FMRI Connectivity Models: GCM & DCM1

2

FUNCTIONAL VS EFFECTIVE CONNECTIVIT Y

Functional Connectivity

x

y

•Temporal correlation

Effective Connectivity

x

y

2•Causal Flow

3

PSYCHO-PH YSIOLOGICAL INTERACTION (PPI)

ConditionY values A BLow 2 4High 3 5

ConditionY values A BLow 2 2High 4 7

Low High0

1

2

3

4

5

6

AB

Main Effect of ConditionNo Interaction

Low High0

1

2

3

4

5

6

7

8

AB

Main Effect of ConditionInteraction

Condition

4

GRANGER CAUSALIT Y MO DEL

4

Prediction of Xt

X,Y < X,Y,Z (less errors)Z contains useful information Z “Granger-causes” X

Time-seriest-1 t t+1 t+2

X 1.18 0.20 -0.83 -0.31Y 2.03 -0.02 0.19 -0.49Z 0.84 0.08 -0.01 -0.39

timeNum. of lagged

observations Coefficients ofcontribution

Errors

5

DYNAMIC CAUSALIT Y MO DEL

5

DCM: deconvolution of BOLD signal

Neural Response HRF BOLD

Regulation

Regulation •Driving Inputs•Modulatory Inputs

timeIntrinsic

Connections

Modulatoryconnections

Inputs to regions

6

GCM VS D CM

GCMGCM•BOLD signal

•“Data-driven”

•mGCM can differentiate b/w direct and indirect connections

DCMDCM•Deconvolved BOLD signal

•“Hypothesis-driven”

•Connections are predefined. No differentiation b/w direct and indirect causal connections