIM software package
(James Long Co., Caroga Lake, NY, www.jameslong.com).
Acoustic stimuli were presented via Telephonics high-impedance
headphones (Farmingdale, NY).
Skin conductance level (SCL) was measured in microSiemens(mS) by attaching two 1081 FG-DIN Ag/AgCl sensors prepared
with Biogel electrode gel (UFI inc., Morro Bay, CA) to the ventral
surface of the middle phalanges on the middle and index fingers
of the participant’s non-dominant hand. The sensors were
connected to an SAI bioamplifier (0.5V constant voltage with a
sensitivity of 600 pS, SAI Inc., Hauppauge, NY), which fed the
signal to a DI-720-P data acquisition unit (DataQ, Akron, OH),
and the digitized data were transmitted to a laptop computer
running Snapmaster software (version 3.5.8, HEM Data
Corporation, Southfield, MI). The physiological software acquired
skin conductance data on two channels: one with the SCL and the
other with SCR. SCL is derived by applying excitation voltage to
the sensors attached to the hand. The resultant current flow is
presented, after root-mean square conversion and signal con-
ditioning, as a DC conductance level. SCL bandpass is 10 Hz, and
range is 0–25 mS. SCR is derived by sampling the SCL channel and
directing it through high gain signal conditioning circuits, which
also remove the DC signal, resulting in a magnified display of skin
conductance change. The bandpass is 0.01–10 Hz, and the range
was 2.5 to +2.5 mS.
Experimental procedureAfter consent, subjects washed their hands and had the sensors
attached. They were then seated about 50 cm from the computer
monitor and instructed to breathe normally and to refrain from
moving or talking while a 60 s baseline reading of SCL/SCR was
taken.Participants were then presented with the conditioned stimuli
and asked to name the colours, as a test of whether they could
appreciate differences between them. Headphones were then
placed over the subject’s ears and a hearing test was administered
using a series of 1 and 2 kHz tones. Participants were asked to
indicate the onset and offset of the tones by raising and lowering
their hands. The hearing screening revealed several patients (two
controls, three FTLD and one Alzheimer’s disease) with some
hearing difficulties (unilateral hearing loss or bilateral high
frequency hearing loss). Inspection of the data in these patients
did not suggest that the hearing problems impacted their
reactivity, and all patients reported hearing the aversive sound
loudly and clearly. Hearing problems are a normal accompani-
ment of aging, and not specifically associated with any of the
disorders studied here. Thus, in order to increase the general-
izeability of the results, these subjects were included in our
analyses, with the presence of these abnormalities entered as a
covariate.
Next, the subject was given a warned presentation of the US
without concomitant presentation of the CS+ in order to acquaint
them with the stimulus. After this presentation, the subject was
given an opportunity to discontinue the experiment if the US was
deemed too aversive (no participants discontinued at this point).
Participants were then informed that they would see a series of
colours and occasionally hear the loud sound. They were
instructed to press a button each time they saw a colour (this
was done to ensure that they were attending to the stimulus).
During the conditioning paradigm, stimuli were presented in
pseudorandom order to diminish anticipatory confounds. During
inter-trial intervals the computer screen was dark. During
habituation, the subject was presented with four CS+ trials and
four CS trials without presentation of the US. Acquisition was
broken into two parts: (i) constant acquisition, where the subject
was presented with four CS+ trials each paired with the US and
four CS trials and (ii) variable acquisition, where the subject was
presented with 6 CS+/US trials, 16 CS trials and 8 CS+ trials
without a paired US. The variable acquisition phase allowed us to
measure the SCR to the CS+ after learning, but without the
confounding effect of the US being presented immediately after
the CS+ and in a phase where the US/CS+ association was still
being intermittently reinforced. During extinction the subject was
presented with 16 unpaired CS+ and 16 CS trials.
At the end of the experiment, a second 1 min 60 s assessment of
resting SCL/SCR was taken.
Image acquisitionT1-weighted images were available in 53 of the 75 subjects
(17 controls, 21 FTLD, 16 Alzheimer’s disease). Structural MR
imaging was accomplished using a 1.5-T Magnetom VISION
system (Siemens Inc., Iselin, NJ), a standard quadrature head coil
and previously described sequences (Rosen et al ., 2002) to obtain
(i) scout views of the brain for positioning subsequent MRI slices,
(ii) proton density and T2-weighted MRIs and (iii) T1-weighted
(MP-RAGE) images of the entire brain. MP-RAGE images were
used for analysis.
AnalysisFor all analyses except baseline assessment of reactivity (see
subsequently), we used the maximum SCR during the period
running from 0.5 s to 4.5 s after stimulus offset.
Conditioning and extinctionConditioning was represented as the change in maximum SCR to
the CS+, as compared with the CS, after acquisition. Relative
response to the CS+ was measured by taking the mean maximum
SCR to the CS for all trials representing each phase and
subtracting this from the mean maximum SCR to the CS+ across
targeted trials for the same phase. If conditioning occurs, this
difference (SCRdiff) should be zero for habituation, but positive
after acquisition. In order to create one number representing
conditioning, this SCRdiff in habituation was subtracted from the
SCRdiff after acquisition. Target trials were as follows: all trialsduring habituation, and those trials occurring in the latter half of
acquisition that were not followed by the US (variable acquisition
phase). A single value for extinction was also created by
subtracting the SCRdiff in the latter half of extinction from the
SCRdiff in the latter half of acquisition.
Physiological response to USThe SCR response to the US was examined for each of the 10
times it was presented during acquisition. We used the average
response across all these trials as a covariate for some statistical
analysis.
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Baseline assessment of reactivityVarious factors, including skin thickness, aging and medications
might affect the ability to measure electrodermal reactivity. To assess
this, we examined the maximum SCR during the 60 s rest periods
that occurred at the beginning or end of the session. During these
rest periods, the experimenter monitored participants’ activity and
noted excessive subject movement or talking. Problems with theinitial rest period were noted in 6 of the 75 subjects (four FTLD, two
Alzheimer’s disease). In three of these, the rest period at the end of
the experiment was considered valid, and was used instead. In the
other three (two FTLD and one Alzheimer’s disease), both rest
periods had problems. For these subjects, the first rest period was
used. Baseline reactivity in Alzheimer’s disease appeared to be higher
than in controls and FTLD, which had similar baseline reactivity, but
the difference was not significant across groups [one way ANOVA;
F (2, 72) = 1.28, P = 0.29]. Baseline SCL also did not differ across
groups. Baseline reactivity (SCR) was included as a covariate in all
statistical analyses.
MedicationsSCR is a sympathetic response mediated by cholinergic fibres in
the periphery. While there is not extensive data examining the
effects of medications on FC, the bulk of the available literature
indicates that agents with substantial anticholinergic effects can
alter FC, but there is little evidence that agents with effects on the
sympathetic nervous system have much impact (Fryer and Lukas,
1999; Mueck-Weymann et al ., 2001; Grillon et al ., 2004; Siepmann
et al ., 2004; Praharaj and Arora, 2006). To address this issue, we
reviewed participants’ medication lists and identified agents likely
to have effects on SCR or conditioning. Targets included
antipsychotics (both first and second generation), selective
serotonin reuptake inhibitors (including fluoxetine, paroxitine,
citalopram and escitalopram, sertraline, duloxetine), other anti-
depressants (tricyclics, trazadone, venlafaxine), antihistamines andbenzodiazepines [which have been shown to affect FC (Brignell
and Curran, 2006)]. Most patients were on one of these agents.
A few were on two or three. Patients were coded dichotomously as
to whether they were, or were not on these agents.
Cholinesterase inhibitors are a standard of care for patients with
Alzheimer’s disease and are sometimes prescribed in other
dementia syndromes. Because they enhance cholinergic function,
they could conceivably enhance SCRs. Thus, we also coded each
patient according to whether they were on such agents. The
presence or absence of anticholinergic medications and cholines-
terase inhibitors were included in all statistical analyses.
Correlations between physiology and other clinicalfeaturesAbnormalities in conditioning or physiologic responding could be
mediated by other cognitive or clinical factors, including severity
of disease. To explore this, we conducted bivariate regression
analyses between physiological variables and the Clinical Dementia
Rating Scale, Neuropsychiatric Inventory and GDS, and all the
neuropsychological variables described earlier.
Voxel-based morphometry (VBM)VBM is a technique that allows voxel-wise analysis of the
relationship between changes in brain tissue content and
independent variables of interest (Ashburner and Friston, 2000).
A detailed description of the VBM pre-processing and analysis
steps used in this study has been published (Rosen et al ., 2005).
Briefly, our approach included the use of a study specific template
composed of the average of all study participants (Good et al .,
2001; Testa et al ., 2004), optimized spatial normalization of grey
matter images (Good et al ., 2002) and multiplication of the gray
matter images by the Jacobian determinants used in normalization
to preserve the original volume. Normalized gray matter images
were smoothed using a 12mm full width at half-maximum
isotropic Gaussian kernel.
VBM analyses were constructed to compare tissue content
across groups before examining the correlates of the physiological
variables. Physiological correlates were examined by entering the
physiological variables into ‘covariates only’ design matrices to
examine their anatomical correlates. The data were then
reanalysed using ‘conditions and covariates’ matrices to look for
group-specific effects. Age, sex and total intracranial volume,
calculated as the sum of the modulated gray, white and CSF
volumes, used to control for head size) were always used as
covariates.Statistical significance was evaluated at P 50.05 corrected for
multiple comparisons using family-wise error correction (Friston
et al ., 1996). The human and animal literature indicates that
particular aspects of FC are linked to specific brain regions,
providing a basis for selection of regions of interest (ROI) to limit
the number of multiple comparisons. Acquisition is most closely
associated with the amygdala (Bechara et al ., 1995, 1999; LaBar
et al ., 1995, 1998; Buchel and Dolan, 2000). Extinction is
associated with ventromedial frontal cortex (Phelps et al ., 2004;
Milad et al ., 2005; Quirk and Beer, 2006) and related processes of
reversal learning with posterior OFC (Kringelbach and Rolls,
2004). More generally, emotional processing is linked with a
diverse set of frontal and temporal regions including the anterior
and mid-cingulate regions, medial superior frontal gyrus, ventro-medial frontal and OFC and amygdala (Phan et al ., 2002; Phillips
et al ., 2003a). These regions were used as the ROI for reactivity to
the US, as the literature does not provide a basis for constraining
the ROI further. ROIs were constructed using WFU Pickatlas
software (version 2.0 for Linux, Wake Forest University, Raleigh,
NC), (Maldjian et al ., 2003) and the AAL brain atlas (Tzourio-
Mazoyer et al ., 2002). For each physiological function, potential
effects within the appropriate ROI were explored down to a voxel-
level P -value of 0.01. All VBM statistical analyses were conducted
using SPM (version 2, University College, London, UK) operating in
a MATLAB environment (version 7.1, Mathworks, Inc.,Natick, MA).
Statistical analysesOur chief aim was to see how FC is affected in each of the clinical
dementia syndromes. We examined this with regression analysis
using our conditioning variable (see Conditioning and extinction
in the analysis section above) as the dependent variable and
diagnosis (control, Alzheimer’s disease or FTLD) and other
covariates as independent variables. Regressions were conducted
in two blocks, with all covariates entered in the first block and
diagnosis in the second block. If the contribution of diagnosis was
significant, as indicated by a P 50.05 for the R2 after adding
diagnosis into the model, differences between each patient group
and controls were evaluated at P 50.05 threshold using a Fischer’s
least significant difference approach (Miller, 1981).
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We also compared the basic reactivity to the US across groups.
Because the US was delivered 10 times during the experiment,
each subject contributes multiple SCR measurements representing
reactivity to the US. We analysed these repeated measures using
linear mixed effects models (McCulloch and Searle, 2001).
Examination of the correlations between individual US responses
across time did not suggest that these responses departed from the
assumption of compound symmetry.
Because of the known effects of age on FC (Labar et al ., 2004),
age was included in all of the analyses in addition to baseline
physiological reactivity, medication use and the presence or
absence of mild hearing impairment. Statistical analyses were
performed using SPSS version 14 (SPSS inc., http://www.
spss.com).
ResultsFC
Figure 2 shows that the SCRdiff for CS+ versus CSincreased for controls in the acquisition phase, consistent
with conditioning. In contrast, there is no evidence of learning in FTLD or Alzheimer’s disease. The overall effect
for diagnosis was significant (P = 0.01), and pairwisecomparisons were significant for FTLD versus controls
(B =0.042, P 50.01) and for Alzheimer’s disease versuscontrols (B =0.049, P 50.01).
Reactivity to the US
We examined the response to the US across the experimentto assess whether this might be abnormal in either patient
group. Linear mixed effects analysis revealed a significanteffect for group overall (P 50.04). Pairwise comparisons
were significant for FTLD versus control (mean difference0.09 mS, 95% CI 0.02–0.16, P 50.02) but not for Alzheimer’sdisease versus controls (mean difference 0.06mS, 95%CI 0.13 to 0.12, P 50.45). The difference between
FTLD and Alzheimer’s disease approached significance
(mean difference 0.09 mS, 95% CI 0.01 to 0.13, P 50.09).Response to the US is depicted in Fig. 3, which
also includes the first presentation of the US priorto the experiment. This first response was higher
than subsequent responses in all groups, but still lowest
in FTLD.We also analysed the reactivity to the US in terms of how
many participants had significant SCRs in response to theUS. Participants were scored as having a significant SCR if any of their responses were 40.5mS, a commonly used
threshold for SCRs (Ohman et al ., 2000). Using thisthreshold, 40% of the controls and 48% of the Alzheimer’s
disease patients had significant SCRs in response to the US,
but only 12% of the FTLD subjects had significant SCRs(2 P 50.05 overall, and for FTLD versus controls andFTLD versus Alzheimer’s disease).
The FTLD group was composed of two syndromes: FTD
and SD. To explore whether either of these groups was
disproportionately showing reduced reactivity, we examined
the response to the US averaged across all 10 learning trialsseparately for FTD and SD. As shown in Fig. 4, the USresponse was low in both SD and FTD, compared with theother groups. A post hoc statistical comparison confirmedthat there was no significant difference in average responseto the US between SD and FTD (two-sample t -test,P = 0.326).
Fig. 3 Magnitude of SCR after each of the 11 presentations of the100 db US, once prior to the experiment and 10 times during theexperiment. Means for controls, FTLD and Alzheimer’s disease.
Fig. 2 Difference in SCR for CS+ versus CS in each group ineach phase of the experiment.
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Reactivity to the US and conditioning
The low reactivity to the US in FTLD suggested that this
may be an important factor determining their apparent lack
of conditioning. Thus, the contribution of the US response
to conditioning was assessed by repeating the original
regression model with conditioning as the dependent
variable and US response as an additional independent
variable. For this analysis, response to the US was averaged
across all 10 presentations to create a single covariate.
With this change, the overall effect of adding diagnosis
to the model was still significant (P 50.04). However, the
difference between FTLD and controls for conditioning wasreduced and was only marginally significant (B =0.028,P = 0.067), while the impairment in Alzheimer’s disease
remained significant (B = 0.041, P 50.01).
Neuropsychological and behavioural data and
clinical-physiology correlationsTables 1 and 2 summarize the neuropsychological and
behavioural findings in controls and in the three patient
groups: Alzheimer’s disease, FTD and SD. There were no
differences in age or sex distribution across groups. All
patient groups showed increased Clinical Dementia Rating
Scales and decreased Mini-Mental State Examinations
compared with controls, but there were no differences
across patient groups. As would be expected, Alzheimer’s
disease patients showed the worst performance in memory
and figure copying, and they also showed poor calculation
abilities and low performance on many measures sensitive
to frontal lobe disease. SD patients showed the worst
picture naming. In the behavioural domain (Table 2), both
FTD and SD showed high levels of apathy, disinhibition
and eating disorders, none of which were significantly
different between FTD and SD, consistent with prior
reports demonstrating behavioural overlap between FTD
and SD (Liu et al ., 2004).Bivariate correlation analyses were performed examining
the relationship between the degree of conditioning,
reactivity to the US and all the variables listed in
Tables 1 and 2 across patients. GDS score was correlatedwith reactivity to the US (r = 0.383, P 50.02, uncorrected
for multiple comparisons). This analysis was repeated as a
partial correlation, factoring out diagnosis and the relation-
ship was still significant. No other variables were correlatedwith the physiology data.
VBMFTLD and Alzheimer’s disease both showed regions of
significant volume loss (P 50.05, corrected for multiplecomparisons) compared with controls (Fig. 5). In FTLD,
these regions included the left posterior orbitofrontal
region, extending into the left insula, and the right insula.In Alzheimer’s disease, the regions were in the tempor-
oparietal cortex bilaterally.Anatomical correlates of physiological variables are
summarized in Table 3 and illustrated in Fig. 6.
Conditioning was examined using reactivity to the US as
a covariate, in addition to age, sex and total intracranial
volume. No regions correlated with conditioning wereidentified across the experimental group at a whole-brain
corrected level of significance, or within the amygdala to a
level of P 50.01. However, in FTLD, conditioning was
correlated with tissue content in the right amygdala (voxel-level P = 0.004, P = 0.062 corrected for volume of the
bilateral amygdala ROI). No effects could be found in
controls or Alzheimer’s disease.
Table 2 Neuropsychiatric Inventory frequency severityproduct (and per cent with that feature) across diagnosticgroups
Feature Controls Alzheimer’sdisease
FTD SD
Delusions 0 0.6 (29) 0.8 (18) 0 (0)Hallucinations 0 0.3 (24) 0.4 (16) 0.2 (10)Agitation 0 1.2 (29) 2.8 (50) 2.9 (60)Depression 0 2.2 (52) 1.7 (17) 1.0 (40)Anxiety 0 2.0 (52) 2.6 (50) 1.8 (40)Elation 0 0.4 (10) 0.8 (33) 1.3 (30)Apathy 0 3.8 (57) 6.3 (92) ¥ 4.9 (70)Disinhibition 0 0.8 (38) 4.1 (67) ¥ ,y 5.1 (80) ¥ ,y
Irritability 0 0.8 (43) 1.4 (33) 2.6 (40)Aberrant motorBehaviour
0 2.0 (33) 3.8 (58) 2.8 (50)
Sleep Disorders 0 1.1 (38) 2.3 (50) 0.8 (30)Eating Disorders 0 1.6 (33) 5.9 (75) ¥ ,y 4.7 (80) ¥
P50.05 across groups (ANOVA for frequency severity
product).
¥
P5
0.05 versus Controls (post hoc using Tukey HSD).yP50.05 versus Alzheimer’s disease (post hoc usingTukey HSD).
Fig. 4 Magnitude of the SCR averaged in each subject across all10 presentations of the US during the experiment. Means forcontrols, FTD, SD and Alzheimer’s disease.
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Reactivity to the US (average across the 10 presentations)
was correlated with volume in dorsal anterior cingulate
cortex (ACC) region (voxel-level P = 0.01) and left insula
across the entire group (voxel-level P = 0.001) but these
effects were not significant after multiple comparison
correction. However, in controls, US reactivity was more
significantly correlated with dorsal ACC volume (voxel-levelP 50.001, P = 0.028 after multiple comparisons correction
within the frontal-temporal ROI). In FTLD, reactivity to
the US appeared to be correlated with volume in the left
insula (voxel-level P = 0.004, not significant after multiple
comparisons correction). No correlations were detected in
Alzheimer’s disease.
Extinction was examined using the magnitude of
conditioning as a covariate, and was correlated with
volume in the posterior OFC, with a more significant
effect on the right (voxel-level P 50.001, P = 0.03 after
Fig. 6 Regions where gray matter content was correlated with physiological responses in specific groups. T -maps overlayed onstudy-specific template.
Table 3 Regions of correlation between gray matter volume and physiological variables
Variable Conditioning US reactivity Extinction
ROI B/L amygdala B/L ACC/OFC/amygdala B/L posterior OFCCoordinatea Regionb Uncorrected/
correctedP-value
Coordinatea Regionb Uncorrected/correctedP-value
Coordinatea Regionb Uncorrected/correctedP-value
Across Groups ç ç ç 4,19, 31 LdACC 0.01/40.1 18, 10, 20 ROFC 50.001/0.0337,11, 12 Linsula 0.001/40.1
Normal Control ç ç ç 4, 8, 36 LdACC 50.001/0028 18,16, 24 ROFC 0.003/40.1FTLD 26, 2, 24 Ramygdala 0.004/0.062 37, 3, 3 Linsula 0.004/40.1 ç ç ç Alzheimer’sdisease
ç ç ç ç ç ç 13, 23, 11 ROFC 0.01/40.1
ROI = Region of interest used for small volume correction for this correlation, B/L = bilateral, dACC= dorsal anterior cingulate cortex,OFC = orbitofrontal cortex. aCoordinate in the standardized space based on the Montreal Neurological Institute (Evans et al., 1994) brain.bAnatomical region of the coordinate, based on overlaying statistical map on the study specific template. L= left, R = right.
Fig. 5 Regions of gray matter tissue loss in FTLD and Alzheimer’sdisease versus controls. T -maps overlayed on study-specific
template.
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multiple comparisons correction within the posterior OFCbilaterally) The same region appeared to be correlated with
extinction in controls (voxel-level P = 0.003, not significantafter multiple comparisons correction), and at a lesssignificant level in Alzheimer’s disease (voxel-levelP = 0.01, not significant after multiple comparisons correc-
tion). No effects with a P -value 50.01 were seen in thisregion in FTLD.
Discussion
Our study suggests that both Alzheimer’s disease and FTLD
are associated with abnormalities emotional reactivity in aclassical fear FC paradigm, but that the nature of the deficitdiffers between the two disorders. In Alzheimer’s disease,the deficit is isolated to conditioning, with normal reactivity to an inherently aversive stimulus, a replication of a priorfinding in Alzheimer’s disease (Hamann et al ., 2002). In
FTLD, the deficit appears to be different, with reduced
physiological reactivity to an aversive stimulus. Physiologi-cal deficits were not accounted for by medication use orhearing deficits, and the deficit in reactivity to the US was
present in both SD and FTD—two variants of FTLD thatshow a large degree of behavioural overlap (Bozeat et al .,2000; Snowden et al ., 2001; Liu et al ., 2004; Rosen et al .,2006), again seen in this study. Physiological reactivity did
not correlate with any neuropsychological variables, indi-cating that these deficits were not mediated by cognitiveimpairment. Thus, this FC paradigm appears to pinpointfunctional deficits in FTLD and Alzheimer’s disease that arenot addressed by traditional neuropsychological testing anddo not correlate with any particular cognitive test. While
the numbers in this study are relatively small for this typeof paradigm and the findings for FTLD should bereplicated, the distinctive types of abnormalities in FTLDversus Alzheimer’s disease suggested here offer new insights
into the emotional and cognitive abnormalities seen inthese two disorders.
Our findings highlight the contribution of several corticalregions to emotional learning. The amygdala, which is theprimary site for the establishment of CS–US association
(LeDoux, 1992), is strongly interconnected with theanterior cingulate, insula and OFC (Amaral and Price,1984). These paralimbic regions function as transition zones
between evolutionarily older limbic cortex and newerneocortical structures subserving higher-level multimodalprocessing, providing a link between visceral processing and
other sensory information (Mesulam and Mufson, 1982a, b;Mufson and Mesulam, 1982). Prior studies have shown thatinjury to some of these regions, including the ventromedialfrontal cortex and ACC, as well as right parietal regions,
causes reduced electrodermal reactivity to aversive stimulisimilar to those used in our study (Tranel and Damasio,1994). FTLD consistently affects these paralimbic regions(Rosen et al ., 2002; Diehl et al ., 2004; Ibach et al ., 2004;
Liu et al ., 2004; Boccardi et al ., 2005), which was
highlighted by our VBM data showing bilateral insulaand left OFC tissue loss in FTLD. Thus, we propose thatinjury to the OFC and/or insula causes reduced reactivity to the US in FTLD. The VBM analysis suggested that insula,in particular, may account for this deficit in FTLD,although the statistical significance of this association
was low, possibly because FTLD patients were uniformly low in both insular tissue content and US reactivity,limiting the variance needed to estimate the relationship.The insula is well-established as a site of visceral processing(Cechetto and Saper, 1987; King et al ., 1999). Insularactivation correlates with SCR magnitude in fMRI studies(Critchley et al ., 2000), and the insula has been proposedas one route for transmission of aversive stimulusinformation to the amygdala in FC paradigms (Shi andDavis, 1999).
The VBM analysis also highlighted a relationship betweencerebral changes in normal aging and physiologicalreactivity. Normal aging is known to be associated with
reductions in reactivity to USs (Labar et al ., 2004) and thiswas correlated with tissue loss in the ACC in our analysis.This association was statistically significant, after correctionusing a large ROI encompassing the amygdala andparalimbic regions mentioned earlier. Notably, this associa-tion was not detected in Alzheimer’s disease, suggesting thatother factors may affect US reactivity in this group.
Although electrodermal activity was reduced in FTLD,many normal controls had low electrodermal activity usingthis method as well, as has been shown in previous work (Labar et al ., 2004). Thus, electrodermal responses to thisspecific stimulus are not a sensitive diagnostic markerof FTLD, but they do suggest that other measures probingthe emotional reactivity system in the brain, such as fMRI,may provide sensitive measures of disease. In addition,although electrodermal activity did not correlate with spe-cific behaviours on the Neuropsychiatric Inventory, reducedphysiological reactivity has potential implications forbehaviour in FTLD. Our image analysis and prior studiesreferenced above suggest that electrodermal reactivity toaversive stimuli depends on the integrity of severalstructures, including the ACC and VMPC/insula, whichare important structures for emotional processing. Reducedphysiological responsiveness to aversive stimuli may be amarker of underactivation of these structures in response to
punishments, which may cause events that would beconsidered aversive to normal individuals be consideredless fearsome by FTLD patients. If this were the case, FTLDpatients may be less likely to alter their choices based onthe fear that their actions could generate potentially aversive consequences. In support of the idea that FTLDpatients are less sensitive to aversive consequences, previousstudies have indicated that FTLD patients have reducedsensitivity to pain (Snowden et al ., 2001). The specificphysical sensations associated with physiological activationhave also been hypothesized to play an important role incognitive and social decision making (Bechara et al ., 2000),
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an idea sometimes referred to as the somatic markerhypothesis (Damasio, 1996). Based on this hypothesis theabsence of the visceral sensations associated with peripheralphysiological activation itself may be a factor contributingto a change in behaviour in FTLD, such that thesesensations deprives FTLD patients of important informa-
tion for cognitive and social decision making. Whether thedeficit in sensory input due to impaired physiologicalactivation could be, in and of itself, an importantcontributor to abnormal behaviour in FTLD, as opposedto altered physiology just being a marker of altered corticalprocessing, is unclear, and cannot be resolved from ourdata. Because some normal older individuals showed low physiological reactivity, but this was more prevalent inFTLD, we would favour the idea that our findings representa marker of more pervasive impairment in activation of cortical emotional processing systems.
Even after correction for reactivity to the US, the FCimpairment in FTLD was marginally significant, suggesting
a true conditioning deficit in addition to their deficit inreactivity. The residual FC ability was correlated withamygdala volume. This is consistent with studies demon-strating FC impairment due to amygdala damage (Becharaet al ., 1995; LaBar et al ., 1995), and with prior studiesshowing volumetric changes in the amygdala in FTLD(Chan et al ., 2001; Liu et al ., 2004). Whereas reducedelectrodermal reactivity appeared to be characteristic of FTLD, conditioning may have occurred in patients withrelatively little amygdala loss.
Abnormal electrodermal responding in FTLD does notnecessarily imply that all emotional reactivity is impaired.A recent study of emotional reactivity in FTLD demon-strated impaired activation of self-conscious emotions, butfound that basic reactivity to a startle stimulus was intact(Sturm et al ., 2006). The stimulus used in that study was presented without warning and was three times louder(115 db) than the stimulus used in this study, which doesnot typically elicit a full defensive startle response. Thepreservation of the defensive response to the more powerfulstartle stimulus suggests that the basic physiologicalmechanisms for autonomic and behavioural responding,generated in the brainstem, hypothalamus and spinal cord,remain intact in FTLD. It is notable that FTLD patients inthe current study appeared to show short-term habituation,
decreasing their reactivity to the US after the first exposureprior to the experiment. Short-term habituation of theacoustic startle reflex has been demonstrated in decerebraterats, indicating that it can occur purely through brainstemmechanisms (Leaton et al ., 1985). This further supports theidea that basic subcortical mechanisms of behavioural andphysiological responding to aversive stimuli are intact inFTLD. What the current study suggests is that corticalinfluences on these basic reactions are altered, so thatresponses to less dramatic stimuli can be impaired.
Our data also demonstrated impaired FC in Alzheimer’sdisease, where the electrodermal response to the US
was normal. These data confirm a previous study (Hamann
et al ., 2002) that demonstrated impaired FC in Alzheimer’s
disease. Although this abnormality could relate to amygdala
damage, we could not identify this relationship with VBM,
even after placing a ROI in the amygdala. This relationship
may emerge with a larger sample, or the deficit may be
determined by functional changes in the amygdala that are notcorrelated with volume loss. The behavioural implications of
the FC deficit in Alzheimer’s disease are not established. One
potential effect is an impact on episodic memory, because of
the known effects of emotional processing in enhancing
episodic memory (Cahill and McGaugh, 1998). A previous
study demonstrated a blunted impact of emotional processing
on memory in Alzheimer’s disease (Hamann et al ., 2000).
There may also be effects in the emotional realm. Alzheimer’s
disease frequently causes behavioural abnormalities including
apathy, irritability and agitation (Cummings, 1997). If deficits
in emotional learning prevent learning new associations
between neutral events and aversive outcomes, patients couldbecome confused about what is threatening in their environ-
ment, leading to irritability and agitation. It is also notable
that reactivity to the US was correlated across all patients with
the GDS score, indicating this type of physiological reactivity
may influence mood, both in Alzheimer’s disease and FTLD.We also found that right OFC volume is associated with
extinction. Although this finding was significant across the
whole group of subjects (after multiple comparison
correction for an OFC ROI), the data looking at this
relationship in individual groups suggested that most of the
effect was driven by the normal control subjects, who were
able to generate conditioned responses. This finding is
consistent with previous studies relating posterior OFC toextinction in FC paradigms, and more generally to
modification of previously established cue-reward associa-
tions (Kringelbach and Rolls, 2004; Phelps et al ., 2004;
Milad et al ., 2005; Quirk and Beer, 2006).The finding of abnormal FC in dementia may have broad
implications. FC is a unique behavioural probe for studying
neurodegenerative disease. Although it represents a very
low-level emotional response that cannot easily be linked to
more complex behaviour, this paradigm has several
attractive features. The behavioural component is simple,
and requires relatively little comprehension, allowing
patients of varying severity to be studied. It does not
seem to correlate with standard measures of cognitive
function, suggesting that it provides a novel window into
brain function relative to these measures. In addition, our
data suggest that these processes are disrupted early in
neurodegenerative disease. Most importantly, FC has been
extensively studied in humans and animals, and appears to
depend on the same brain structures across species,
allowing parallel studies in humans and animals to be
designed. These features suggest that FC can be a powerful
tool for linking molecular changes in neurodegenerative
disease with human behaviour.
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AcknowledgementsThis work was supported by the State of California
Department of Health Services (DHS) grant 04-35516NIA,
State of California DHS Alzheimer’s Disease Research
Center of California (ARCC) grant 01-154-20, NIH grants
1K08AG020760-01, AG10129, P50-AG05142 and AG16570,
NINDS grant NS050915, grant number M01 RR00079(UCSF General Clinical Research Center) and the Hillblom
Network.
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