resting-state functional connectivity and spontaneous brain co … · 2020. 4. 13. · corrmap from...

Resting-state Functional Connectivity and

Spontaneous Brain Co-activation

Xiao Liu, Ph.D.

Assistant Professor

Department of Biomedical Engineering

Institute for CyberScience

Functional Magnetic Resonance Imaging (fMRI)

Introduction | Background Methods Results Discussions

Sig

na

l

T2*/T2 Decay

TE

Rest

Difference Activation MapOgawa S et al. PNAS, 1990&1992

Bandettini PA, et al. MRM 1992

Kwong KK et al. PNAS 1992

Neural Activity

Activated

CBF

CBVCMRO2

RestActivated

T2*/T2 Weighted MR Images

Resting-state fMRI Connectivity: The First Study

Functional Map

(Task-evoked Response)

Biswal et al. MRM (1995)

Correlation Map

(Spontaneous Correlation)

Finger Tapping

HRFParadigm

Session #1

Resting State

Reference Time Course

Session #2

Spontaneous fMRI Signal


(External Modulation)

Reference Time Course

(Endogenous Fluctuation)

Other Networks


Auditory

Cordes D et al. AJNR (2000) Language

Hampson et al. HBM (2002)

Default Mode

Fransson P et al. HBM (2005)

Memory

Vincent JL et al. J Neurophysiol (2006)

Attention

Fox MD et al. PNAS (2006)

Visual

Mantini et al. PNAS (2007)

More …

RsfMRI Correlation Functional Connectivity

Correlational Patterns Resting-state Networks

Significance


Raichle Science (2006) Whitefield-Gabrieli et al. PNAS (2009)

Brain’s Dark Energy

1. Basic Neuroscience 2. Clinical Application

Functional Connectivity

Network Organization

Characterize Brain States

…

Schizophrenia

Parkinson’s Disease

Alzheimer’s Disease

Autism

…

Trend


What causes rsfMRI signal correlations & their network

pattern?


Agenda

Part I

Q: What causes network-specific correlations

and their temporal dynamics?

resting-state networks and co-activation

patterns

a temporal decomposition method

Part II

Q: What causes global rsfMRI signal, non-specific

correlation, and their behavioral relevance?

global rsfMRI signal, non-specific co-

activation, and their relation to vigilance

underlying neuronal events

Non-stationary Brain Connectivity

Functional connectivity

Temporal fMRI signal correlation

(Averaged relationship)

=

A

BC

t1

A

BC

t2

A

BC

t3

A

BC

tm

A

BC

tn

… …

A

BC

Average

Co-activation Pattern | Background Methods Results Discussions

RsfMRI Signal Correlations Vary over Time

Chang et al. NeuroImage (2010)

Stationary correlations give similar information as structural connectivity

Structural Connectivity Functional Connectivity

from HCP


Extra dimension of information?


Limitations

Pairwise correlation

Shorter time window Larger temporal variations

solely by signal-to-noise ratio reduction?

Neuron 1

Neuron 2

Neuron 3

Case #1 Case #2

non-neuronal events OR brain connectivity?

high-order correlation: co-activations of multiple regions

particularly important for neuroimage data

the number of voxels (N) >> the number of time points (T)

N. (N-2)/2 >> N.T(pairwise correlations) (actual measurements)

Alternative way to understand non-stationary functional connectivity?

Replicate RSN Patterns with A Few Time Points

CorrMap from 123 volumes Average of 18 volumes Single volume

Average of 15% data

Group Level

CorrMap from 100% data

r = 0.995

Threshold


fMRI signal from the posterior cingulate cortex (PCC) seed

BO

LD

[S

.D.]

Time [sec]PCC

r

-0.5

0.5

temporal

mean

Liu X. and Duyn J.H., PNAS (2013)

Distinct Patterns at Different Time

50 sec

2 S

.D.

PCC

mPFC

Frame 1Frame 2

Frame 2 Frame 1

Time

BO

LD

**

Liu X. and Duyn J.H., PNAS (2013)Co-activation Pattern | Background Methods Results Discussions

PCC

mPFC

Classifying fMRI Volumes According to Their Spatial Patterns

CAP i

CAP i +1


CAP = Co-Activation Pattern


Temporal Decomposition of Default Mode Network

0.8

-0.8

BOLD

[S.D.]

=

+

+

+

+

+

+

+

PC

C-C

AP

1P

CC

-CA

P 2

PC

C-C

AP

3P

CC

-CA

P 4

PC

C-C

AP

5P

CC

-CA

P 6

PC

C-C

AP

7P

CC

-CA

P 8

Overa

ll

Avera

ge

of

15%

da

ta


caudate

nucleus

Hippocampus

parahippocampal gyrus


Temporal Decomposition of Default Mode Network

PCC-CAP 1 PCC-CAP 2

MFG

PCC-CAP 1 PCC-CAP 3

Z >

6

SFG

PCC-CAP 1 PCC-CAP 4

Liu X. and Duyn J.H., PNAS (2013)Co-activation Pattern | Background Methods Results Discussions

Not Limited By Seeding

CAP i

CAP i +1


Not Limited By Seeding

CAP i

CAP i +1

30 CAPs

Liu X. et al. Front Syst Neurosci (2013)Co-activation Pattern | Background Methods Results Discussions

Two Anti-Correlated Networks? Or Multiple versus One?

Liu X. et al. Front Syst Neurosci (2013)

CAP 3

CAP 9

CAP 2

FEF

IPS

CAP 4

SMA CAP 6

PCG

-20 206-6 Z

Fox et al. PNAS (2005)


Thalamocortical Co-Activations


Visual

CAP 19 CAP 23

[-14, -22, 4][-14, -16, 4]

20

3

20

6

Sensorimotor

VPL VPM

CAP 3 20

6[18, -26, 10]

CAP 2

[18, -26, 10]

20

6

CAP 26

20

3

Z

[24, -22, -4]

LGN

Pulvinar Pulvinar


Thalamocortical Co-Activations (Negative)


-9 91.75-1.75 Z

-0.2 0.20.06-0.06 r

Co

rrM

ap

IC 6

-20 206-6 Z

CA

P 1

9

Thalamic Reticular

Nucleus?


Functional Relevance of the CAPs: An Example

CAP 16

Motor

Medial IPS

SMA

Grefkes et al. J Anat. (2005)

Medial IPS plays a critical role in

visuomotor coordinate transformation


Occurrence Rate versus Correlation: A Simulation

Case #

1

2

Correlation Occurrence

0.71

0.76

3

5

+7% +67%


Occurrence Rates of CAPs: Males versus Females


CAP #

Occu

rren

ce R

ate

**

CAP 23

p < 0.01, Bonferroni corrected


Further Exploration in This Area


We are trying to develop computational methods/models to quantify and

study temporal dynamics of brain networks.

Spontaneous brain activity

measured by fMRI

CAP 1 CAP 2 CAP 3 CAP 4 CAP 5

• Temporal Graph Theory

• Hidden Markov Chain

Summary

Occurrence rate of CAPs

differentiating conditions or populations

A new data-driven approach

few assumptions and data transformations

more specific information regarding brain co-activations

robust against motion artifacts

Resting-state network patterns result from co-activation patterns

(CAPs) at discrete time points.

CAPs explain non-stationary functional connectivity

whether (or how many) critical points with clear patterns are included

(change of signal-to-noise ratio), and

what types of patterns (dynamics of neuronal activity) are include in the

time window



Agenda

Part I

Q: What causes network-specific correlations

and their temporal dynamics?

resting-state networks and co-activation

patterns

a temporal decomposition method

Part II

Q: What causes global rsfMRI signal, non-specific

correlation, and their behavioral relevance?

global rsfMRI signal, non-specific co-

activation, and their relation to vigilance

underlying neuronal events

Global Signal | Background Methods Results Discussions

Global RsfMRI Signal & Non-specific Correlations

Fox, M et al., J Neurophysiol, 2009


Anti-correlation and Global Signal Regression

Fox et al. PNAS (2005)

BOLD [S.D.]

-0.8

0.

8

PC

C-C

AP

2P

CC

-CA

P 8

PC

C-C

AP

5

With GSR Without GSR

Correlation

-0.4

0.

4

Map Statistics

Distribution

Corr

Mpa

Liu et al. ISMRM 2013 #2251

CAP Decomp CAP Decomp

Murphy et al. NeuroImage (2009)

Artifact

The whole-brain co-activation occurs preferentially with

sensory systems !


Neural Component in Global rsfMRI signal

Scholvinck ML et al., PNAS, 2010

Gamma-band LFP power recorded locally is correlated with global fMRI

light sleep >> awake (e.g., Horovitz SG et al., HBM, 2008)

eyes-closed > eyes-open

caffeine

hypnotic drugs

(Wong CW et al., NeuroImage, 2013)

(e.g., Litaca CS et al., NeuroImage, 2013)

(e.g., Jao T et al., NeuroImage, 2013)

Global rsfMRI signal is inversely correlated with vigilance

Why Important?


Yang et al. PNAS (2014)

Global changes due to vigilance change ?

OR

Whitefield-Gabrieli et al. PNAS (2009)

An example: functional

connectivity in schizophrenia

Local connectivity change due to network dysfunction ?

Non-specific correlations confounded with network-specific correlations

1. What is the neural correlate of the global fMRI signal?

2. Why is it sensitive to vigilance change?


Global Signal in ECoG Data

Eyes-closed Rest

Ketamine/Medetomidine

Propofol

Sleep

Liu, X. et al. Cere. Cor. 2014

Electrocorticography (ECoG)


Global Signal in ECoG Data

Liu, X. et al. Cere. Cor. 2014

…

r

-0.5

0.5

…

Average

Average

60 sec

14

23

31

53

14

23

31

…

…

…

…

…

53

Z

-2

2

1 sec

1423

31

53

Cross-electrode

Correlation

Clustering

53

1423

31a-BLP

q-BLP

d-BLP

g-BLP

b-BLP

aqd

g

b

or

Eyes-closed Rest

Ketamine/Medetomidine

Propofol

Sleep

Electrocorticography (ECoG)

Broadband

removed

global averaged

spectrogram

1. Event-like process?2. Spectral characteristics?3. Relation to vigilance?

Channel-Averaged Spectrogram

10 sec

2 mV

64

24

93

126

69

12

74

90


A Sequential Spectral Transition (SST) Pattern

1 sec

2 mV

1 2 3

High (42−87 Hz)

Middle (9−21 Hz)Low (<4 Hz)

20

40

60

80

1 2 3

Fre

qu

en

cy [

Hz]

10 sec

1 sec

Δ P

ow

er

[dB

]

-5

5

Liu, X. et al. NeuroImage, 2015


Averaged Pattern of Sequential Spectral Transition (SST)

Low-Frequency SSI

Sleep

(Monkey G)

-10-20 100 20

90

70

50

30

10

Δ P

ow

er

[dB

]

-1.3

1.3

10-20 100 20

0.4

0.2

-0.1

-0.2

0

-0.4

-0.3

0.3

0.1

-12.8 sec

-1.2 sec

Sleep

(Monkey C)

-10-20 100 20

90

70

50

30

10

Δ P

ow

er

[dB

]

-2

2

Time [s] 10-20 100 20

0.3

0.1

-0.1

-0.2

0

-0.4

-0.3

0.2

Corr

ela

tion

Middle vs. High

Middle vs. Low

-8 sec

-1.4 sec

Fre

quency [

Hz]

Eyes-closed Eyes-open

-10-20 100 20

90

70

50

30

10

-10-20 100 20

90

70

50

30

10

Δ P

ow

er

[dB

]

-1

1

Δ P

ow

er

[dB

]

-1

1

10-20 100 20

0.4

0.2

-0.1

-0.2

0

-0.4

-0.3

0.3

0.1

Middle vs. Low

Sleep

EC

EO

Middle vs. High

Sleep

EC

EO

Time [s]

Corr

ela

tion



SST-like Structure at the Induction of Propofol Anesthesia

Experiment 1

Experiment 2



“Sequential” Changes

Wake-promoting System Sleep-promoting System

Saper CB. et al. Nature. 2005

Takahashi K. et al. Neuroscience. 2010

Mice


Spatial Pattern and Feedback Hypothesis

Time [s]

Corr

ela

tion

Middle 1 vs High 1

Middle 1 vs High 2

Middle 2 vs High 1

Middle 2 vs High 2

12

Inhibitory feedback control of

the mid-frequency activity???

Eyes ClosedSleep

High

Frequency

Δ P

ow

er

[dB

]

-1.5

1.5

Middle

Frequency

[-5, 0] sec

Klimesch W et al. Brain Res Rev 2007

Jensen O and Mazaheri A. Front Hum Neurosci 2010

A loss of feedback

inhibitory control

A release from

such a control

Middle 1

leading in time



Spatial Pattern of Global Co-activation: ECoG versus fMRI

Eyes ClosedSleep

Concurrent fMRI-Electrophysiology data


Epochs 1 2 3 4 5 6

Time [s]

Fre

qu

en

cy [H

z]

MION-CBV

(sign flipped)

Local Field Potential

(LFP)

Data from Scholvinck ML et al., PNAS, 2010

0 5.2 10.42.6 7.8Time [s]

1

2

3

0 5.2 10.42.6 7.8

4

5

6-4

4

Norm

aliz

ed C

BV

[S.D

.]

Concurrent fMRI-Electrophysiology data

Global fMRI

average

Time-frequency

LFP pattern


-0.15

0.30.14

-.07



Eyes ClosedSleep

Human Connectome Project Data:

2mm isotropic, TR = 0.72 sec

Global Signal

averaging

large peaks

Sensorimotor

Auditory

Visual

Anterior commissure

Optic tract

Anterior commissure

Optic tract

Anterior commissure

Optic tract

Haines DE. Neuroanatomy 5th Edition

Nucleus basalis of

basal forebrain



a

b

Substantia

Nigra (SN)

-12 3015-6

Z

Dorsal Midline

Thalamus


Summary

Global signal (spatially non-specific correlation)

global average of ECoG power is characterized by a recurrent

sequential spectral transition (SST) pattern.

SST is strong during the light sleep state, weak but still present during

eyes-closed condition, but largely absent during eyes-open condition,

similar to the state-dependency of global fMRI signal

SST induces large, nearly whole-brain changes in fMRI signals

sensory regions show largest fMRI changes during global co-

activations, consistent with the spatial pattern of high-frequency

gamma power changes at SSTs.

nucleus basalis of the basal forebrain, which is the wake-promoting

center of the brain, show de-activation at the whole-brain co-

activation.

the global signal may directly or indirectly affect resting-state

connectivity quantification, and needs to be taken care properly.

Acknowledgement | Background Methods Results Discussions

Acknowledgement

Jeff Duyn

Alan Koretsky

Afonso Silva

Catie Chang

Peter van Gelderen

Jacco de Zwart

Duan Qi

Dante Picchioni

Hendrik Mandelkow

Natalia Gudino

Erika Raven

Roger Jiang

NIH/NINDS/LFMI Collaborators

David Leopold (NIMH)

Toru Yanagawa (RIKEN)

Naotaka Fujii (RIKEN)

resting-state functional connectivity and spontaneous brain co … · 2020. 4. 13. · corrmap from...

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