[pymvpa] Balancing strategy

[Chris Johnson]

On 11/26/2013 04:11 PM, Chris Johnson wrote:
> On 11/25/2013 04:39 PM, Yaroslav Halchenko wrote:
>>
>> On Mon, 25 Nov 2013, Chris Johnson wrote:
>>> I have an experimental (fMRI) paradigm wherein the conditions were
>>> balanced across runs, and performed a searchlight MVPA analysis with
>>> leave-one-run-out cross validation. In some subjects, however, we found
>>> excessive head motion and had to eliminate scans
>>
>> before jumping to rebalancing:
>>
>> was such periods motion associated just with few consecutive volumes?
> 
> They're typically isolated. To be clear, we're using a sparse scanning
> paradigm (e.g.
> http://www.frontiersin.org/Brain_Imaging_Methods/10.3389/fnins.2013.00055/abstract),
> so this is not surprising.
> 
> In some cases there are multiple in a row, but the effect is that each
> modeled event impacts only two scans, leaving the events relatively
> unconstrained. Attempting to remove these scans from the GLM actually
> ends up causing artifacts in surrounding scans due to the reduced
> constraints.
> 
> We thus decided that it would be best to use standard GLM modeling and
> simply remove the events that are measured in motion-outlier scans.
> 
>> if so -- and if you are using some GLM modeling first to extract betas
>> for your MVPA analysis -- you could also 'drop' those volumes by adding
>> additional single-time-point regressors into your GLM model, so that
>> would account for those volumes effects, while still providing you
>> estimates for the explanatory variables of interest.  This way you might
>> avoid eliminating complete trials from your further MVPA and thus not
>> requiring re-ballancing

Just bumping this thread. Anybody have any opinions with regard to
balancing strategy? Or possibly I haven't adequately explained the
reasoning for dropping trials?

-- 
Chris Johnson

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