A brief description of terms like: psychophysiological interaction, functional connectivity, effective connectivity, dynamic causality model and granger causality model.

1. FMRI Connectivity Models: GCM & DCM
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2. FUNCTIONAL VS EFFECTIVE CONNECTIVIT Y
Func l c
tiona Conne tivity Effe tiveConne tivity
c c
x x
y y
•Temporal correlation •Causal Flow 2
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3. PSYCHO-PH YSIOLOGICAL INTERACTION (PPI)
Condition Condition
Condition
Y values A B Y values A B
Low 2 4 Low 2 2
High 3 5 High 4 7
6 8
7
5
6
4
5
3 A 4 A
B B
3
2
2
1
1
0 0
Low High Low High
Main Effect of Condition Main Effect of Condition 3
No Interaction Interaction

4. G RANG ER CAUSALIT Y M O D EL
Time-series
t-1 t t+1 t+2
X 1.18 0.20 -0.83 -0.31
Y 2.03 -0.02 0.19 -0.49
Z 0.84 0.08 -0.01 -0.39
Prediction of Xt
X,Y < X,Y,Z (less errors)
Z contains useful information  “Granger-causes” X
Z
Num. of lagged
observations Coefficients of
time Errors
contribution
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5. D YNAM IC CAUSALIT Y M O D EL
DCM: deconvolution of BOLD signal
Neural Response HRF BOLD
Intrinsic
time Connections Inputs to
regions
Regulation
Modulatory
•Driving Inputs
Regulation connections
•Modulatory Inputs
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6. G CM VS D CM
GCM DCM
•BOLD signal •Deconvolved BOLD signal
•“Data-driven” •“Hypothesis-driven”
•mGCM can differentiate b/w direct and •Connections are predefined. No
indirect connections differentiation b/w direct and indirect
causal connections
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