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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/


====MEG Working group====
LIST OF PARTICIPATING INSTITUTES


[http://enigma.ini.usc.edu/ The ENIGMA Consortium]<br>
====Data Analysis Format====
[http://enigma.ini.usc.edu/ongoing/enigma-meg-working-group/ The ENIGMA MEG Working Group]</br>
Data analysis will be available in two flavors: 1) upload coregistered anonymized data to NIH, 2) perform analysis at acquisition site using EnigmaMeg scripts


==Enigma Project - MEG working group==
===Upload Data to be analyzed on NIH Biowulf cluster===
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>
Data anonymization will be performed using


[mailto:nugenta@nih.gov Allison Nugent, PhD], ENIGMA MEG Chair <br>
Upload data to the NIH using the win/mac/linux compatible Globus application. This software is designed to handle "sensitive" data and has perpetual sessions that allow for interruption and continued upload.
[mailto:jeff.stout@nih.gov Jeff Stout, PhD], Lead Scientist <br>
https://docs.globus.org/
[mailto:anna.namyst@nih.gov Anna Namyst, BA], Project Coordinator</br>
https://docs.globus.org/how-to/share-files/


==MEG Working Group: Participating Institutions==
===Data Preperation Prior to Upload===
[[File:ENIGMA-MEG-WG 12.2021.jpg|frameless|right|500px|600px| Global Participation in the ENIGMA MEG Working Group]]
Defacing the MRI prior to upload - suggested software is pydeface - custom scripts will be provided to confirm defacing.
* Advent Health for Children
* Arkansas Childrens Hospital
* Aston University, UK
* Cambridge University
* Children's Hospital of Philadephia
* DNISC (Università degli Studi G.D'Annunzio Chieti Pescara)
* IRCCS San Camillo Hospital Venice
* Johannes Gutenberg University, Mainz, Germany (Biomedical statistics and multimodal signal processing Unit)
* Macquarie University
* Massachusetts General Hospital
* McGill University, Montreal Neurological Institute
* National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
* Nemours DuPont Hospital for Children
* NeuroSpin
* New York University and NYU Abu Dhabi
* NIH/NICHD
* NIH/NIMH
* Precision Medicine Centre, Hokuto Hospital
* Precision Medicine Centre, Kumagaya General Hospital
* Salisbury VA Medical Center, Wake Forest University, Wake Forest School of Medecine
* The Mind Research Network
* Thomas Jefferson University
* Universiti Sains Malaysia
* Universiti Teknologi PETRONAS
* University of Alabama, Birmingham
* University of Göttingen
* University of Jyväskylä, Finland
* University of Pittsburgh
* University of Pittsburgh
* University of Texas Southwestern
* University of Tübingen


If you are interested in participating in this project, please email the project coordinator, [mailto:anna.namyst@nih.gov Anna Namyst], for more information.
====Potential MEG Subanalyses====

==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

*[https://github.com/jstout211/enigma_MEG ENIGMA MEG Pipeline on GitHub]
*[https://megcore.nih.gov/index.php/ENIGMA_MEG_Pipeline_FAQ Processing Pipeline FAQ]

==Upload Data to be analyzed on NIH Biowulf cluster==
Instructions for data preparation and upload: <br>
[https://megcore.nih.gov/index.php/ENIGMA_MEG_data_upload ENIGMA MEG Data Upload]

==Potential MEG Subanalyses==
Healthy Volunteers
Healthy Volunteers
Epilepsy
Epilepsy
Line 31: Line 70:
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.

v1
1) anatomical_proc.py
2) data_cleanup.py
3) source_analysis.py
4) summary_statistics.py

v2
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
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

Latest revision as of 16:48, 14 February 2024


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, BA, Project Coordinator

MEG Working Group: Participating Institutions

Global Participation in the ENIGMA MEG Working Group
Global Participation in the ENIGMA MEG Working Group
  • Advent Health for Children
  • Arkansas Childrens Hospital
  • Aston University, UK
  • Cambridge University
  • Children's Hospital of Philadephia
  • DNISC (Università degli Studi G.D'Annunzio Chieti Pescara)
  • IRCCS San Camillo Hospital Venice
  • Johannes Gutenberg University, Mainz, Germany (Biomedical statistics and multimodal signal processing Unit)
  • Macquarie University
  • Massachusetts General Hospital
  • McGill University, Montreal Neurological Institute
  • National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India
  • Nemours DuPont Hospital for Children
  • NeuroSpin
  • New York University and NYU Abu Dhabi
  • NIH/NICHD
  • NIH/NIMH
  • Precision Medicine Centre, Hokuto Hospital
  • Precision Medicine Centre, Kumagaya General Hospital
  • Salisbury VA Medical Center, Wake Forest University, Wake Forest School of Medecine
  • The Mind Research Network
  • Thomas Jefferson University
  • Universiti Sains Malaysia
  • Universiti Teknologi PETRONAS
  • University of Alabama, Birmingham
  • University of Göttingen
  • University of Jyväskylä, Finland
  • University of Pittsburgh
  • University of Pittsburgh
  • University of Texas Southwestern
  • University of Tübingen
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 preparation 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
Retrieved from "https://megcore.nih.gov/index.php?title=ENIGMA_MEG_Working_Group&oldid=5585"