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README.md

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License

This file is part of the project megFingerprinting. All of megFingerprinting code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. megFingerprinting is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with megFingerprinting. If not, see https://www.gnu.org/licenses/.

Status

Manuscript has been submitted and is under review

Objective

To identify people based on patterns of brain activity (as indexed by MEG) and how demographic data interacts with this

Colaborators

Both Jason da Silva and Hector Orozco contributed equally to this project (both are first authors of this study)

Supervisors

Dr Bratislav Misic and Dr Sylvain Baillet at the Montreal Neurological Institute

Dataset

For this project we've been using OMEGA. The Open MEG Archive (OMEGA) is the fruit of a collaborative effort between the McConnell Brain Imaging Centre (MNI, McGill) and the Université de Montréal to provide a core repository of MEG data for open dissemination.

Contents

The codes listed below are intended to allow other researchers to preprocess, fingerprint, and plot their results on both the OMEGA dataset as well as other MEEG data

1-dataSetup_BIDS

  • This bash script will download the folders from the BIC server to your local computer. You need special access for this
    • Needs to be run from the local computer
    • Specify the number of participants that it will download in line 19
    • It also downloads metadata, empty room recordings, and extra files
    • It uses rsync, so, if the file was already downloaded, it will not overwrite it
    • Please note it downloads reconstructed anatomy data from a preprocessed version of OMEGA, as opposed to the newest OMEGA version

2-preprocessing_AEC_ortho.m and 2-preprocessing_PSD.m

  • These MATLAB scripts take all the subjects in the OMEGA_BIDS folder and preprocess them. One of them was intended for the amplitude envelope correlation analysis (connectivity), and the other to explore area-specific power spectral density (PSD)
  • MEG Preprocessing pipline:
    1. Import BIDS dataset (will not work if we are not using this format!)
    2. Import subject's anatomy
    3. Prepare MEG and Noise files
    4. Run PSD on sensors
    5. Filtering: Line noise and high pass
    6. SSP: EOG and ECG
    7. postProcessing: PSD on sensors
    8. SSP: Sacades and EMG
    9. Preprocess empty room recordings
    10. Separate into FOI's
    11. Data/Noise Covariance
    12. Compute head model
    13. Inverse Modelling: Beamformers
    14. Snapshot: Contact sheet of sources
    15. Amplitude Envelope Correlation or PSD estimation
    16. Output CSV file
    17. Save and ouput report
    18. Delete intermediate files and save beamformer weights
  • All output files are saved in output folder...
    • Brainstorm's subject report
    • The output of the PCA (% variance explained)
    • Matrices (csv format)
    • Matrices (brainstorm file (.mat))

3-matrix_visualization.ipynb

  • This notebook has some useful functions to plot the connectivity matrices and to assign the different areas to the corresponding resting state network

4-AEC_fingerprinting_*.ipynb and 4-PSD_fingerprinting_*.ipynb

  • We used the same method as Finn et al., 2015 & Amico & Goñi, 2018 to compare both PCA reconstructed data and raw data
  • After that, we run edge-wise analysis to understand what connections are consistent at the group level and which ones are more important to identify individuals than others
  • Finally, we run several sanity checks (correlating identifiability with subject characteristics and artifact summary statistics & separating structural connectivity and functional connectivity)
  • We perform this analysis with our different analysis conditions: between session, within sessions, for both AEC and PSD, for both the broadband data and specific frequency bands
  • Please note that subject identification is referred to as differentiation in the manuscript and 'self-identifiability' is referred to as differentiability in the manuscript

5-PLS_analysis.m

  • Performs the Partial Least Square analysis to try and find relationships between individuals' identifiability matrices and demographic information

6-Plotting_results_with_ggplot.R

  • Plots PLS results

Dependencies

  • Includes files that correspond to the assignation of the different anatomical areas to the resting state network, analysis used to compare movement/heart/eye artifacts to indidividuals' identifiability scores (used as sanity checks)...

Important Note

Please note that subject identification is referred to as differentiation in the manuscript and 'self-identifiability' is referred to as differentiability in the manuscript.