Meg information based connectivity coding session: Difference between revisions

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!Under Construction - will be finalized by Friday!


We will be meeting virtually over Zoom from 1pm-3pm with breakout rooms for questions and collaboration <br>
We will be meeting virtually over Zoom from 1pm-3pm with breakout rooms for questions and collaboration <br>
There will be a quick discussion/demo (10-20mins) and some example data on biowulf. <br>
There will be a quick discussion and some example data on biowulf. <br>


==FRITES toolbox==
==FRITES toolbox==
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jupyter lab
jupyter lab
#Once jupyter is open, the notebooks are in the notebooks folder
#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!)-===
===Install in current environment (Better to use New environment as listed above!)-===
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pip install frites
pip install frites


===Use on biowulf===
==SAM Symoblic Transfer Entropy Connectivity - (tutorial will be formalized by Friday)==
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===
===Background===
[https://megcore.nih.gov/MEG/Robinson_InformationTheoryMEG_ClubMEG_03102022.mp4 Club MEG SAM Connectivity Presentation Recording]
[https://megcore.nih.gov/MEG/Robinson_InformationTheoryMEG_ClubMEG_03102022.mp4 Club MEG SAM Connectivity Presentation Recording]
====Recommendations====
====Recommendations====
This process is a trigger-free process that estimates the symbolic transfer entropy (connectivity assessed by delayed propagation of signals) between two ROIs
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
70-185Hz has been tested and is recommended as a filter band
Use an atlas based input for the bivariate connectivity assessment
Use an atlas based input for the bivariate connectivity assessment (AAL atlas, Desikan K., Destreux...)
SAMwts or Patch_wts to create the beamformer
SAMwts or Patch_wts to create the beamformer
Output is a text file
Output is a text file
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
Minimal requirements (1Khz sampling rate and 300s of data)
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)
The inputs do not need to be brain regions (for example can compare STE between audio track to brain region)


===Use on Biowulf===
===Use on Biowulf===
#Log into biowulf
#Log into biowulf
sinteractive --mem=6G --cpus-per-task=4
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
#Make sure modules are loadable -- this can also be added to ~/.bashrc file
module use --append /data/MEGmodules/modulefiles
module use --append /data/MEGmodules/modulefiles
module load SAMsrcDEV
module load SAMsrcDev


#Make the SAM beamformers and apply the symbolic transfer entropy
#This part is already done
sam_cov ...
sam_cov ...
sam_wts ...
sam_wts ...
STEdelay ....
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 "_")

Latest revision as of 13:58, 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 "_")