Dear List,
I have a question concerning DCM model specification and RFX Bayesian Model Selection.
My DCMs consist of two regions in the network (R1 and R2), and two conditions (A and B).
The question is whether it is reasonable to compare models with condition A *or* condition B modulating coupling between R1 and R2. Consider two models DCM1 and DCM2 from the model space that differ only in terms of whether condition A or B modulates coupling (B matrix). Both A and B are driving inputs (C):
drange([DCM1.DCM.xY.y]-[DCM2.DCM.xY.y]) % Data same
drange([DCM1.DCM.U.u]-[DCM2.DCM.U.u]) % Events same
DCM1.DCM.a - DCM2.DCM.a
DCM1.DCM.c - DCM2.DCM.c
DCM1.DCM.b - DCM2.DCM.b % B different (as expect)
drange(DCM1.DCM.y-DCM2.DCM.y) % Fit is (slightly) different
figure,hold on
plot(DCM1.DCM.y(:,1),'r')
%plot(DCM1.DCM.y(:,1)+DCM1.DCM.R(:,1),'r:')
plot(DCM2.DCM.y(:,1),'b')
%plot(DCM2.DCM.y(:,1)+DCM2.DCM.R(:,1),'b:')
corr(DCM1.DCM.y(:,1),DCM2.DCM.y(:,1))
drange([DCM1.DCM.y+DCM1.DCM.R] - [DCM2.DCM.y+DCM2.DCM.R]) % Reconstructed data NOT same (rounding error, or interaction between design and X0)?)
The fitted responses and F are different. And indeed, the BMS results show moderate evidence favouring models with condition B modulating coupling between R1 and R2. My question is whether DCM is capable of teasing apart which of only two conditions (with shared input) modulates connectivity, or whether this difference is more likely due to a local minimum?
Best regards
Taylor
|