Thanks, a follow-up question, should we use intensity normalization instead?

On Fri, Apr 9, 2010 at 4:40 PM, Stephen Smith <[log in to unmask]> wrote:

Hi - using confounds can indeed help, though we wouldn't recommend global

mean timeseries for this.   Better options are confound timeseries from

physiological monitoring, head motion parameters, and timeseries derived

from conservative white matter and CSF masks / voxels.

Cheers.

On 7 Apr 2010, at 16:55, Ciara McCabe wrote:

Dear FSl

I have recently completed resting state analysis using the hypothesis driven

approach using featquery to get the timeseries and then using this in the

FEATS as a regressor  then group differences

Im wondering though if I need to go back and use a global time series as

another regressor in each lower level feat to remove noise such as breathing

etc?

any help much appreciated

Thanks

Ciara

---------------------------------------------------------------------------

Stephen M. Smith, Professor of Biomedical Engineering

Associate Director,  Oxford University FMRIB Centre

FMRIB, JR Hospital, Headington, Oxford  OX3 9DU, UK

+44 (0) 1865 222726  (fax 222717)

[log in to unmask]    http://www.fmrib.ox.ac.uk/~steve

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Xue, Feng Phd. candidate
Major in Developmental Cognitive Neuroscience

National Key Laboratory of Cognitive Neuroscience and Learning
Beijing Normal University
Beijing, China. 100875
Tel: +86-13810154455
web: http://psychbrain.bnu.edu.cn
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