fMRI and MEG Fingerprints Diverge at the Individual Level

Abstract

Functional connectivity (FC) profiles derived from fMRI and MEG offer complementary perspectives on large-scale brain organization, while showing reasonable correspondence at the population-average level. However, how their individual variability relates between these modalities remains unclear. Using the Cam-CAN dataset, we derived neural fingerprints from subject-level resting-state fMRI FC and MEG FC obtained from the same participants (N=543). Fingerprints derived from each modality separately showed robust within-subject, cross-session consistency and successfully predicted age and cognition, confirming that these features capture stable and behaviourally relevant individual traits. We then quantified shared individual variability between modalities using variance partitioning analyses and representational similarity measures. Two main findings emerged. First, despite strong similarity at the population-average level, correspondence between MEG and fMRI neural fingerprints at the subject level was low, as reflected in both cross-modal shared variance and the preservation of pairwise inter-subject similarity patterns, quantified by linear Centred Kernel Alignment (CKA). Second, structural fingerprints accounted for the majority of age-related variance in functional neural fingerprints, almost entirely explaining the age-related variance in, and shared between, fMRI and MEG. MEG functional fingerprints did have unique information not accounted for by structure when explaining variability in cognitive traits, but this was not shared with fMRI. Together, these findings demonstrate that there is a surprisingly lack of similarity in the way that subjects vary between fMRI and electrophysiology, especially when structural variability is accounted for.