Neural Fingerprinting based on Brain Network Dynamics: A Cross-Platform MEG Study

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.