Functional fingerprinting for the developing brain using deep metric learning.

ABSTRACT: Brain fingerprinting offers a promising method for delineating the unique functional architecture of individual brains using neuroimaging data. Here, we present a novel deep learning framework-Metric-BolT-for brain fingerprinting and use it to characterize distinct developmental trajectories during childhood and early adolescence. Based on longitudinal neuroimaging data, the extracted brain fingerprints achieved identification accuracies of 97.6% for two data runs acquired within a single session and 86.6% for runs spanning 4 years. Notably, the most discriminative brain fingerprints were driven by higher-order association cortices, particularly regions of the default-mode network. Moreover, we showed these fingerprints to correlate with cognitive abilities, such as fluid and crystallized

SUBMITTER: Xu R 

PROVIDER: S-EPMC12849230 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature