Hi Yoonho,
the steps look good. Take a look at
https://surfer.nmr.mgh.harvard.edu/fswiki/LinearMixedEffectsModels
which basically describes exactly these steps for the case of linear
mixed effects models. Note that we have dedicated code for that as part
of FreeSurfer (see also the references explaining why this code is
great, e.g. "Spatiotemporal Linear .... Neuroimage 2013")
Also, you can do a more powerful 2stage FDR correction using
lme_mass_FDR2 in matlab directly (best to combine hemisphers for that,
see wiki).
cheers, Martin
On 05/24/2016 01:55 PM, Chung, Yoonho wrote:
Hi - If I would like to try to perform mass-univariate statistics at
the vertex level (thickness, area, vol at each vertex) without using
the mri_glmfit, is this order a sound step (in general) for mapping
stats parameter of choice (p-val or t s or weights for example) to
the surface?
1. Use mris_preproc and surf2surf to get the data to common space and
smooth.
2. Load the data to matlab using fs_read_Y() or MRIread
3. Perform stats at each vertex using stats toolbox to apply modeling
approach of your choice (e.g., machine learning, linear mixed model etc.)
4. Extract P-values (or other parameters) for each vertex for the
variable of primary interest
5. Use MRIwrite to create mgh file
6. Perform multiple comparison using freesurfer functions (e.g.,
FDR or monte carlo sim)
7. view corrected maps using tksufer?
Any steps I should consider adding or avoid?
Thank you!
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--
Martin Reuter, PhD
Assistant Professor of Radiology, Harvard Medical School
Assistant Professor of Neurology, Harvard Medical School
A.A.Martinos Center for Biomedical Imaging
Massachusetts General Hospital
Research Affiliate, CSAIL, MIT
Phone: +1-617-724-5652
Web : http://reuter.mit.edu
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