Neural Fingerprinting based on Brain Network Dynamics: A Cross-Platform MEG Study
- 1University of Nottingham
Abstract
Neural fingerprinting seeks to identify individuals based on measurements of brain activity, exploiting the fact that aspects of brain function unique to an individual remain stable across repeated scans. Magnetoencephalography (MEG) is a powerful technique for fingerprinting. However, most MEG studies have used conventional (SQUID-based) MEG technology and typically rely on data aggregated over time, overlooking the rich temporal dynamics available in MEG. Here, using SQUID-MEG and the more recently introduced OPM-MEG, we showed fingerprinting was possible within and between modalities using static (time-aggregated) features; this is consistent with previous work. We further asked whether fingerprinting was possible based on network dynamics, estimated using a canonical hidden Markov model (CHMM). Results showed that the CHMM-derived networks provide a better fit to SQUID data than to OPM data; however, this effect was small and to be expected given the canonical networks were trained on SQUID data. We further showed that fingerprinting was possible within and between modalities using CHMM-derived state activation time courses and state power spectral densities. However, state summary statistics and transition probabilities only supported within modality fingerprinting. Our findings suggest that subject-specific information is preserved within CHMM states and across MEG technologies. This represents an important step towards advancing our understanding of brain dynamics, and particularly the brain networks delineated by the CHMM. The study also supports the future use of the CHMM for processing and interpretation of OPM-MEG data.
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BibTeX
@misc{beer2026neural,
title = {{Neural Fingerprinting based on Brain Network Dynamics: A Cross-Platform MEG Study}},
author = {A. Y. Beer and C. Gohil and Z. Tanner and L. Rier and R. M. Hill and N. Holmes and D. Ferring and V. M. Shah and M. Woolrich and M. J. Brookes},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.14.751409},
url = {https://www.biorxiv.org/content/10.64898/2026.09.14.751409v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Neural Fingerprinting based on Brain Network Dynamics: A Cross-Platform MEG Study
AU - A. Y. Beer
AU - C. Gohil
AU - Z. Tanner
AU - L. Rier
AU - R. M. Hill
AU - N. Holmes
AU - D. Ferring
AU - V. M. Shah
AU - M. Woolrich
AU - M. J. Brookes
PY - 2026
DA - 2026/09/14
PB - bioRxiv
DO - 10.64898/2026.09.14.751409
UR - https://www.biorxiv.org/content/10.64898/2026.09.14.751409v1
AB - Neural fingerprinting seeks to identify individuals based on measurements of brain activity, exploiting the fact that aspects of brain function unique to an individual remain stable across repeated scans. Magnetoencephalography (MEG) is a powerful technique for fingerprinting. However, most MEG studies have used conventional (SQUID-based) MEG technology and typically rely on data aggregated over time, overlooking the rich temporal dynamics available in MEG. Here, using SQUID-MEG and the more recently introduced OPM-MEG, we showed fingerprinting was possible within and between modalities using static (time-aggregated) features; this is consistent with previous work. We further asked whether fingerprinting was possible based on network dynamics, estimated using a canonical hidden Markov model (CHMM). Results showed that the CHMM-derived networks provide a better fit to SQUID data than to OPM data; however, this effect was small and to be expected given the canonical networks were trained on SQUID data. We further showed that fingerprinting was possible within and between modalities using CHMM-derived state activation time courses and state power spectral densities. However, state summary statistics and transition probabilities only supported within modality fingerprinting. Our findings suggest that subject-specific information is preserved within CHMM states and across MEG technologies. This represents an important step towards advancing our understanding of brain dynamics, and particularly the brain networks delineated by the CHMM. The study also supports the future use of the CHMM for processing and interpretation of OPM-MEG data.
ER -