RT Journal Article
SR Electronic
T1 MEG, myself, and I: individual identification from neurophysiological brain activity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.18.431803
DO 10.1101/2021.02.18.431803
A1 Jason Da Silva Castanheira
A1 Hector D Orozco
A1 Bratislav Misic
A1 Sylvain Baillet
YR 2021
UL http://biorxiv.org/content/early/2021/07/03/2021.02.18.431803.abstract
AB 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 StatementThe authors have declared no competing interest.