Amnamyst: Updated Analysis Information

2021-12-15T18:02:26Z

Updated Analysis Information

← Older revision		Revision as of 14:02, 15 December 2021
Line 1:		Line 1:
	UNDER CONSTRUCTION		'''UNDER CONSTRUCTION'''

			===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

Amnamyst: ENIGMA Analysis Page

2021-12-15T16:24:02Z

ENIGMA Analysis Page

New page

UNDER CONSTRUCTION

ENIGMA Analysis - Revision history

Amnamyst: Updated Analysis Information

Amnamyst: ENIGMA Analysis Page