Abstract
Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual’s neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.
Highlights
Understanding the biological nature of individual traits and behavior is an overarching objective of neuroscience research[1,2,3,4]
This epistemological question has become vivid with recent research showing that individuals can be differentiated from a cohort via their respective neural fingerprints derived from structural magnetic resonance imaging (MRI)[10,11], functional MRI12–16, electroencephalography (EEG)[17,18,19], or functional near-infrared spectroscopy[20]
The recent leveraging of large, open functional MRI (fMRI) datasets has brought empirical evidence that individuals may be differentiated within a cohort from their brain imaging functional connectivity, inspiring the metaphor of a neural fingerprint
Summary
Understanding the biological nature of individual traits and behavior is an overarching objective of neuroscience research[1,2,3,4]. With bigger and deeper data volumes, neuroscientists are confronted with a paradox: while big-data neuroscience approaches the realm of population neuroscience, we remain challenged by understanding how interindividual data variability echoes the singularity of the self[1,3,8,9] This epistemological question has become vivid with recent research showing that individuals can be differentiated from a cohort via their respective neural fingerprints derived from structural magnetic resonance imaging (MRI)[10,11], functional MRI (fMRI)[12,13,14,15,16], electroencephalography (EEG)[17,18,19], or functional near-infrared spectroscopy (fNIRS)[20]. We use resting-state recordings of magnetoencephalography MEG35 from a cohort of participants to identify neurophysiological features of individual differentiation We both derive measures of functional organization (i.e., functional connectivity) inspired by fMRI neural fingerprinting approaches, and spectral signal markers that are proper to the wider frequency spectrum of brain signaling accessible to neurophysiological data. Our work extends the notion of a neural fingerprint to the fast and large-scale resting-state dynamics of electrophysiology
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