Difference between revisions of "ENIGMA MEG Working Group"

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UNDER CONSTRUCTION
 
UNDER CONSTRUCTION
====Enigma Project - MEG working group====
 
The enigma project is a large scale neuroimaging project to leverage data across multiple institutes to identify neuroimaging findings that are generally not possible at a single institute. <br>
 
http://enigma.ini.usc.edu/
 
   
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[http://enigma.ini.usc.edu/ The ENIGMA Consortium]
====MEG Working group====
 
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[http://enigma.ini.usc.edu/ongoing/enigma-meg-working-group/ The ENIGMA MEG Working Group]
LIST OF PARTICIPATING INSTITUTES
 
  +
==Enigma Project - MEG working group==
  +
The goal of the ENIGMA MEG Working Group is to explore the spatiotemporal patterns of brain oscillatory activity, and determine how these patterns relate to age, gender, and common genetic variants. Data from numerous modalities have demonstrated a hierarchical organization of the brain from sensory systems to higher cortical areas. There is some evidence that this hierarchical organization may also be reflected in the spatial distribution of intrinsic timescales of activity. This ENIGMA working group intends to explore these patterns across the lifespan. To achieve the highest possible number of subjects, this needs to be done by meta-analysis.<br>
   
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[mailto:nugenta@nih.gov Allison Nugent, PhD], ENIGMA MEG Chair <br>
====Data Analysis Format====
 
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[mailto:jeff.stout@nih.gov Jeff Stout, PhD], Lead Scientist <br>
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[mailto:anna.namyst@nih.gov Anna Namyst], Project Coordinator</br>
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==MEG Working Group: Participating Institutions==
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* NIH/NIMH
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* NIH/NICHD
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* University of Pittsburgh
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* Aston University, UK
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* Children's Hospital of Philadephia
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* University of Pittsburgh
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* Universiti Teknologi PETRONAS
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* Advent Health for Children
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* McGill University, Montreal Neurological Institute
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* University of Texas Southwestern
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* IRCCS San Camillo Hospital Venice
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* University of Alabama, Birmingham
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* Cambridge University
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* Salisbury VA Medical Center, Wake Forest University, Wake Forest School of Medecine
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* University of Jyväskylä, Finland
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* The Mind Research Network
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* DNISC (Università degli Studi G.D'Annunzio Chieti Pescara)
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* New York University and NYU Abu Dhabi
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* Arkansas Childrens Hospital
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* Nemours DuPont Hospital for Children and Thomas Jefferson University
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* Johannes Gutenberg University, Mainz, Germany (Biomedical statistics and multimodal signal processing Unit)
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* University of Göttingen + University of Tübingen
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* Macquarie University
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* National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
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* NeuroSpin
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* Massachusetts General Hospital
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* Precision Medicine Centre, Hokuto Hospital
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* Precision Medicine Centre, Kumagaya General Hospital
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* Universiti Sains Malaysia
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If you are interested in participating in this project, please email the project coordinator, [mailto:anna.namyst@nih.gov Anna Namyst], for more information.
  +
  +
==Data Analysis==
 
Data analysis will be available in two flavors: 1) upload coregistered anonymized data to NIH, 2) perform analysis at acquisition site using EnigmaMeg scripts
 
Data analysis will be available in two flavors: 1) upload coregistered anonymized data to NIH, 2) perform analysis at acquisition site using EnigmaMeg scripts
   
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[[ENIGMA_MEG_data_upload]]
 
[[ENIGMA_MEG_data_upload]]
   
====Potential MEG Subanalyses====
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===Potential MEG Subanalyses===
 
Healthy Volunteers
 
Healthy Volunteers
 
Epilepsy
 
Epilepsy
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Traumatic Brain Injury
 
Traumatic Brain Injury
 
Stroke
 
Stroke
 
===Singularity Container===
 
Singularity is a container technology (similar to Docker). We are using containers to allow for easy distribution of the analysis pipeline and analysis consistency. Singularity was chosen becuase it does not require administrative priveledges during runtime and can be run on an HPC system or local computing resources. Being a container, the analysis can be run on any platform (linux, mac, windows, ...) with singularity installed. <br>
 
For more information visit: https://sylabs.io/singularity/
 
 
The singularity def file can be found at:
 
github.com/........ upload
 
 
To build from scratch:
 
sudo singularity build enigma_meg.sif enigma_meg.def
 
 
The pre-built singularity container can be downloaded from (recommended/easier):
 
Under Construction
 
 
Calling commands:
 
The singularity container will be provided in a folder that also includes a ./bin folder
 
The commands within the bin folder are links to functions in the container
 
 
Commands can be called from the full path:
 
e.g.) /home/jstout/enigma/bin/enigma_rel_power -i /data/my_meg_data/subj1_resting_state.ds
 
Commands can be added to the path and run using the command name:
 
for BASH, add this line to the /home/$USER/.bashrc file and save:
 
export PATH=$PATH:/this/is/the/path/to/enigma/bin
 
enigma_rel_power -i /data/my_meg_data/subj1_resting_state.ds
 
 
Freesurfer related operations require a license file (download from https://surfer.nmr.mgh.harvard.edu/fswiki/License). You will need to put your license file in the enigma folder and call it fs_license.txt
 
 
===Resting State Analysis===
 
The analysis routine has been implemented in MNE python (https://mne.tools/stable/index.html) and packaged into a singularity container. This guarantees that differences in software dependencies and operating system configurations have been eliminated. The analysis has been chunked into steps to make processing straightforward.
 
 
1) process_anatomical.py
 
2) process_meg.py
 
 
Anatomical Preprocessing:
 
Surface models: Scalp, Outer Skull, Inner Skull, Pial Surface
 
Coregistration of the MRI and MEG data
 
Parcel extraction (freesurfer autorecon3)
 
Subparcel calculation (mne ....)
 
Source space
 
BEM - single shell
 
 
Data Cleanup: (Based on MNE pipeline) (Goal - Remove bad channels, bad segments, correct EOG, correct ECG)
 
Perform ICA analysis to extract a heartbeat signal
 
Extract heartbeats into epochs
 
Autobad runs on evoked data: Run autobad on ECG epoched data to identify bad channels and bad epochs
 
Perform ICA analysis after excluding bad data
 
Use template to identify and exclude heartbeats and eyeblinks
 
Remove bad ICAs
 
**Save pre and post cleanup noise RMS for use in summary statistics
 
 
Source Analysis (and spectral analysis):
 
Calculate covariance matrix
 
Load anatomical information (source space, bem, ...)
 
Project data to cortical surface (MNE)
 
Extract signal from ROIs
 
Calculate spectrum from ROI and save to csv
 
Calculate relative power by normalizing spectrum by all subject/frequency power
 
 
Summary Statistics:
 
*Evaluate for outliers
 
Compile the single subject CSV files to create a mean and standard deviation at each parcel
 
Save the subject number N and noise level of data
 
 
Outputs:
 
The outputs of the analysis will result in a csv file
 
A csv file for each subject will be created in the subfolder of the singularity directory
 
A final command can be run to calculate the summary statistics
 
 
===Meta-Analysis===
 
Submission of Results:
 
After calculating the local institutes summary statistics, the group csv file will be uploaded to the NIMH.
 
The group csv will have mean and standard deviation for each parcel and frequency band
 
A separate demographic csv will also be created with the demographic summary statistics
 
 
Meta-Analysis:
 
Statistical results will be produced using hierarchical meta-analysis to adjust for site-specific variance.
 
 
Data Harmonization (examples in MRI):
 
COMBAT - homepage:www.elsevier.com/locate/neuroimagehttp://dx.doi.org/10.1016/j.neuroimage.2017.08.047
 
Original Paper - (genetics) Adjusting batch effects in microarray expression data using empirical Bayes methods W.E. Johnson, C. Li and A. Rabinovic Biostatistics, 8 (2007), pp. 118-127
 
COMBAT Normalization in ENIGMA - https://doi.org/10.1016/j.neuroimage.2020.116956
 
Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan - https://www.sciencedirect.com/science/article/pii/S1053811919310419
 
DTI Harmonization using COMBAT - https://www.sciencedirect.com/science/article/pii/S1053811917306948?via%3Dihub#!
 
Cortical Thickness Harmonization using COMBAT - https://www.sciencedirect.com/science/article/pii/S105381191730931X
 
 
COVBAT - https://www.biorxiv.org/content/10.1101/858415v2.full.pdf
 
DeepHarmony (deep learning harmonization) - https://doi.org/10.1016/j.mri.2019.05.041
 
https://www.sciencedirect.com/science/article/pii/S1053811919310419
 
https://www.biorxiv.org/content/10.1101/2020.04.14.041582v1.abstract
 

Revision as of 14:33, 15 December 2021

UNDER CONSTRUCTION

The ENIGMA Consortium The ENIGMA MEG Working Group

Enigma Project - MEG working group

The goal of the ENIGMA MEG Working Group is to explore the spatiotemporal patterns of brain oscillatory activity, and determine how these patterns relate to age, gender, and common genetic variants. Data from numerous modalities have demonstrated a hierarchical organization of the brain from sensory systems to higher cortical areas. There is some evidence that this hierarchical organization may also be reflected in the spatial distribution of intrinsic timescales of activity. This ENIGMA working group intends to explore these patterns across the lifespan. To achieve the highest possible number of subjects, this needs to be done by meta-analysis.

Allison Nugent, PhD, ENIGMA MEG Chair
Jeff Stout, PhD, Lead Scientist
Anna Namyst, Project Coordinator

MEG Working Group: Participating Institutions

  • NIH/NIMH
  • NIH/NICHD
  • University of Pittsburgh
  • Aston University, UK
  • Children's Hospital of Philadephia
  • University of Pittsburgh
  • Universiti Teknologi PETRONAS
  • Advent Health for Children
  • McGill University, Montreal Neurological Institute
  • University of Texas Southwestern
  • IRCCS San Camillo Hospital Venice
  • University of Alabama, Birmingham
  • Cambridge University
  • Salisbury VA Medical Center, Wake Forest University, Wake Forest School of Medecine
  • University of Jyväskylä, Finland
  • The Mind Research Network
  • DNISC (Università degli Studi G.D'Annunzio Chieti Pescara)
  • New York University and NYU Abu Dhabi
  • Arkansas Childrens Hospital
  • Nemours DuPont Hospital for Children and Thomas Jefferson University
  • Johannes Gutenberg University, Mainz, Germany (Biomedical statistics and multimodal signal processing Unit)
  • University of Göttingen + University of Tübingen
  • Macquarie University
  • National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
  • NeuroSpin
  • Massachusetts General Hospital
  • Precision Medicine Centre, Hokuto Hospital
  • Precision Medicine Centre, Kumagaya General Hospital
  • Universiti Sains Malaysia
If you are interested in participating in this project, please email the project coordinator, Anna Namyst, for more information.

Data Analysis

Data analysis will be available in two flavors: 1) upload coregistered anonymized data to NIH, 2) perform analysis at acquisition site using EnigmaMeg scripts

Upload Data to be analyzed on NIH Biowulf cluster

Instructions for data preperation and upload

 ENIGMA_MEG_data_upload

Potential MEG Subanalyses

 Healthy Volunteers
 Epilepsy
 Alzheimer's and dementia
 Motor Analysis
 Language Processing
 Anxiety Disorders
 Schizophrenia and related disorders
 Developmental disorders
 Traumatic Brain Injury
 Stroke