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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 and frequency bands. 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. 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 fingerprints 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. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identification 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.

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A growing body of literature suggests dietary components can support mood and cognitive function through the impact of their bioactive or sensorial properties on neural pathways. Of interest, objective measures of the autonomic nervous system—such as those regulating bodily functions related to heartbeat and sweating—can be used to assess the acute effects of dietary components on mood and cognitive function. Technological advancements in the development of portable and wearable devices have made it possible to collect autonomic responses in real-world settings, creating an opportunity to study how the intake of dietary components impacts mood and cognitive function at an individual level, day-to-day. In this paper, we aimed to review the use of autonomic nervous system responses such as heart rate or skin galvanic response to investigate the acute effects of dietary components on mood and cognitive performance in healthy adult populations. In addition to examining the existing methodologies, we also propose new state-of-the-art techniques that use autonomic nervous system responses to detect changes in proxy patterns for the automatic detection of stress, alertness, and cognitive performance. These methodologies have potential applications for home-based nutrition interventions and personalized nutrition, enabling individuals to recognize the specific dietary components that impact their mental and cognitive health and tailor their nutrition accordingly.