Guess Who? Identifying individuals from their brain natural frequency fingerprints

Abstract

Neural oscillations are critical for brain function and cognition. Thus, identifying the typical or natural oscillatory frequencies of the brain is an important first step for understanding its functional architecture. Recently, a data-driven algorithm has been developed for mapping the brains natural frequencies throughout the whole cortex, free of anatomical and frequency-band constraints. However, an important limitation of this methodology is that it yields robust results only at the group level. Here, we aimed to adapt this algorithm to improve the quality of the single-subject maps of natural frequencies obtained from magnetoencephalography (MEG) recordings. To achieve this goal, we incorporated two modifications to the original method: (1) increasing the number of individual power spectra to be assigned to each k-means cluster, and (2) smoothing across neighboring voxels. To assess the quality of the single-subject maps, we relied on the fingerprinting technique. Our results show a high degree of accuracy in individual identification, both within a single recording session and across separate sessions. Furthermore, we were able to identify individuals by their natural frequency fingerprints, even with a gap of over four years between sessions. This demonstrates the robustness of the single-subject mapping of natural frequencies and opens new opportunities for identification of pathological variations in intrinsic oscillatory activity in individual subjects.