Helmut is right, you don't want to fill in the mask as the data will
be artificially set at 0 in the first-level analysis.
An alternative to the mask issue, besides changing the implicit
setting in the first-level model, is to use GLM Flex as it can handle
missing data.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Postdoctoral Research Fellow, GRECC, Bedford VA
Website: http://www.martinos.org/~mclaren
Office: (773) 406-2464
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On Sat, Aug 10, 2013 at 7:07 AM, H. Nebl
<[log in to unmask]> wrote:
> Filling the holes is no good idea at all. If you work on the masks after model estimation and fill the holes, then you treat the subjects as if they had a beta of 0 in this particular voxel. But this does not have to be the case at all, just your signal is very low.
>
> You should rather think about why you have signal dropout in some subjects only. Using the same slice orientation should result in rather similar dropouts in general. There are some methods papers dealing with regions which are prone to susceptibilitiy artefacts (amygdala, OFC) and how to reduce dropouts. Maybe a fieldmap can help as well.
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