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MEG, myself, and I: individual identification from neurophysiological brain activity

View ORCID ProfileJason Da Silva Castanheira, Hector D Orozco, View ORCID ProfileBratislav Misic, View ORCID ProfileSylvain Baillet
doi: https://doi.org/10.1101/2021.02.18.431803
Jason Da Silva Castanheira
1Montreal Neurological Institute, McGill University, Montreal QC, Canada
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Hector D Orozco
1Montreal Neurological Institute, McGill University, Montreal QC, Canada
2Department of Psychology, Neuroscience, and Behavior, McMaster University, Hamilton ON, Canada
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Bratislav Misic
1Montreal Neurological Institute, McGill University, Montreal QC, Canada
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  • For correspondence: bratislav.misic@mcgill.ca sylvain.baillet@mcgill.ca
Sylvain Baillet
1Montreal Neurological Institute, McGill University, Montreal QC, Canada
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  • For correspondence: bratislav.misic@mcgill.ca sylvain.baillet@mcgill.ca
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Abstract

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. This amount of variability challenges the sensitivity and specificity of analysis methods to capture the personal characteristics of a putative neural portrait. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully identify individuals within a cohort and predict some individual traits, yielding the notion of a neural fingerprint. Here, we aimed to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. Akin to fMRI approaches, neurophysiological functional connectomes enabled the identification of individuals, with identifiability rates similar to fMRI’s. We also show that individual identification was equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that identifiability can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: individuals remain identifiable from recordings performed weeks after their baseline reference data was collected. Based on these results, we can anticipate a vast range of further research and practical applications of individual differentiation from neural electrophysiology in personalized, clinical, and basic neuroscience.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 03, 2021.
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MEG, myself, and I: individual identification from neurophysiological brain activity
Jason Da Silva Castanheira, Hector D Orozco, Bratislav Misic, Sylvain Baillet
bioRxiv 2021.02.18.431803; doi: https://doi.org/10.1101/2021.02.18.431803
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MEG, myself, and I: individual identification from neurophysiological brain activity
Jason Da Silva Castanheira, Hector D Orozco, Bratislav Misic, Sylvain Baillet
bioRxiv 2021.02.18.431803; doi: https://doi.org/10.1101/2021.02.18.431803

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