Dear DCM experts: I just ran bayesian model selection on a dataset (16 subjects, 1 run per subject), and got a rather confusing result. Please let me know if you can help:
The experiment was simple: an electrical stimulus was applied to each subject's brain in a block design (subject at rest). Activation (2nd level - one sample t-test, pFWE < 0.05) was observed in 2 brain areas with established connectivity. I then set up 16 competing models. The A matrix included within- and between-region connections for the 2 regions. The A matrix was held constant across all models. The B matrix representing the effect of the stimulus was varied across model space, with the stimulus affecting all possible combinations of the 4 A Matrix connections (2^4 = 16 models per subject).
The winning model (by both RFX and FFX BMS) was the model in which the stimulus affected None of the connections. Since the group activations must be a result of the stimulus (the subjects were otherwise at rest), how can it be possible that the stimulus did not affect connectivity between (or within) the activated regions? This result makes absolutely no sense to me.
I used stochastic, two-state DCM within SPM12.
Please let me know if you have any thoughts/suggestions.
Many thanks.
-Will
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