Discovering individual fingerprints in resting-state functional connectivity using deep neural networks

Juhyeon Lee, Jong Hwan Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Non-negligible idiosyncrasy due to interindividual differences is an ongoing issue in resting-state functional MRI (rfMRI) analysis. We show that a deep neural network (DNN) can be employed for individual identification by learning important features from the time-varying functional connectivity (FC) of rfMRI in the Human Connectome Project. We employed the trained DNN to identify individuals from an independent dataset acquired at our institution. The results revealed that the DNN could successfully identify 300 individuals with an error rate of 2.9% using 15 s time-window and 870 individuals with an error rate of 6.7%. A trained DNN with nonlinear hidden layers led to the proposal of the “fingerprint of FC” (fpFC) as representative edges of individual FC. The fpFCs for individuals exhibited commonly important and individual-specific edges across time-window lengths (from 5 min to 15 s). Furthermore, the utility of our model for another group of subjects was validated, supporting the feasibility of our technique in the context of transfer learning. In conclusion, our study offers an insight into the discovery of the intrinsic mode of the human brain using whole-brain resting-state FC and DNNs.

Original languageEnglish
Article numbere26561
JournalHuman Brain Mapping
Volume45
Issue number1
DOIs
Publication statusPublished - 2024 Jan

Bibliographical note

Publisher Copyright:
© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Keywords

  • Deep neural networks
  • Fingerprints
  • Functional connectivity
  • Functional magnetic resonance imaging
  • Human Connectome Project
  • Individual identification
  • Transfer Learning

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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