Sorry, but I forgot to mention that the normalization results shown in Fig. II
were created using the funtional mean image. In Fig. III I was using no
mean image. I used the 200'rd out of 400'rd functional images for this
session. Of course after performing the realignment.
As You can see the results are quite similar.
I think the problem is mainly depending on the small FOV we used to aquire
the functional data.
Sincerely,
Ferenc
------------------------------------------------------------
Ferenc Acs
Lehrstuhl Prof. Dr. M. W. Greenlee
Institut für Psychologie
Universität Regensburg
93040 Regensburg
Tel. +49 (0)941 943 3582
Fax +49 (0)941 943 3233
http://www.psychologie.uni-regensburg.de/Greenlee/team/Acs/acs.html
>>> John Ashburner <[log in to unmask]> 11/29/04 12:38 >>>
Here are a few additional comments ...
The small field of view also means that the registration is likely to be less
than ideal. What works best is likely to be dependant upon your data, so I
would not like to prescribe any best approach for your data without empirical
evidence.
The limited field of view of your EPI data means that a good match can not be
obtained for the whole brain if the warps are estimated by matching an EPI
image to the EPI template. Because of this, the warps need to be
extrapolated - which is not especially sucessful.
Another contributing factor is that there is missing data in the resliced
fMRI. If spatial normalisation parameters are estimated by matching one of
these resliced images to the EPI template, then you will experience problems
because these regions have a value of zero (and so looks like the top and
bottom of the head have been sliced off). If you created a mean during the
reslicing, then using this to estimate the warps may do a better job. This
is because the mean should not have regions of missing data.
Best regards,
-John
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