Multiplexity of human brain oscillations as a personal brain signature

Abstract

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

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

“…Our study presents a possible exploration into the characteristics of these approaches for our sub-network fingerprints. Our Simulated Annealing results showed performance variations between the number of regions (10,20,30,40) and MEG signal duration (10 s, 30 s, 50 s) as shown in Fig. 2.…”

“…Investigation into the different numbers of regions (10,20,30,40) that we used and signal duration (10 s, 30 s, 50 s) demonstrated across all conditions similar region frequency and co-occurrence frequency patterns. These patterns are shown 10 regions with 30 s in Fig.…”

“…Our method demonstrates 100% person re-identification performance using sub-networks with deterministic regions/features. The use of multi-day data (1-27 days) for robustness, only done in MEG fingerprinting studies by [7,20,19,18] further strengthens the significance of these sub-networks. Our findings show that 10 out of 90 regions are feasible for our method from a search space of 90 10 , where each sample represents 45 components.…”

“…Furthermore, we found performance variations in the number of regions from our initial optimization searches that were run locally. We, therefore, grid-searched the duration of the MEG signal (10 s, 30 s, 50 s) and the number of regions used (10,20,30,40). Differential identifiability was used to evaluate performance.…”

“…In [7, 18, 12], connectivity is done on sensor-space data. Dynamic connectivity fingerprinting on the functional connectome includes signal avalanching [19] and spectral-coupling [20]. Approaches without connectivity include spectral activity [7, 11, 12, 18, 21] and temporal activity [18, 21, 22].…”