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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
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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
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DYNAMIC CAUSALIT Y MO DEL
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DCM: deconvolution of BOLD signal
Neural Response HRF BOLD
Regulation
Regulation •Driving Inputs•Modulatory Inputs
timeIntrinsic
Connections
Modulatoryconnections
Inputs to regions
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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
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