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SPM  August 2011

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Subject:

Re: DCM analysis on intracranial EEG

From:

Vladimir Litvak <[log in to unmask]>

Reply-To:

Vladimir Litvak <[log in to unmask]>

Date:

Wed, 10 Aug 2011 18:07:17 +0100

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Dear Liang,

On Wed, Aug 10, 2011 at 5:37 PM, liang wang <[log in to unmask]> wrote:
> Thanks for your correction. I think I should use DCM for induced responses,
> but I am still confusing, though I have read several relevant papers.
>
> I do not clear know what's the main differences between DCM for evoked
> response (ERP) and for induced responses (IR). From the definition for two
> terminology, evoked response is phase-locked to the stimulus, whereas
> induced response is non-phase-locked to the stimulus. By reading SPM manual,
> I understand that DCM for IR models coupling within (i.e., linear) and
> between (i.e., non-linear) frequencies across the sources. However, I think
> this different viewpoint by looking at coupling in temporal and frequency
> domain may not clear distinguish the two DCM variants.

Lets make things simpler and define that evoked responses (a.k.a.
ERP/ERF) for the purposes of DCM are waveforms computed by averaging
EEG/MEG signals in the time domain around events of interest (which
might be stimuli or responses).  Induced responses are time-frequency
matrices computed by averaging time-frequency images of power of
EEG/MEG around events of interest. You get those images by using
Fourier analysis or something similar. There is a different sense to
evoked and induced that was recently discussed here
(http://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;e02519b7.1105) , but
lets ignore that kind of distinction for now.

>
> Also, SPM manual 16.11.1 says that "we will model the entire spectra,
> including both the evoked and induced components", which means that DCM for
> IR covers both.

That's exactly the distinction that is not very important for the
present discussion.

> I think this understanding is true if I epoch the data
> around the stimulus onset. In the paper (Chen et al., J Neuro 2010), they
> epoched the data around the movement onset, whereby they could correctly
> call induced response (non-phase-locked to the stimulus).

Induced responses can be induced by stimulus or response. DCM-IR does
not look at phase-locking. It models TF images that include both
phase-locked and non phase-locked components.

> In addition, I do
> not know what's the onset (default 60ms) used for model specification? which
> region received the external input (that paper did not show this input)?
>

Yes , input is not mentioned in the paper but it's just an omission by
the authors. There definitely were inputs in their model. I don't know
exactly how they were set up but it couldn't be any other way. 60ms is
something that is there for historical reasons and is not relevant for
DCM-IR. As I mentioned before you should look where you start getting
activity deviating from the baseline and put your input around that
time point. If your event is something like a button press then the
input will probably come before the button press.

> To my understanding, DCM for IR likes bi-directional connections. If I
> specify unidirectional connection, the model will converge very fast and
> predicted data for that region involved will be empty. Is that reasonable?

It's not necessarily a matter of unidirectional vs. bidirectional but
the model needs to be complex enough to generate interesting dynamics.
If all your connections are linear, the activity will dissipate very
fast and won't be able to fit anything. Under these circumstances the
Bayesian scheme might prefer to shrink the parameters to zero. So try
for instance making the connections nonlinear.

> I can not specify linear connection within a source (disable option), is this
> connection specified as default?

Yes. There must be at least intrinsic linear connection.

>
> Last question, could you guide me how to check how much variance is
> explained by the specified frequency modes, because Chen's paper mentioned
> this information. Otherwise, I need compute them manually.
>

You can find in DCM.xY.S the singular values corresponding to the
modes. You can use them to compute variance explained by something
like:

S = DCM.xY.S;

VE = S.^2./sum(S.^2);

Vladimir

> Sorry about a lot of questions from a new DCM explorer.
>
> Best,
> Liang
>
> 2011/8/9 Vladimir Litvak <[log in to unmask]>
>>
>> Dear Liang,
>>
>> It sounds like you are somewhat confused by the different DCM
>> variants. DCM for induced responses does not distinguish between
>> forward and backward connections, just between linear and nonlinear.
>> You should usually have some well-defined model (or a series of
>> models) to specify which are which. See Chen et al. NeuroImage,
>> 45(2):453-462, 2009 and Chen et al. J Neurosci. 23;30(25):8393-9 for
>> examples.
>>
>> SPM and DCM cannot handle variable length trials. Also since DCM for
>> induced responses models the average  response your must take a set of
>> trials that when averaged will yield something meaningful. So I
>> suggest that you select a set of trials with comparable durations and
>> cut them to the same length. Usually for induced responses your trials
>> will be longer than 200-300ms especially for areas high in the
>> hierarchy. But you can just look at your averaged TF data and select
>> the trial boundaries so that the interesting features will be
>> included. The input should be more or less at the time when the
>> activity starts departing from baseline.
>>
>> Best,
>>
>> Vladimir
>>
>> On Mon, Aug 8, 2011 at 10:18 PM, liang wang <[log in to unmask]> wrote:
>> > Hi DCM experts,
>> >
>> > I would like to use DCM to investigate information flow direction across
>> > several attention regions (mainly focusing on frontal parietal attention
>> > network). The regions selected are ventral medial frontal gyrus (vMFG),
>> > middle frontal gyrus (MFG), dorsal premotor motor cortex (dPMC),
>> > intraparietal sulcus (IPS) and inferior parietal lobe (IPL). I have a
>> > couple
>> > of questions for specifying the connections.
>> >
>> > 1) to my understanding, all the regions are high-level regions. Is that
>> > possible that I just select those regions if I have a hypothesis about
>> > the
>> > connection direction. I am not sure whether or not it is necessary to
>> > specify forward (bottom-up) and backward (top-down) connections in this
>> > situation (or leave them alone). The reason for no visual region is that
>> > the
>> > electrodes did not cover that area.
>> >
>> > 2) I want to focus on induced responses (not evoked response). Could I
>> > set
>> > the start window, like starting at 200ms? and chane 60ms onset (default
>> > setup for neural response to my understanding) to 0?
>> >
>> > 3) because of variable stimuli duration for my experiment, is it
>> > possible to
>> > make each trail have different data length? what is the minimum
>> > (restricted)
>> > data length for DCM analysis? (because sometime one of trails has very
>> > short
>> > duration, like 300ms).
>> >
>> > 4) (the last question may be not related to this topic) could you give
>> > me
>> > some advice how to specify forward or backward connection. Let me say if
>> > I
>> > have several regions including thalamus and few cortical regions. For an
>> > attention task, thalamus is an important area, partly because this area
>> > projects a lot of anatomical connections to posterior part of the brain.
>> > However, it is hard to simply specify it as low- or high-level region.
>> > To
>> > me, a simple solution is trying each possible model and compare those
>> > model
>> > based on BMC tool (too much time will be taken). Maybe there is
>> > alternative
>> > ways to easy achieve that. thanks.
>> >
>> > Please let me know if you think some steps are important for this type
>> > of
>> > analysis that I need pay more attention on.
>> >
>> > Best,
>> > Liang
>> >
>> > --
>> > Liang Wang, PhD
>> > Neuroscience of Attention and Perception Laboratory
>> > Princeton Neuroscience Institute
>> > Princeton University
>> > Princeton, NJ, 08540
>> >
>> >
>> >
>
>
>
> --
> Liang Wang, PhD
> Neuroscience of Attention and Perception Laboratory
> Princeton Neuroscience Institute
> Princeton University
> Princeton, NJ, 08540
>
>
>

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