ENIGMA Analysis

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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.
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


 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


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 


 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