This is a problem that others have struggled with as well.
I agree that choosing to repeat the same statistical analysis in the
clustered blobs to see if there are subdivisions might be hard to argue.
Have you tried using MELODIC rather than the standard FSL method?
It may be that the subdivisions explain different aspects of the
variance and therefore a principal components approach may tease out the
subcomponents quite naturally.
It may also be the case that we should try out the method in
A Multimodal Approach to Representational Similarity Analysis by Li Su
et al.
http://www.mrc-cbu.cam.ac.uk/people/nikolaus.kriegeskorte/rsa.html
Gary
Gary Green
York Neuroimaging Centre
The Biocentre
York Science Park
Innovation Way
Heslington
York
YO10 5DG
http://www.ynic.york.ac.uk
https://www.ynic.york.ac.uk/about-us/people/ggrg
tel. +44 (0) 1904 435349
PA - Claire Fox : +44 (0) 1904 435329 or Claire.Fox(a)ynic.york.ac.uk
fax +44 (0) 1904 435356
mobile +44 (0) 788 191 3004
philip quinlan wrote:
Hi
Together with Andre and Nikos, we have collected some potentially very
interesting results via an fMRI study on task switching.
The issue is this. When we do group analyses with cluster correction,
3 very large blobs are found in contrasting cond 1 with cond 2.
However, in playing with fslview it is clear that within these blobs
there are more interesting localised islands of activity.
My dilemma is this if we simply report the cluster-corrected blobs that
is okay but what
I really want to discuss are these other ROIs.
So how do I get this published and satisfy the statisitcal reviewer?
I cannot do a simple a priori ROI analysis because some of the
conditions are relatively novel and we cant predict where the ROIs
might be.
We can do this via email or I can talk about this at YNiC next week
perhaps?
Philip.
********************************************************************
Philip Quinlan E-Mail: ptq1(a)york.ac.uk
Department of Psychology FAX: (01904) 323181
The University of York Tel: (01904) 320000 Ext. 3135
Heslington Direct : (01904) 323135
York
YO10 5DD
U.K.
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