ORIGINAL RESEARCH article

Front. Aging Neurosci.

Sec. Neurocognitive Aging and Behavior

Volume 17 - 2025 | doi: 10.3389/fnagi.2025.1493855

This article is part of the Research TopicNeuroimaging of the Aging BrainView all 15 articles

Characterizing Changes to Individual-Specific Brain Signature with Age

Provisionally accepted
  • University of Cincinnati, Cincinnati, United States

The final, formatted version of the article will be published soon.

The increasing prevalence of neurodegenerative diseases in an aging population underscores the critical need for reliable biomarkers distinguishing normal aging from pathological neurodegeneration. This study leverages neuroimaging to identify age-resilient biomarkers, establishing a baseline of neural features that are relatively stable across the aging process. Our research objectives are threefold: a) Validate a methodology using leverage scores to identify age-robust neural signatures; b) Confirm the consistency of these features across a diverse age cohort (18-87 years); and c) Assess the stability of individual-specific neural characteristics across multiple brain parcellations (Craddock, AAL, and HOA). Using functional connectomes data from resting-state and task-based fMRI, we found that a small subset of features consistently capture individual-specific patterns, with significant overlap (∼50%) between consecutive age groups and across atlases. Our approach effectively minimized inter-subject similarity while maintaining intra-subject consistency across different cognitive tasks. The stability of these signatures throughout adulthood and their consistency across different anatomical parcellations provide new perspectives on brain aging. They highlight both the preservation of individual brain architecture and subtle age-related reorganization. These findings enhance our understanding of age-related brain changes, potentially aiding in differentiating normal cognitive decline from neurodegenerative processes.

Keywords: Brain signature, functional connectome, Matrix sampling, biomarkers of aging, Feature Selection

Received: 09 Sep 2024; Accepted: 16 Jun 2025.

Copyright: © 2025 Taimouri and Ravindra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Monireh Taimouri, University of Cincinnati, Cincinnati, United States

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