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Hi Rita, I’m having a little difficulty following, perhaps you could clarify how you have set this up. First, when you talk about 2 components, do you mean 2 sets of voxels identified by 2 contrasts? Assuming that’s right... If I understand correctly, you are surprised that you get activations for both contrasts. A decision on how to set up the DCM will rest on understanding what these contrasts show. Your first contrast involves a union operator – did you implement this with a conjunction test? Which type? Regarding your second question. In DCM, you define the boundaries of the system, through the regions you select. Dynamics in the system are caused by the driving inputs, which will generally be experimental and from outside the brain. The bilinear term (the modulatory inputs) relate experimental inputs to changes in coupling strength, which is analogous to the probability of transmission changing with the experimental condition or context. If, however, you want to model the change in coupling as depending on the activity in a third region, rather than just an experimental context, you can represent this with an additional non-linear term. See the original non-linear DCM paper (Stefan et al. 2008, NeuroImage) for further discussion on the distinction between bilinear and nonlinear terms. Best, Peter From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Rita Elena Loiotile Sent: 09 June 2015 23:27 To: [log in to unmask] Subject: [SPM] DCM questions Hi, I am interested in doing a DCM analysis and have 2 conceptual questions I'm hoping someone can help me with. Sorry that this is so long! 1) I have 2 stimuli: Stim1 and Stim2. I will refer to the common component between Stim1 and Stim2 as Component1 (i.e. Stim1 U (Stim2 - Stim1)) and the component unique to Stim2 (i.e. Stim2 - Stim1) as Component2. Component2 significantly activates region A. At first, I thought this would be a straightforward example in which I put a driving input of Component1 in some regions that are active under Component1 and then test modulatory connectivity to region A for Component2. This is analogous to assuming a photic driving input in V1 and testing modulation of afferent connections to V5 for motion stimuli. The part that is not analogous is that, while photic input doesn't significantly activate V5 on its own, I do see a weak but significant activation of Component1 on region A. Therefore, should I set up my model to have a driving input of Component1 on Component1-activated regions and then modulatory connectivity to region A for both Component1 and Component2? My thinking is that if I only specify modulatory connectivity for Component2, it will incorrectly subsume part of the Component1 modulatory connectivity. If so, can you please suggest what statistical tests I may be able to perform to show that Component2 modulatory connectivity is significantly greater than Component1 modulatory connectivity? (A paired t-test?) 2) Several DCM papers analogize driving and modulatory DCM inputs to driving and modulatory neuronal afferents. According to Sherman & Guillery, "The driver can be identified as the transmitter of receptive field properties; the modulator can be identified as altering the probability of certain aspects of that transmission." Maybe I am misunderstanding this, but it seems like, then, that the proper analogs to driver and modulator neurons are, respectively, bilinear and nonlinear connections. But, if that's the case (which it may not be if I've just totally misunderstood this), then what exactly would driving inputs represent? To me, it seems like driving inputs represent input from areas that have not been included in the modeled system. For example, driving input to V1 occurs in the attention to motion example because V1's actual inputs have been left out of the model (if they were included, it would probably have been evidenced as a modulation of connectivity between V1 and its inputs). Okay. That's it for now. Thanks again, Rita