<div dir="ltr">Hi,<br><br>I feel a little bit confused with all these studies which use searchlight analysis concept, but practically make it in completely different way. Specifically:<br><br>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.<br>
<br>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<br>
<br>3. Some new stuff of Li at al. 2009 (<a href="http://www.cell.com/neuron/abstract/S0896-6273%2809%2900239-6" target="_blank">http://www.cell.com/neuron/abstract/S0896-6273(09)00239-6</a>) <br>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).<br>
<br>The Questions:<br>As far as I understand, pyMVPA searchlight doesn't run univariate GLM, but just runs classification for sphere in different locations?<br>An assumption, that I have to feed it with block design data. unless I am using ERNiftiDataset, which is under development?<br>
I would appreciate if you can send any paper reference on how ERNiftiDataset extracts events from fast ER design.<br><br>Thanks a lot,<br>Vadim<br>
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