Identifying individuals from their brain natural frequency fingerprints

Neural oscillations are critical for brain function and cognition. Thus, identifying the typical or natural frequencies of the brain is an important step in understanding its functional architecture. Recently, a data-driven algorithm has been developed for mapping these frequencies throughout the co...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 22492 - 13
Main Authors: Arana, Lydia, Herrera-Morueco, Juan José, Santonja, Javier, Capilla, Almudena
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 02.07.2025
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/2404
631/378/1385/1877
631/378/2649
Adult
Algorithms
Brain
Brain - physiology
Brain architecture
Brain Mapping - methods
Brain oscillations
Cognition
Female
Fingerprinting
Fourier transforms
Humanities and Social Sciences
Humans
Magnetoencephalography
Magnetoencephalography - methods
Male
Mapping
multidisciplinary
Natural frequencies
Oscillations
Resting state
Science
Science (multidisciplinary)
Time series
Young Adult
Resting state
Fingerprinting
Brain oscillations
Magnetoencephalography
Natural frequencies
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Neural oscillations are critical for brain function and cognition. Thus, identifying the typical or natural frequencies of the brain is an important step in understanding its functional architecture. Recently, a data-driven algorithm has been developed for mapping these frequencies throughout the cortex, free of anatomical and frequency-band constraints, but its robustness is limited to group-level analyses. Here, we adapt this algorithm to improve the single-subject maps of natural frequencies derived from magnetoencephalography and validate them using the fingerprinting technique. Modifications to the original method included (1) increasing the number of power spectra assigned to each k-means cluster, and (2) smoothing across neighboring voxels. Our results show high accuracy in individual identification within single sessions and across sessions separated by over four years. This demonstrates the stability and reliability of the single-subject mapping of natural frequencies, enhancing opportunities for identification of pathological variations in the intrinsic oscillatory activity of individuals.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-05632-7