The influence of the head model on magnetoencephalography-derived functional connectivity fingerprinting

Abstract

AbstractFunctional connectivity (FC)-based neural fingerprinting is an approach that promises to distinguish subjects within a cohort on the basis of the patterns of statistical dependencies between time series recorded mostly if not always noninvasively, with electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). The message is that brain activity is what differentiates subjects, or in other words, what makes a neural fingerprint “unique”. In EEG- and MEG-derived FC fingerprinting, the activity recorded at the sensors is projected back into cortical sources by means of an inverse model containing the shape of the head and its conductivity, and further averaged to obtain time series of regional activity, used to compute FC. In this study we investigated the role of the head model in fingerprinting. Through a set of experiments aimed to decouple recorded activity and head model for each subject, we found that the head model has a strong influence on the fingerprinting performance, according to two different sets of metrics.