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Functional fingerprinting for the developing brain using deep metric learning.

Rui Xu1, Shuwan Zhao1, Zhengyi Liu1

  • 1Center for Artificial Intelligence in Medical Imaging, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.

Imaging Neuroscience (Cambridge, Mass.)
|January 30, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Metric-BolT, a deep learning method for brain fingerprinting. It accurately identifies individuals using neuroimaging, revealing insights into brain development and genetics.

Keywords:
brain developmentbrain fingerprintingfMRIindividual identificationindividual variability

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Genetics

Background:

  • Individual brain functional architecture is unique.
  • Neuroimaging data can capture this uniqueness.
  • Understanding developmental trajectories is crucial.

Purpose of the Study:

  • To introduce Metric-BolT, a novel deep learning framework for brain functional fingerprinting.
  • To characterize developmental trajectories during childhood and adolescence using brain fingerprints.
  • To explore the relationship between brain fingerprints, cognitive abilities, and genetic factors.

Main Methods:

  • Developed and applied the Metric-BolT deep learning framework.
  • Utilized longitudinal neuroimaging data from children and adolescents.
  • Analyzed correlations with cognitive abilities and genetic associations.

Main Results:

  • Metric-BolT achieved high identification accuracies (97.6% within-session, 86.6% across 4 years).
  • Discriminative fingerprints were linked to higher-order association cortices and the default-mode network.
  • Fingerprints correlated with fluid/crystallized intelligence and showed significant genetic associations.

Conclusions:

  • Metric-BolT is an effective computational approach for brain functional fingerprinting.
  • Brain fingerprints provide insights into individual variability in adolescent neurodevelopment.
  • Genetic factors influence individual brain functional organization during development.