Fig. 1: Neural fingerprinting analysis pipeline and definition of differentiability.

a Schematic of exemplar MEG data divided into datasets used in each of the specified differentiation challenges. (i) Within-session challenge: the session data was split in half to generate segments of equal duration; (ii) Between-sessions challenge: differentiation was performed using data recorded on two separate days; (iii) Between-session shortened challenge: data recorded on two different days were split into three 30 s segments. b Schematic of the data analysis pipeline: source modeling was first performed before extracting features from each region of the Desikan-Killiany atlas³⁷. These features were vectorized and subsequently used to fingerprint individuals, yielding a participant correlation matrix. c Features for the between-session challenge from an exemplar subject. Left panel depicts amplitude envelope correlation (AEC) functional connectivity matrices across two datasets; both matrices feature the Pearson correlation coefficients between all 68 regions of the Desikan-Killiany atlas³⁷. Right panel plots the power spectrum density estimates from two regions of the atlas, across two datasets. d Differentiability was derived for each participant as the z-score of their correlation to themselves, relative to the correlation between themselves and the rest of the cohort. A participant with a high correlation to themselves and low correlations to others was qualified as highly differentiable. An individual highly correlated to both themselves and many others in the cohort was qualified as less differentiable.