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=== Install Afni on computer: ===
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html
=== Statistical Processing in AFNI ===
#! /bin/bash
# First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset
# Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp
# Example: One Sample T-test on alpha band log ratio of face power and shape power
# Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution.
# First make a list of subjects for test:
ls *_7_13_*lograt*.HEAD > alpha_list
# Then run one sample T-test
3dttest++ -setA `cat alpha_list` -prefix alpha_ttest
# Optional: add effect size to output
3dMean -prefix alpha_mean `cat alpha_list`
3dMean -sd -prefix alpha_std `cat alpha_list`
3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b'
3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc
3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc
# Could do a paired T-test between faces and shapes instead of log-ratio
ls *_7_13_*shape*.HEAD > alpha_shape_list
ls *_7_13_*face*.HEAD > alpha_face_list
3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest
# Can add covariates to T-tests.
# Can do mixed-effects ANOVAs and other fancier stuff
== MNE Python ==
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Revision as of 14:29, 24 January 2022
!!UNDER CONSTRUCTION!!
Afni Prep
Install Afni on computer:
https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html
Statistical Processing in AFNI
#! /bin/bash # First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset # Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp # Example: One Sample T-test on alpha band log ratio of face power and shape power # Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution. # First make a list of subjects for test: ls *_7_13_*lograt*.HEAD > alpha_list # Then run one sample T-test 3dttest++ -setA `cat alpha_list` -prefix alpha_ttest # Optional: add effect size to output 3dMean -prefix alpha_mean `cat alpha_list` 3dMean -sd -prefix alpha_std `cat alpha_list` 3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b' 3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc 3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc # Could do a paired T-test between faces and shapes instead of log-ratio ls *_7_13_*shape*.HEAD > alpha_shape_list ls *_7_13_*face*.HEAD > alpha_face_list 3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest # Can add covariates to T-tests. # Can do mixed-effects ANOVAs and other fancier stuff
MNE Python
Install MNE Python
conda install mamba -y mamba create -n tutorial_stats conda-forge::mne conda-forge:datalad -y conda activate tutorial_stats
Download the data
git clone ------- data repo cd ---- datalad get ./*
Additional MEG Stats Links
https://mne.tools/stable/auto_tutorials/stats-sensor-space/10_background_stats.html https://eelbrain.readthedocs.io/en/stable/getting_started.html https://neuroimage.usc.edu/brainstorm/Tutorials/Statistics https://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics/