Meg information based connectivity coding session: Difference between revisions
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!Under Construction - will be finalized by Friday! |
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We will be meeting virtually over Zoom from 1pm-3pm with breakout rooms for questions and collaboration |
We will be meeting virtually over Zoom from 1pm-3pm with breakout rooms for questions and collaboration <br> |
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There will be a quick discussion |
There will be a quick discussion and some example data on biowulf. <br> |
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FRITES toolbox |
==FRITES toolbox== |
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[https://megcore.nih.gov/MEG/FRITES_Tutorial_07132022.mp4 Club MEG Presentation Recording] |
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[https://brainets.github.io/frites/ Background] |
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[https://github.com/brainets/CookingFrites Getting Started Scripts] |
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[https://brainets.github.io/frites/auto_examples/index.html Example Scripts] |
[https://brainets.github.io/frites/auto_examples/index.html Example Scripts] |
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[ ] |
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===Install in new environment (RECOMMENDED!):=== |
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conda create -n frites conda-forge::mne conda-forge::jupyterlab pip -y |
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conda activate frites |
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pip install frites |
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git clone https://github.com/brainets/CookingFrites.git |
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SAM Information Based Connectivity - (tutorial will be formalized by Friday) |
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cd CookingFrites |
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jupyter lab |
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#Once jupyter is open, the notebooks are in the notebooks folder |
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#For MEG data to use with this --- Just test data, there are many optimizations that have not been performed |
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pip install git+https://github.com/jstout211/connectivity_tutorial.git |
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cp /vf/users/MEGmodules/modules/frites_dset.zip ./ |
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unzip frites_dset.zip |
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#In python, frites_output_root=$(pwd)/frites_dset |
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ipython |
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from frites_connectivity_tutorial.load_data import load_dataset |
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dt = load_dataset(<<FRITES_OUTPUT_ROOT>>) |
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===Install in current environment (Better to use New environment as listed above!)-=== |
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#Download getting started tutorials |
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git clone https://github.com/brainets/CookingFrites.git |
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pip install frites |
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===Use on biowulf=== |
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sinteractive --mem=12G --cpus-per-task=8 |
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module use --append /data/MEGmodules/modulefiles |
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module load mne_spyder #loads mne/mne-bids/frites/frites_tutorial |
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==SAM Symoblic Transfer Entropy Connectivity == |
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⚫ | |||
[https://megcore.nih.gov/MEG/Robinson_InformationTheoryMEG_ClubMEG_03102022.mp4 Club MEG SAM Connectivity Presentation Recording] |
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====Recommendations==== |
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This process is a trigger-free process that estimates the symbolic transfer entropy (connectivity assessed by delayed propagation of signals) between two ROIs |
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70-185Hz has been tested and is recommended as a filter band |
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Use an atlas based input for the bivariate connectivity assessment (AAL atlas, Desikan K., Destreux...) |
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SAMwts or Patch_wts to create the beamformer |
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Output is a text file |
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Must determine an embedding dimension (4 as default) - the higher the embedding the more data needed |
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Minimal requirements (>=1Khz sampling rate and >=300s of data) |
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The inputs do not need to be brain regions (for example can compare STE between audio track to brain region) |
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===Use on Biowulf=== |
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#Log into biowulf |
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sinteractive --mem=6G --cpus-per-task=4 #You may need more mem/cpus |
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#Make sure modules are loadable -- this can also be added to ~/.bashrc file |
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module use --append /data/MEGmodules/modulefiles |
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module load SAMsrcDev |
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#Make the SAM beamformers and apply the symbolic transfer entropy |
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sam_cov ... |
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sam_wts ... |
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STEdelay .... |
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Usage: STEdelay [options] Version 5.0 (64-bit) rev-1, Aug 10 2022 |
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Options: |
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If a parameter begins with '--', it is allowed on the command line, |
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otherwise it is only allowed in a parameter file (see -m). |
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All times are in seconds. |
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-h, --help show this help |
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-r DSNAME, --DataSet DSNAME |
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MEG dataset name [env_var = ds] |
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-m PFILE, --param PFILE parameter file name (optionally ending |
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in ".param") [env_var = param] |
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-v, --verbose verbose output |
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--CovType GLOBAL|SUM|ALL which covariance matrix to use for the analysis |
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--CovBand LO HI covariance bandwidth limits in Hz |
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--ImageBand LO HI imaging covariance bandwidth limits in Hz |
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--OrientBand LO HI band to use for orientation instead of Global (Hz) |
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--SmoothBand LO HI used to smooth Hilbert envelope, kurtosis, or RVE (Hz) |
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--Notch apply a powerline notch |
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(including harmonics) |
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--Hz HZ frequency for powerline (Hz, default 60) |
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ImageMetric METRICSPEC imaging metric |
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--Extent DIST radial extent from ROI voxels or centroid (mm) |
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-t TARGETFILE, --TargetName TARGETFILE |
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file containing target coordinates |
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-i MRIDIR, --MRIDirectory MRIDIR |
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MRI directory root [env_var = mridir] |
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-o IMAGEDIR, --ImageDirectory IMAGEDIR |
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image output directory [env_var = imagedir] |
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--PrefixLength N DataSet prefix may be specified as a number |
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or as a prefix delimiter (default "_") |
Latest revision as of 12:58, 12 August 2022
We will be meeting virtually over Zoom from 1pm-3pm with breakout rooms for questions and collaboration
There will be a quick discussion and some example data on biowulf.
FRITES toolbox
Club MEG Presentation Recording
Background Getting Started Scripts Example Scripts
Install in new environment (RECOMMENDED!):
conda create -n frites conda-forge::mne conda-forge::jupyterlab pip -y conda activate frites pip install frites
git clone https://github.com/brainets/CookingFrites.git cd CookingFrites jupyter lab #Once jupyter is open, the notebooks are in the notebooks folder
#For MEG data to use with this --- Just test data, there are many optimizations that have not been performed pip install git+https://github.com/jstout211/connectivity_tutorial.git cp /vf/users/MEGmodules/modules/frites_dset.zip ./ unzip frites_dset.zip
#In python, frites_output_root=$(pwd)/frites_dset ipython from frites_connectivity_tutorial.load_data import load_dataset dt = load_dataset(<<FRITES_OUTPUT_ROOT>>)
Install in current environment (Better to use New environment as listed above!)-
#Download getting started tutorials git clone https://github.com/brainets/CookingFrites.git pip install frites
Use on biowulf
sinteractive --mem=12G --cpus-per-task=8 module use --append /data/MEGmodules/modulefiles module load mne_spyder #loads mne/mne-bids/frites/frites_tutorial
SAM Symoblic Transfer Entropy Connectivity
Background
Club MEG SAM Connectivity Presentation Recording
Recommendations
This process is a trigger-free process that estimates the symbolic transfer entropy (connectivity assessed by delayed propagation of signals) between two ROIs 70-185Hz has been tested and is recommended as a filter band Use an atlas based input for the bivariate connectivity assessment (AAL atlas, Desikan K., Destreux...) SAMwts or Patch_wts to create the beamformer Output is a text file Must determine an embedding dimension (4 as default) - the higher the embedding the more data needed Minimal requirements (>=1Khz sampling rate and >=300s of data) The inputs do not need to be brain regions (for example can compare STE between audio track to brain region)
Use on Biowulf
#Log into biowulf sinteractive --mem=6G --cpus-per-task=4 #You may need more mem/cpus #Make sure modules are loadable -- this can also be added to ~/.bashrc file module use --append /data/MEGmodules/modulefiles module load SAMsrcDev
#Make the SAM beamformers and apply the symbolic transfer entropy sam_cov ... sam_wts ... STEdelay ....
Usage: STEdelay [options] Version 5.0 (64-bit) rev-1, Aug 10 2022 Options: If a parameter begins with '--', it is allowed on the command line, otherwise it is only allowed in a parameter file (see -m). All times are in seconds. -h, --help show this help -r DSNAME, --DataSet DSNAME MEG dataset name [env_var = ds] -m PFILE, --param PFILE parameter file name (optionally ending in ".param") [env_var = param] -v, --verbose verbose output --CovType GLOBAL|SUM|ALL which covariance matrix to use for the analysis --CovBand LO HI covariance bandwidth limits in Hz --ImageBand LO HI imaging covariance bandwidth limits in Hz --OrientBand LO HI band to use for orientation instead of Global (Hz) --SmoothBand LO HI used to smooth Hilbert envelope, kurtosis, or RVE (Hz) --Notch apply a powerline notch (including harmonics) --Hz HZ frequency for powerline (Hz, default 60) ImageMetric METRICSPEC imaging metric --Extent DIST radial extent from ROI voxels or centroid (mm) -t TARGETFILE, --TargetName TARGETFILE file containing target coordinates -i MRIDIR, --MRIDirectory MRIDIR MRI directory root [env_var = mridir] -o IMAGEDIR, --ImageDirectory IMAGEDIR image output directory [env_var = imagedir] --PrefixLength N DataSet prefix may be specified as a number or as a prefix delimiter (default "_")