Meg information based connectivity coding session: Difference between revisions
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Output is a text file |
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 |
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) |
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) |
The inputs do not need to be brain regions (for example can compare STE between audio track to brain region) |
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Revision as of 09:22, 11 August 2022
!Under Construction - will be finalized by Friday!
We will be meeting virtually over Zoom from 1pm-3pm with breakout rooms for questions and collaboration
There will be a quick discussion/demo (10-20mins) 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
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
SAM Symoblic Transfer Entropy Connectivity - (tutorial will be formalized by Friday)
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 #Make sure modules are loadable -- this can also be added to ~/.bashrc file module use --append /data/MEGmodules/modulefiles module load SAMsrcDEV
#This part is already done sam_cov ... sam_wts ... STEdelay ....
cp ***TESTDATA*** (fix this part) Local folder
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 "_")