Robust sub-network fingerprints of brief signals in the MEGfunctional connectome for single-patient classification

Abstract

Recent studies have shown that the Magnetoen-cephalography (MEG) functional connectome is person-differentiable in a same-day recording with as little as 20 latent components, showing variability across synchrony measures and spectral bands. Here, we succeed with [Formula] components of the functional connectome on a multi-day dataset of 43 subjects and link it to related clinical applications. By optimizing sub-networks of [Formula] regions with 30 seconds of broadband signal, we find robust fingerprinting performance, showing several patterns of region re-occurrence. From a search space of 5.72 trillion, we find 46,071 of many more acceptable solutions, with minimal duplicates found in our optimization. Finally, we show that each of these sub-networks can identify 30 Parkinsons patient sub-networks from 30 healthy subjects with a mean F1 score of 0.716 {+/-} 0.090SD. MEG fingerprints have previously been shown on multiple occasions to hold patterns on the rating scales of progressive neurodegenerative diseases using much coarser features. Furthermore, these sub-networks may similarly be useful for identifying patterns across characteristics for age, genetics, and cognition.