[pymvpa] group-level analysis differences vs. pattern classification

Vadim Axel axel.vadim at gmail.com
Mon Sep 23 09:23:37 UTC 2013


There are two commonly used approaches to analyze the data of the
experiment below:

Simple design with two conditions (A and B), which  both activate large
network of well established regions (e.g. conjunction analysis of A >
baseline and B > baseline). The question is whether we can find neural
correlates of difference between the two. Direct group-level analysis
comparison between A and B results in small activations (~5% of volume
comparing to commonality of conjunction analysis) and these activations are
located mostly outside the main network, all over the brain. Given that the
result is dependent on p-value threshold, it looks like a classical
blobology.  Another approach is to select (independently) the ROIs of the
common network nodes and to run MVPA. With this analysis I successfully
discriminate between the two conditions. So, two people analyzing the same
data can draw absolutely different conclusions: one would say, that small
regions X, Y, Z are the regions, which discriminate between conditions A
and B; the other, in contrast, would say that since both A and B activate
common network, the difference between the two lies within this network
(different patterns of activity).

What approach is more reliable in your opinion?

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