[pymvpa] searchlight concept and pyMVPA

[Michael Hanke]

Hi Vadim,

On Fri, May 15, 2009 at 12:19:45PM +0200, Vadim Axel wrote:
> Hi,
> 
> I feel a little bit confused with all these studies which use searchlight
> analysis concept, but practically make it in completely different way.
> Specifically:
> 
> 1. Kriegeskorte et al 2006 (and Kriegeskorte et al 2007) neither talked
> about classification, nor SVM. His proposal was to take the output (contrast
> image between two conditions of a standard GLM for unsmoothed data), to
> define a sphere, make some smart average in this sphere and then iterate
> over all possible spheres in order to find which spheres pass the threshold
> (aka significant regions). No classification, no raw data.
> 
> 2. Haynes et al 2007 or Soon et al. 2008 ("mind reading"), extends
> Kriegeskorte by taking beta vectors per condition / session and by using SVM
> for a iterating sphere finds regions, which result in best classification.
> No raw data and classification is based on some 10 data points per
> condition. The similar methodology I think was used here as well: Hassabis
> et al. 2009
> 
> 3. Some new stuff of Li at al. 2009 (
> http://www.cell.com/neuron/abstract/S0896-6273(09)00239-6<http://www.cell.com/neuron/abstract/S0896-6273%2809%2900239-6>)
> 
> They used Kriegeskorte searchlight method (and code as well) but then they
> employed SVM to classify searchlight regions on averaged two volumes data
> points. Given that their design was fast ER I am not fully understand how
> this classification worked "out of the box" (I haven't succeed to dig any
> details from the paper).
> 
> The Questions:
> As far as I understand, pyMVPA searchlight doesn't run univariate GLM, but
> just runs classification for sphere in different locations?
> An assumption, that I have to feed it with block design data. unless I am
> using ERNiftiDataset, which is under development?

Searchlight in PyMVPA is simply an abstraction of the idea of computing
_something_ in any sphere of a given size in the dataset. To that end it
doesn't matter what you want to compute. It could be a a simple GLM on
the mean signal, a SVM classification accuracy, or something totally
different -- any (in PyMVPA terminology) DatasetMeasure() should work.

> I would appreciate if you can send any paper reference on how ERNiftiDataset
> extracts events from fast ER design.

Hmm, paper .... there is none (yet). Currently, the best would probably
be to take a look at the searchlight examples in the docs:

  http://www.pymvpa.org/examples/searchlight_minimal.html
  http://www.pymvpa.org/examples/searchlight_2d.html
  http://www.pymvpa.org/examples/searchlight_dsm.html

ERNiftiDataset simply extracts boxcar-shaped volume series from a 4d
timeseries, defined by onset and duration of events. What you do with
those boxcars is up to you. You could time-average them, or do more (or
even less) fancy things, before feeding them into a classifier.

We are thinking about writing a manuscript that shows how to do that
with a really fastER design -- but that is work in progress and doesn't
even have the highest priority ATM.

HTH,

Michael


-- 
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