Difference between revisions of "Tutorial stats 011422"

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!!UNDER CONSTRUCTION!!
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=!!UNDER CONSTRUCTION!!=
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== Tutorial Session ==
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Recording Download: [https://megcore.nih.gov/MEG/ClubMEG_Carver&Stout_MNE&AFNI-GroupAnalysisTutorial_012122.mp4 MNE & AFNI Group Analysis Tutorial (mp4)]
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== Afni Prep ==
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=== Install Afni on computer: ===
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https://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html
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=== Statistical Processing in AFNI ===
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#! /bin/bash
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# First things first: Good practice to convert .nii files to afni .HEAD .BRIK format: 3dcopy mydataset.nii myafnidataset
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# Here the data have been converted to common space (+tlrc) but if not can be done in afni with adwarp
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# Example: One Sample T-test on alpha band log ratio of face power and shape power
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# Why log-ratio? log(faces/shapes) = log(faces) - log(shapes) ; log attentuates outliers , normalizes distribution.
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# First make a list of subjects for test:
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ls *_7_13_*lograt*.HEAD > alpha_list
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# Then run one sample T-test
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3dttest++ -setA `cat alpha_list` -prefix alpha_ttest
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# Optional: add effect size to output
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3dMean -prefix alpha_mean `cat alpha_list`
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3dMean -sd -prefix alpha_std `cat alpha_list`
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3dcalc -prefix alpha_effectsize -a alpha_mean+tlrc -b alpha_std+tlrc -expr 'a/b'
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3dbucket -prefix alpha_ttest_effectsize alpha_ttest+tlrc alpha_effectsize+tlrc
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3drefit -sublabel 2 "effectsize" alpha_ttest_effectsize+tlrc
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# Could do a paired T-test between faces and shapes instead of log-ratio
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ls *_7_13_*shape*.HEAD > alpha_shape_list
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ls *_7_13_*face*.HEAD > alpha_face_list
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3dttest++ -setA `cat alpha_face_list` -setB `cat alpha_shape_list` -paired -prefix alpha_paired_ttest
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# Can add covariates to T-tests.
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# Can do mixed-effects ANOVAs and other fancier stuff
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== MNE Python ==
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=== Install MNE Python ===
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conda install mamba -y
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mamba create -n tutorial_stats conda-forge::mne conda-forge:datalad -y
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conda activate tutorial_stats
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=== Download the data ===
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Will Update soon with appropriate paths etc.
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git clone ------- data repo
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cd ----
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datalad get ./*
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=== Additional MEG Stats Links ===
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https://mne.tools/stable/auto_tutorials/stats-sensor-space/10_background_stats.html
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https://eelbrain.readthedocs.io/en/stable/getting_started.html
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https://neuroimage.usc.edu/brainstorm/Tutorials/Statistics
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https://www.fieldtriptoolbox.org/tutorial/eventrelatedstatistics/

Latest revision as of 17:58, 24 January 2022

!!UNDER CONSTRUCTION!!

Tutorial Session

Recording Download: MNE & AFNI Group Analysis Tutorial (mp4)

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

Will Update soon with appropriate paths etc.

 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/