Functional Connectome from Phase Synchrony at Resting State is a Neural Fingerprint

Abstract

Coherent oscillatory activity across brain regions provides a variety of individual-specific characteristics, sometimes referred to as a neural fingerprint. This information, however, may not be directly retrieved from raw functional magnetic resonance imaging (fMRI) time series. In this study, we examined the data of 205 participants who completed two resting-state fMRI scanning sessions, separated by an average of 2.63 years. In the first step, we tested the long-term reliability of functional connectomes derived from amplitude-based functional connectivity (the conventional method) and found that they remained accurate markers (>85%, p < 0.001, permutation test) for identifying individuals, even after a period longer than 800 days. Using the same data set, we further expanded our exploration of the extent to which two analytic components of oscillatory activity (amplitude envelope and instantaneous phase) may function as reliable fingerprints. Both analytic signals—in particular, the instantaneous phase—were identified as useful indices in shaping functional connectivity fingerprints (86%, p < 0.001, permutation test). Connectivity profiles derived from the ventral attention, frontoparietal, and default mode networks were the largest contributing factors to identification. The current results suggest that neural synchronization tapped by analytical signal from a low-frequency resting-state fMRI blood oxygen level-dependent oscillation could be a reliable and useful fingerprint for identifying individuals and might provide an alternative method for characterizing dynamic functional connectivity profiles.