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Cited by 101Functional Brain Connectivity StudiesNeural dynamics and brain functionEEG and Brain-Computer Interfaces

Brief segments of neurophysiological activity enable individual differentiation

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da Silva Castanheira, Jason, Orozco Perez, Hector Domingo, Misic, Bratislav, Baillet, Sylvain·2021·Nature Communications
DOI 10.1038/s41467-021-25895-8PMC PMC8481307
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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. 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.

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  • Depending upon the technology and data format, neurodata can be used to reasonably identify someone in ways that go beyond traditional PII or demographic aggregates.

    “Depending upon the technology and data format, neurodata can be used to reasonably identify someone 9 in ways that go beyond traditional PII or demographic aggregates.”

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Cited by

Sorted by citations

Authors
da Silva Castanheira, J., Orozco Perez, H. D., Misic, B., & Baillet, S.
Affiliations
McGill University (CA)
Corresponding author
Baillet, Sylvain
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2021-09-29
Volume
12
Issue
1
Pages
5713-5713
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-021-25895-8
PMC
PMC8481307
OpenAlex
W3202391304
Cited by
101
References
11
Citations by year
2026: 18 · 2025: 23 · 2024: 19 · 2023: 23 · 2022: 13 · 2021: 5
FWCI
7.89
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • McGill University
  • Fondation Brain Canada
  • Canada Research Chairs
  • Canada First Research Excellence Fund
  • National Institutes of Health
  • Health Canada
  • Canadian Institutes of Health Research
  • Natural Sciences and Engineering Research Council of Canada (Discovery Grant RGPIN \#017-04265, 01704265, 436355-13)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB026299)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Keywords
neural fingerprints, individual differentiation, resting-state functional connectivity, magnetoencephalography, functional connectome, interindividual variability
MeSH terms
Adolescent, Adult, Brain, Female, Humans, Individuality, Male, Middle Aged, Neurophysiology, Rest, Magnetoencephalography, Least-Squares Analysis, Artifacts, Electrophysiological Phenomena, Young Adult, Connectome

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