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Hi Peter and SPM users, Thank you very much, Peter, for your email. It cleared many things up about the units of the self-connection parameters. Something I am still a little unsure about is, after converting these self-connection parameters into Hertz, why they are not the largest values in their respective rows of the matrix. If effective connectivity is a reflection of the neuronal influence one region has over another, shouldn't the neuronal influence of a region over itself be larger values compared to influence over other regions? I am still a little confused about this, so if you could clear this up for me, I would greatly appreciate it! Thank you very much for your time and patience, Neeraja Mahalingam Biomedical Engineering University of Cincinnati, Class of 2019 ________________________________ From: Zeidman, Peter <[log in to unmask]> Sent: Tuesday, June 26, 2018 6:55:49 AM To: Mahalingam, Neeraja (mahalina); [log in to unmask] Subject: RE: Question about effective connectivity in DCM Dear Neeraja The units for the self-connections are different than the units for the between-region connections in (one-state) DCM for fMRI. The self-connection parameters are log scaling parameters. They scale up or down the default value of -0.5Hz: a_hz = -0.5 * exp(a) Where ‘a’ is the value in DCM.Ep.A and a_hz. This is done to ensure the self-connections are negative. You can use the equation above to change the self-connections in your matrix to units of Hz, e.g for PCC->PCC: a_hz(1,1) = -0.5 * exp(0.098) = -0.55Hz The between-region connections are in units of Hz already. Best Peter From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Mahalingam, Neeraja (mahalina) Sent: 22 June 2018 16:53 To: [log in to unmask] Subject: [SPM] Question about effective connectivity in DCM Hello, I am utilizing the Dynamic Causal Modeling functions in SPM12 on resting state fMRI data. I am using the default modal network for my regions and the fully connected model for DCM. After specifying the DCM and estimating the cross spectra, I obtain the effective connectivity parameters (the A matrix). I was expecting the endogenous coupling parameters between the same region (ie PCC to PCC or mPFC to mPFC) to be the largest values because the neuronal influence of a region on itself should be very strong, but that does not seem to be the case for me in any of the 15 subjects I have analyzed. If we are taking PCC as the target region, I would expect the effective connectivity between PCC and PCC to be much larger than if the mPFC, LIPC, or RIPC are the source region and PCC is the target region. I have provided a screenshot of just one example I am referring to. As you can see, the values in the main diagonal are not the greatest values in their respective rows. Is this an expected result? If so, could I get an explanation as to why this is so, because I may have the wrong understanding of effective connectivity. Thank you, Neeraja Mahalingam Biomedical Engineering University of Cincinnati, Class of 2019