JiscMail Logo
Email discussion lists for the UK Education and Research communities

Help for SPM Archives


SPM Archives

SPM Archives


SPM@JISCMAIL.AC.UK


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

SPM Home

SPM Home

SPM  June 2014

SPM June 2014

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

Re: stochastic DCM

From:

Brian Numelin Haagensen <[log in to unmask]>

Reply-To:

Brian Numelin Haagensen <[log in to unmask]>

Date:

Mon, 2 Jun 2014 13:22:56 +0200

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (86 lines)

Dear Peter,

thanks for the reply.
In this case, it's an event-related reversal-learning paradigm, divided 
into 4 sessions according to a 2*2 design with varying stimulus-type 
(dimension 1) and valence of feedback (dimension 2). We model Prediction 
Errors as parametric modulation on the outcome onsets.
We find that the Prediction Error effect changes sign in one region, 
according to stimulus-type and valence. We then seek out the source of 
this change in sign by comparing connectivity between this region and 
two cortical regions that show constant effect of Prediction Error 
across all dimensions. So, we have Prediction Error as direct input to 
these cortical regions and a block corresponding to stimulus-type and 
valence as the modulatory input. Based on 2nd level parametric effects 
we would say that the observed neural activity is due to the experiment.

Concerning ROIs: we extract using a mask based on aal and the 2nd level 
SPM{t} - typically around 80 voxels. We include all voxels per subject 
within this mask (threshold set to 1). The % variance explained by the 
1st PC was always between 70 - 90. The inclusion of all voxels might be 
a problem wrt. noise- but we've also done DCM in another event-related 
paradigm, where we used 0.05 uncorrected at individual SPM{F} maps of 
the effects of interest and spheres on the peak, and also in this case 
we had flat-lines (or when changing hyper-priors we could avoid 
flat-lines, but still only modest variance explained around 5-15 %).

So, we think the issue might more be related to the application of DCM 
on event-related designs- the GLM result may average noise out looking 
at the entire time-series, but when trying to model trial-wise dynamics, 
there might be a problem for deterministic DCM, as noise is also very 
much present in the trial-wise dynamics? This was our rationale behind 
using the stochastic approach.

Concerning the use on non-linear DCM, our thought was to exploit the 
shorter integration time step- so we just have a D-matrix of zeros, and 
would still primarily be interested in the B-matrix.

Best regards, Brian






Den 02-06-2014 11:16, Zeidman, Peter skrev:
> Dear Brian,
> I think it would be good to try to diagnose why your model estimation isn't getting off the ground before turning to stochastic DCM. Some questions:
>
> - What kind of task are your participants performing, and what's the experimental design? Do you think the neural activity you observe is caused by your experimental manipulations, or by endogenous activity? These considerations will have a big impact on the success of your models. E.g. if you had an autobiographical memory recall task over many seconds, it might be fair to argue that most activity is caused endogenously rather than by your cue, and thus stochastic DCM would be favourable.
>
> - You say you get robust 1st level main effects. How are you defining your ROIs? Based on single-subject activation clusters? Or anatomically?
>
> I'm not sure about if you'll get an advantage from non-linear DCM - it depends on whether you hypothesise a region modulates a connection. Give it a try if you think it makes sense. You could also try 2-state DCM, which has richer dynamics and so might stand a  better chance of fitting your data.
>
> Best,
> Peter.
>
> Peter Zeidman, PhD
> Methods Group
> Wellcome Trust Centre for Neuroimaging
> 12 Queen Square
> London WC1N 3BG
> [log in to unmask]
>
>
>
>
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On Behalf Of Brian Haagensen
> Sent: 31 May 2014 14:04
> To: [log in to unmask]
> Subject: [SPM] stochastic DCM
>
> Dear DCM experts,
>
> we apply bilinear DCM for event-related fMRI data, but often see that the model flat-lines, also with more tight hyper-priors than the default ones. This is the case also for quite robust 1st level main effects of the inputs.
>
> What is the opinion among you on the use of stochastic DCMs in a case like this, ie. where we also have known inputs to the system? My own thoughts would be that if we're interested in inference on model space, this approach would be ok, but I'm more in doubt concerning inference on model parameters- because noise explains so much of data, the posterior parameter estimates are very small and in our case typicaly having posterior probabilities around 0.5. So I guess stats and correlations on these would be problematic?
>
> A more preferable option if we're interested in the parameters might be to use the nonlinear integration scheme with its shorter time step, because our modulatory input (multiplied on B) is event-related (more like neural activity multiplied on D) - do you have some thoughts on this ?
>
> Thanks for your time!
>
> Best regards, Brian Haagensen
>

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

March 2024
February 2024
January 2024
December 2023
November 2023
October 2023
September 2023
August 2023
July 2023
June 2023
May 2023
April 2023
March 2023
February 2023
January 2023
December 2022
November 2022
October 2022
September 2022
August 2022
July 2022
June 2022
May 2022
April 2022
March 2022
February 2022
January 2022
December 2021
November 2021
October 2021
September 2021
August 2021
July 2021
June 2021
May 2021
April 2021
March 2021
February 2021
January 2021
December 2020
November 2020
October 2020
September 2020
August 2020
July 2020
June 2020
May 2020
April 2020
March 2020
February 2020
January 2020
December 2019
November 2019
October 2019
September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
2006
2005
2004
2003
2002
2001
2000
1999
1998


JiscMail is a Jisc service.

View our service policies at https://www.jiscmail.ac.uk/policyandsecurity/ and Jisc's privacy policy at https://www.jisc.ac.uk/website/privacy-notice

For help and support help@jisc.ac.uk

Secured by F-Secure Anti-Virus CataList Email List Search Powered by the LISTSERV Email List Manager