Dear FSL-team,
At the moment we have preprocessed our resting state data by also using FIX. Our study involves seed-based resting-state analyses.
We have extracted the time courses from our seed and inserted this as a physiological regressor in FEAT.
We now are running the statistics in which we have age, gender and voxelwise grey matter atrophy as covariates (and masked with a grey matter mask based on FAST).
We have done the statistics via randomise by merging all cope images from FEAT and running randomise on this 4D file.
The results show that our seed is connected with every grey matter voxel in the brain!
Then we used the FEAT-GLM tool and nothing is significant when using a voxelwise correction. When running a cluster-wise correction at z-threshold=3 and p<0.001, we get some more sparse results.
But this makes us wonder what it the best way to go now? Should we use randomise or the GLM part in FEAT?
Should we opt for a voxelwise or clusterwise correction? And is the chosen threshold not too lenient?
Thanks!
Heidi & Kim
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Dr. Heidi Jacobs
Postdoc researcher
Faculty of Health, Medicine and Life Sciences
School for Mental Health and Neurosciences
Division Cognitive Neuropsychiatry and Clinical Neurosciences
Alzheimer Center Limburg
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www.maastrichtuniversity.nl
www.heidijacobs.nl
Dr. Tanslaan 12, 6229 ET Maastricht
P.O. Box 616, 6200 MD Maastricht, The Netherlands
T +31 43 38 84 090 F +31 43 38 84 092
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