extracting diagnostic information from intrinsic...

$: Extracting Diagnostic Information from Intrinsic Networksmialab.mrn.org/2011_HBM_GroupICASymposium.pdf · Constrained ICA 35 1 { ( ) } { ( : ) } 2 ... and V. D. Calhoun, "Capturing$
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Comparison of multi-subject ICA methods for analysis of fMRI data:

Consistency and Variability of Intrinsic Networks in the Healthy and Diseased Brain

Vince D. Calhoun, Ph.D. Chief Technology Officer &

Director, Image Analysis & MR Research The Mind Research Network

Professor, Electrical and Computer Engineering (primary),

Psychiatry, Neurosciences, & Computer Science The University of New Mexico

Overview

• Motivation

• A brief review of group ICA

• A Testing Framework

• Application to large study

• Application to disease groups

• A Simulation Framework & Toolbox (simTB)

• Pushing the limits of Group ICA

• Summary

2

Prediction of Individual Responses is Important

3

E. Allen, et al, "A baseline for the multivariate comparison of resting state networks," Frontiers in Human Neuroscience, vol. 1, p. 12, 2011. 4

Extracting Diagnostic Information

from Intrinsic Networks

•Accurate classification requires single-subject accuracy -> very stringent requirement!

•We cannot use knowledge of the diagnosis in the development of the classification algorithm

Overview

• Motivation







• Summary

5 6

Group ICA

R L

V.D. Calhoun, T. Adali, G.D. Pearlson, and J.J. Pekar, "A Method for Making Group Inferences From Functional

MRI Data Using Independent Component Analysis," Hum. Brain Map., vol. 14, pp. 140-151, 2001.

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Single Subject ICA vs Group ICA

E. Erhardt, E. A. Allen, Y. Wei, T. Eichele, and V. D. Calhoun, "SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability," NeuroImage, Under Review. & Poster #657. E. A. Allen, E. Erhardt, Y. Wei, T. Eichele, and V. D. Calhoun, "Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study," NeuroImage, Under Review. & Poster# 690.

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Group ICA

:

Subject 1

Subject N

Data

Subject i

Back-reconstruction through inversion1

1

Ai Si

ICA (Forward Estimation)

PCA reduction1

A S_agg

ICA

A1

AN

PCA reduction2

Subject i

Back-reconstruction through Spatial-temporal (dual) regression2,3

Ai

Si

S_agg 1) Regress onto each timepoint of to generate

Subject i Ai 2) Regress onto each image of to generate

Iterate steps 1 & 2 until converged

1. V. D. Calhoun et al Hum.Brain Map., vol. 14, pp. 140-151, 2001. 2. V. D. Calhoun et al Neuropsychopharmacology, vol. 29, pp. 2097-2107, 2004.

3. N. Filippini et al Proc Natl Acad Sci U S A, vol. 106, pp. 7209-7214, Apr 28 2009

Default Mode Group Maps

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Inversion STR

E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject ICA methods for analysis of fMRI data," Hum Brain Mapp, In Press.

Evaluation of Group ICA Methods

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E. Erhardt, S. Rachakonda, E. Bedrick, T. Adali, and V. D. Calhoun, "Comparison of multi-subject ICA methods for analysis of fMRI data," Hum Brain Mapp, In Press.

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X

Subject 1

Subject N

Data

A S_agg

ICA

A1

AN

Subject i

Back-reconstruction

1

Ai Si

Comp# R2 Subject Reg1 Reg2

2 0.81

1 1.89 0.02

2 2.28 0.66

10 0.80

1 0.28 2.19

2 0.65 2.03

4 0.017

1 -0.19 -0.40

2 -0.10 0.08

Component Timecourses

Task-modulation

(e.g. Fit timecourses

to GLM model then

test parameter)

Component Images

Voxel-wise stats

(e.g. one-sample t-test,

two-sample t-test,

correlation, etc)

}

}

Component

images

(one per

subject)

T-statistic

}

}

Multiple

regression

fit to ICA

timecourses

Beta-weights

(second

level

model)

} Model

timecourses

Spectra

(e.g. power spectra,

fractal parameters,

etc)

Controls-Patients

-5

-4

-3

-2

-1

0

1

2

3

4

0.03 0.08 0.13 0.19 0.24 0.3

Frequency (Hz)

T-v

alu

e

T-Values

} Component

timecourses

} Power

spectra

group

differences

Tim

e

Components Voxels Voxels

Co

mp

oe

nts

Tim

e

Tim

e

Voxels

Tim

e

Components

Functional Network

Connectivity

(e.g. inter-component

correlation)

Overview

• Motivation







• Summary

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3

Rest fMRI Networks (N=603)

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E. Allen, et al, "A baseline for the multivariate comparison of resting state networks," Frontiers in Human Neuroscience, vol. 1, p. 12, 2011.

Main Effects & MANCOVA Model

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1

1

1

log ,..., log

,...,

atanh ,..., atanh

TT T

c c Mc

TT T

c c Mc

TT T

M

P P P

S S S

K K K

Selecting/Labeling Components

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28 labeled components superset of 75 components

N=603 subjects http://mialab.mrn.org/data/index.html

Various Metrics for component selection Compare with below atlas

Spatial Maps

16


Functional Network Connectivity (FNC)

17


Spectra

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http://mialab.mrn.org/data/index.html









4

Overview

• Motivation







• Summary

19

Relationship to Disease (N=1140)

Healthy (N=590) Substance Use (N=469)

Schizo/BP (N=81)

Within network example: anterior DMN Between network example:

precuneus-cerebellar connectivity

Overview

• Motivation







• Summary

23

• Motivation

• Simulated data facilitate comparative evaluations

• Creating reasonable simulations can be challenging

given the complexities of fMRI data

• SimTB goals

• Flexible, easy and fast generation of simulated fMRI

data under an ICA model

• Ability to answer basic questions regarding the group

ICA model

A simulation toolbox

1

Cnn r

i ic ic ic i i i i i

c

Y g N diag N

r s R g S

5

E. Erhardt, E. A. Allen, Y. Wei, T. Eichele, and V. D. Calhoun, "SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability," NeuroImage, Under Review. & Poster #657.

Overview of simTB Spatial Maps

• Roughly 30 SMs can be selected through the graphical

user interface by clicking on components, or by component

number in the batch script

• For each subject, each selected component can be included

or not, be translated and rotated in space, and the spatial

spread of the component can be increased or decreased

• Each SM is normalized to have a peak-to-peak amplitude

range of one

Moments compared with real fMRI data

• Tissue types affect baseline intensity

• Possible to match simulation to real fMRI data

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Two paradigm examples

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• x- and y- translation and rotation

• Subject 1 (bold) has half the motion of subject 2

http://mialab.mrn.org/software

Overview

• Motivation







• Summary

29

Group ICA: Pushed to the Limits

• Four Experiments

• Varied component rotation

• Varied component position

• Varied component amplitude

• Varied the temporal correlation among components

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E. A. Allen, E. Erhardt, Y. Wei, T. Eichele, and V. D. Calhoun, "Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study," NeuroImage, Under Review. & Poster #690.


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Rotation

31

Position

32

Amplitude

33

Temporal Correlation Among Components (FNC)

34

Constrained ICA

35

1{ ( ) } { ( : ) }

2

T TE G E g y yλ W ρ Wx x μ y W x

Q. Lin, J. Liu, Y. Zheng, H. Liang, and V. D. Calhoun, "Semi-blind Spatial ICA of fMRI Using Spatial Constraints”, Hum. Brain Map., vol. 31, 2010

Overview

• Motivation







• Summary

36

7

Summary

• groupICA provides excellent estimates of single

subject SMs and TC and captures extensive spatial,

temporal, and amplitude variability.

• In general spatial ICA assumes consistency across

space, however estimation is surprisingly robust to

modest violation of this model.

• We introduce a tool for rapidly producing realistic

simulations of fMRI data under an ICA model.

• Enable rapidly answering questions about existing and

new ICA and group ICA approaches.

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E. Erhardt, E. A. Allen, Y. Wei, T. Eichele, and V. D. Calhoun, "SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability," NeuroImage, Under Review. & Poster #657. E. A. Allen, E. Erhardt, Y. Wei, T. Eichele, and V. D. Calhoun, "Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study," NeuroImage, Under Review. & Poster# 690.


Software/Algorithms

Left Hemisphere

Visual Stimuli Onset

Left Hemisphere

Visual Stimuli Onset

http://mialab.mrn.org/software 4100+ unique downloads

Funded by: 1R01EB006841


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http://mialab.mrn.org Posters: #657 (M-T), #690 (W-Th)

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extracting diagnostic information from intrinsic...

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