Hi Will,
Those big spikes at the beginning and end are probably because an effects of interest contrast hasn't been used :-) It is a contrast which tells DCM which regressors are of interest. Anything not included in that f-contrast will be regressed out. For example, I assume you a single task regressor and then some movement regressors? Add an f-contrast called 'effects of interest' with a value of just a single 1. When you extract the ROI, select this f-contrast when prompted. See how your signal looks after that.
Best,
P
-----Original Message-----
From: Gibson, William S. [mailto:[log in to unmask]]
Sent: 20 May 2015 21:48
To: Zeidman, Peter
Subject: Re: [SPM] Confusion about DCM result
Thanks Peter, no worries - thanks for the reply! - I guess this isn't "resting state" data. The subjects are at rest and the stimulus is being briefly turned on and off, and we are looking at the effect of the stimulation, so it's more task based. I simply have a single regressor representing the bursts of stimulation, which last for only 6 seconds. I am unfamiliar with using the effects of interest F-contrast. do you think you could point me in the direction of some information on this?
I have tried quite a few things since I last emailed you, including modeling the stimulus as a driving input (C matrix) rather than a modulatory input (B matrix). This seemed to yield results that made more sense. One thing I am still unsure of though, is when I am extracting the ROIs, most of the graphs representing the 1st eigenvariate come out as representing about 60-70% of the variance, but some appear to have massive fluctuations at the beginning and/or end of the run, and it comes out as representing >99% of the variance. Any thoughts on what this might mean?
Thank you so much for your help.
-Will
On 5/20/15, 10:42 AM, "Zeidman, Peter" <[log in to unmask]> wrote:
>Dear Will,
>Sorry for the delay in replying. If you're still having trouble with
>this, please could you clarify how you modelled the resting state data
>and got it into the DCM? The effects of interest F-contrast I mentioned
>is used to regress out any non-task related regressors such as
>movement, and mean correct the signal, prior to ROI extraction if using
>SPM to extract ROIs.
>
>Best,
>P
>
>-----Original Message-----
>From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
>On Behalf Of Gibson, William S.
>Sent: 28 April 2015 18:36
>To: [log in to unmask]
>Subject: Re: [SPM] Confusion about DCM result
>
>Hi Peter - thanks so much for your quick reply.
>
>- I extracted ROIs based on the 2nd level T-map (one sample T test).
>Not quite sure what you mean by effects of interest F contrast. If you
>are referring to using an F contrast to look at correlations with
>canonical HRF and/or time, dispersion derivatives, then I have done
>that and the map looks basically the same as the T-map.
>
>- I went with 2 state DCM because I tested out BMS using stochastic 1
>state vs stochastic 2 state in a couple of subjects, and the 2 state
>models had dramatically (~4000) greater log-evidences.
>
>- We did not collect intracranial recordings - just fMRI.
>
>- Driving input: No, I did not include a driving input. I am wondering
>if this is the key? Last night, instead of including the effect of stim
>in the B matrix, I tried including it as a driving input (C matrix) instead.
>The log evidences for these models came out much greater. Would it make
>more sense, therefore, to include the effect of stimulation as a
>driving input as opposed to a modulatory input, and to vary the anatomical priors?
>Intuitively, this seems to make more sense to me, since what I am
>trying to test is: how does the stimulation-evoked activity travel
>through the network nodes that we have identified. Wouldn¹t the A
>matrix values represent the possible ³paths² that the
>stimulation-evoked activity is taking to travel between these nodes?
>
>Thoughts?
>
>Thanks
>
>-Will
>
>
>On 4/28/15, 3:41 AM, "Zeidman, Peter" <[log in to unmask]> wrote:
>
>>Dear Will,
>>This is indeed surprising, which probably means there are some
>>inappropriate priors somewhere. Some initial questions please:
>>
>>- How did you extract your ROIs? Did you have an effects of interest
>>f-contrast?
>>- Did you have a driving input in your DCM?
>>- Have you tried switching off two-state DCM? It shouldn't cause a
>>problem, but I'm not aware of it having been used with stochastic DCM.
>>- Were you also collecting intracranial recordings from the subject's
>>brain, or was this non-invasive?
>>
>>Best
>>Peter
>>
>>-----Original Message-----
>>From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
>>On Behalf Of Will Gibson
>>Sent: 28 April 2015 03:04
>>To: [log in to unmask]
>>Subject: [SPM] Confusion about DCM result
>>
>>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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