[pymvpa] classification based on individual parameter estimates from FSL

Fri Aug 8 15:16:50 UTC 2014

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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