Fumiko,
I’m not convinced that this outcome
is primarily due to the mismatch between the characteristics of your study
population and the template (although it might be aggravated by it):
Is it possible that you didn’t set
the origin before preprocessing the data? In our experience the unified
segmentation approach is pretty vulnerable to end up with such deformed results
if you don’t give the program a reasonable starting point. Assuming that the
data quality is o.k., my best guess would be: reset the origin to AC, rerun data
preprocessing for these subjects, and you’re done.
If you work with very large sample sizes:
Christian Gaser’s wrote a tool to check for sample inhomogenities of
segmentation results that proved to be very useful in this context. My
colleague Fabio Sambataro adapted the code so that it runs under SPM5, and added
some other useful features ([log in to unmask]).
Best,
Heike
Heike
Tost, M.D. Ph.D.
Post-Doctoral
Research Fellow
Unit for Systems
Neuroscience in Psychiatry
Genes, Cognition,
and Psychosis Program
National Institute
of Mental Health, National Institutes of Health
10-3C103, 9000 Rockville Pike,
Phone: 301-594-9514, Fax: 301-480-0169
From: Fumiko Hoeft
[mailto:[log in to unmask]]
Sent: Tuesday, April 24, 2007 2:56
PM
To: [log in to unmask]
Subject: [SPM] VBM5 problem in
young children
Dear experts,
We are performing VBM5 (Dr. Christian Gasser's scripts) in
SPM5.
The ages of the subjects are 1-3 years old.
The segmented images come out like this (please see attached
jpg of brains with modulation, segmentation and normalization applied; the left
brain is the one with the problem, the right brain is for comparison).
Can you give us some suggestions on how we can improve the
segmentation?
Thank you in advance.
Best,
Fumiko
--
Fumiko Hoeft, MD, PhD
Stanford University School of Medicine
Center for Interdisciplinary Brain Sciences Research
401 Quarry Rd. Stanford CA USA 94305-5795
Tel: +1 (650) 245-7016
Fax: +1 (650) 724-4794
Email: [log in to unmask]
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