[pymvpa] classification based on individual parameter estimates from FSL

Fri Aug 8 15:55:40 UTC 2014

Thanks for the response, yes I tried to do a cross-subjects classification
and got some results but looked noisy (0.58 classification accuracy
only on some voxels following a  whole brain search light)....however in
the univariate
FEAT GLM I get robust activations which overlap spatially across the
different conditions.

I think the poor cross-subject classification may be noisy perhaps due to
noise in the registration to standard space.....I thought that I could
address this by trying Hyperalignment or by trying within-subject
classification at the first level, but for the later it woudl be good to
increase the number of images......

TO be clear each of individual had 8 blocks for each of the 2
classification targets,
but each of the 4 trials within the block represents are different example
withing the higher order classification target, hence this is why I though
to try and estimate an independent COPE for each despite the fixed 2 s
ISI...that would give me 32 COPES per classification target per subject
which may be sufficient for within-subject crossvalidation?

cheers
ds

On Fri, Aug 8, 2014 at 4:16 PM, Meng Liang <meng.liang at hotmail.co.uk> wrote:

> Hi David,
>
> In my opinion, the issue of 'independence' itself is not really a problem
> because they are the samples of the same classification target. However, I
> do not think that pooling the PEs for original EVs and the PEs for the time
> derivatives and treating them as the samples for the same classification
> target is a good idea. Although they were associated with the same
> event/target, they represents very different things and they are orthogonal
> in mathematical sense. It's very likely that patterns in these two types of
> PEs for the same classification target were very different, and if so, it
> would increase the noise level by pooling them together.
>
> Another concern. Your way of obtaining the data samples for the
> classification is a bit problematic to me. If I understand correctly, the
> four trials within each mini block were the same and they were separated by
> 2 sec. I assume that you defined a single EV for each trial in order to get
> a PE for each trial in your GLM model? I'm not sure whether those PEs were
> meaningful (or, say, carrying much useful information) given that the
> trials were so close to each other and the ISI was fixed - the data
> probably do not have enough power to resolve the information for each trial
> - for such rapid design, a jittered ISI would have been better.
>
> I guess you have tried to use only the 16 COPEs (8 for each classification
> target) and the results did not look good? Have you tried between-subject
> classification which would give you more samples for training the
> classifier (obviously whether a between-subject classification is suitable
> depends on what question you are studying)?
>
> Best,
> Meng
>
> ------------------------------
> Date: Fri, 8 Aug 2014 12:50:39 +0100
>
> From: d.soto.b at gmail.com
> To: pkg-exppsy-pymvpa at lists.alioth.debian.org
> Subject: Re: [pymvpa] classification based on individual parameter
> estimates from FSL
>
> hi, my thought is that, for instance, if 2 images (i.e. a PE  and its
> temporal derivative OR two basis functions)
> are associated with the same fMRI event, then it appears that wont be able
> to contribute independently to classification
> performance becos they basically relate to the same thing.
>
> In my design, for each classification target I have little blocks of 4
> trials each ---with trials separated by 2 seconds.
> Initially I used the averaged COPE for the mean across the 4 trial blocks,
> but this gave few COPES (only 8 as there are 8 mini-blocks per
> classification target per subject,
>
> which is little to do within subject classification.
>
> Hence it would be great if I could get more COPES, what am doing at the
> moment is to model each trial event within each of the blocks (plus its
> temporal derivative) so that I can get at least 4 COPES x 8 blocks= 32
> COPES per classification target for each subject, which I am hoping it may
> be sufficient to carry out kNN or SVM within subject classification.
> I am aware it is not possible to fully separate the HRF associated with
> the 4 trials of each blocks (as ISI is fixed at 2 secs)
> but given each of the 4 trials are of the same classification target, I
> thought it should be okay.
>
> Of course I could try to get each PE and  its temporal derivative for each
> of the 4 trials of each block which would give me
> 64 betas per class per subject....but I am concerned about the
> independence issue outlined above
>
> any thoughts or suggestions welcome
>
> thanks!
> ds
>
>
> On Fri, Aug 8, 2014 at 11:49 AM, Meng Liang <meng.liang at hotmail.co.uk>
> wrote:
>
> Hi David,
>
> In your case with contrasts defined as 1000, 0100, etc, the PEs and the
> corresponding COPEs should be the same, so it should not make any
> difference either using PEs or COPEs. But I don't really understand why you
> say the PEs would not be independent. Can you explain it a bit more?
>
> Best,
> Meng
>
> ------------------------------
> Date: Tue, 5 Aug 2014 16:40:39 +0100
> From: d.soto.b at gmail.com
> To: pkg-exppsy-pymvpa at lists.alioth.debian.org
> Subject: Re: [pymvpa] classification based on individual parameter
> estimates from FSL
>
>
> Hi Michael (and all), just a quick clarification on your previous response
> to my query relating classification based on individual parameter estimates
> (PEs) - you mentioned  I could use the PEs associated with the temporal
> derivative or even the PEs associated with a set of basis
> functions....however I wonder that this PEs would not be  independent (as
> would be PEs obtained from different runs)
> ....would it be okay to use those PEs anyways?
>
> A second related thing is that I have not been using the PEs  exactly but
> the Contrast of PEs (i.e. COPES in FSL)
> associated with each EV- I have 16 EVs (8 per class) and hence obtained
> COPES such that
> 1000
> 0100
> 0010
> 0001
> etc
>
> I dont see why it would make any difference to work wit COPEs rather than
> PEs, except that only with the later I could boost my dataset by using the
> temporal derivatives or basis functions....
>
> cheers
> ds
>
>
>
> On Fri, Jul 4, 2014 at 2:33 PM, Michael Hanke <mih at debian.org> wrote:
>
> Hi,
>
> On Tue, Jul 01, 2014 at 12:25:40AM +0100, David Soto wrote:
> > Hi Michael, indeed ..well done for germany today! :).
> > Thanks for the reply and the suggestion on KNN
> > I should have been  more clear that for each subject I have the
> > following *block
> > *sequences
> > ababbaabbaabbaba in TASK 1
> > ababbaabbaabbaba in TASK 2
> >
> > this explains that I have  8 a-betas and 8 b-betas for each task
> > AND for each subject..so if i concatenate & normalize all the beta data
> > across subjects I will have 8 x 19 (subjects)= 152 beta images for class
> a
> > and the same for class b
>
> Ah, I guess you model each task with two regressors (hrf + derivative?).
> You can also use a basis function set and get even more betas...
> >
> > then could I use SVM searchlight trained to discriminate a from b in
>  task1
> > betas and tested in the task2 betas?
>
> yes, no problem.
>
> Cheers,
>
> Michael
>
> PS: Off to enjoy the quarter finals ... ;-)
>
>
> --
> Michael Hanke
> http://mih.voxindeserto.de
>
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