Hi DCM experts,
I recently conducted a couple of experiments to familiarize myself with DCM and got some really surprising results; I would appreciate any feedback to find potential errors in my implementation of DCM.
First I applied DCM to an existing dataset in the lab that had been used to test granger, and got results that were inconsistent with a seemingly highly probable hypothesis. Conceding that we weren’t 100% sure of our effective connectivity hypothesis, I turned to simulations.
I simulated data using the following parameters/procedure (using the R package FIAR). Everything (timing, scanning parameters etc.) was based on a template subject’s scan.
- Single 5 min scan
- TR = 0.240 sec (to be consistent with granger dataset parameters)
- TE = .025 sec
- A = [0 0 0 0; 0 0 0 0; .01 1 0 .01; 1 .01 .01 0]
- B = zeros
- C = [1 .5 0; .5 1 0; 0 0 0; 0 0 0]
- SNR = 10, and then in another simulation, NO noise was added.
- Each of the first two inputs had 10 unique onsets (~14 seconds apart, duration of .5 seconds).
I input the timeseries generated from the above model into our SPM scripts by replacing the following fields in the template subject’s DCM.mat file:
- DCM.Y.y(:, roi) = fake timeseries generated for that roi
- DCM.xY(1,roi).y = fake timeseries
- DCM.xY(1,roi).u = fake timeseries
I included just three models in model space. The first was the one used to generate the data. The second was the “opposite” model in which the intrinsic connections pointed the other direction and the direct inputs entered in the opposite pair of regions. The third was the union of those two models.
The BMS results (FFX with one subject, and FFX & RFX when I repeated this with two template subjects) always yield the third model (union model) as the very, very clear winner.
How can this be? There are a number of things that we have done that I understand are atypical (fast TR, no modulations, use FIAR to generate data, short-cut timeseries replacement …), but nothing I can think of should have made the wrong model win so definitively.
I would be happy to follow up with more details about what we have tried.
Thank you for your time,
Becky van den Honert
|