Exploring MEG brain fingerprints: evaluation, pitfalls, and interpretations

Abstract

Individual characterization of subjects based on their functional connectome (FC), termed 'FC fingerprinting', has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction and frequency bands. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprints in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identification offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

MEG, myself, and I: individual identification from neurophysiological brain activity

“…Future work should corroborate these results with regards to fingerprinting. The choice of connectivity measure to derive electrophysiological connectomes may also influence identifiability (59). We look forward to current progress in electrophysiological brain connectomics to put forward measures of network connectivity informed by mechanistic principles and emerging as a standard metrics in the field to confirm and expand present fingerprinting results (60).…”

Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study

“…Next, orthogonalized time-series were Hilbert-transformed and their amplitude envelopes (magnitude of the analytic signal) were pair-wise correlated using the Pearson's correlation coefficient, thus computing the corrected Amplitude Envelop Correlation (AECc) (Brookes et al, 2012;Hipp et al, 2012). The AECc is a robust functional connectivity measure comprised between −1 and 1 that demonstrates high levels of within-and between-subject consistency and grouplevel reproducibility (Colclough et al, 2016;Sareen et al, 2021). The resulting functional connectivity matrices were then made symmetric by averaging their upper and lower triangular parts, averaged over the 8 epochs, and used to compute (i) the average functional connectivity at the whole-brain level (i.e., the average over all functional connections between the 80 cortical ROIs), and (ii) the nodal functional connectivity strength (i.e., the rowwise sum of the functional connectivity matrices) for each subject.…”

Brain structure-function coupling provides signatures for task decoding and individual fingerprinting

“…Finally, our analyses are limited to slow temporal scales accessible with fMRI. Previous studies had attempted brain fingerprinting using electrophysiological recordings [46][47][48] , but the link between faster brain dynamics and structural topology remains poorly understood 49,50 . Future research may address how the hierarchy of structure-function dependencies vary at faster temporal scales, possibly carrying distinct fingerprinting and decoding information.…”