Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations

Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

Functional Brain Connectivity Analysis in Intellectual Developmental Disorder During Music Perception

Intellectual Developmental Disorder (IDD) is a neurodevelopmental disorder involving impairment of general cognitive abilities. This disorder impacts the conceptual, social, and practical skills adversely. There is a growing interest in exploring the neurological behavior associated with these disorders. Assessment of functional brain connectivity and graph theory measures have emerged as powerful tools to aid these research goals. The current research contributes by comparing brain connectivity patterns of IDD individuals to those typical controls. Considering the intellectual deficits linked to the IDD population, we hypothesized an atypical connectivity pattern in the IDD group. Brain signals were recorded by a dry-electrode Electroencephalography (EEG) system during the rest and music states observed by the subjects. We studied a group of seven IDD subjects and seven healthy controls to understand the connectivity within the human brain during the resting-state vis-à-vis while listening to music. Findings of this research emphasize (1) hyper-connected functional brain networks and increased modularity as potential characteristics of the IDD group, (2) the ability of soothing music to reduce the resting state hyper-connected pattern in the IDD group, and (3) the effect of soothing music in the lower frequency bands of the control group compared to the higher frequency bands of the IDD group.

EEG dataset of individuals with intellectual and developmental disorder and healthy controls under rest and music stimuli

This article presents a collection of electroencephalographic (EEG) data recorded from 14 participants, that includes 7 participants with Intellectual and Developmental Disorder (IDD) and 7 Typically Developing Controls (TDC) under resting-state and under music stimuli. The EEG data were acquired using the EMOTIV EPOC+ device that is a 14-channel dry electrode device. The article provides two types of data: (1) Raw EEG data under resting-state and with music stimuli (i.e., task based data) and (2) pre-processed EEG data under resting state and with music stimuli. Alongside this data, we provide a robust and fully automated pre-processing pipeline for EEG data. The pipeline performs filtering, line noise removal, data selection, ICA, and automatic artefact removal.

Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magneto encephalography (MEG) recordings. Nevertheless, it is still uncertain to what extent MEG signatures can serve as an indicator of human identifiability during task-related conduct. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.