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. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual’s neural fingerprint. Here, we aim 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. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.

We all have the intuition that our brain makes us unique. Here, the authors show that seconds of brain activity are sufficient to differentiate an individual, even when recorded weeks or months apart.

Details

Title
Brief segments of neurophysiological activity enable individual differentiation
Author
da Silva Castanheira Jason 1   VIAFID ORCID Logo  ; Orozco Perez Hector Domingo 2 ; Misic Bratislav 1   VIAFID ORCID Logo  ; Baillet Sylvain 1   VIAFID ORCID Logo 

 McGill University, Montreal Neurological Institute, Montreal, Canada (GRID:grid.14709.3b) (ISNI:0000 0004 1936 8649) 
 McMaster University, Department of Psychology, Neuroscience and Behavior, Hamilton, Canada (GRID:grid.25073.33) (ISNI:0000 0004 1936 8227) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2577604922
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.