Brain connectivity patterns derived from aging-related alterations in dynamic brain functional networks and their potential as features for brain age classification

• Published in: Journal of neural engineering Vol. 21; no. 2; pp. 26015 - 26033
• Main Authors: Han, Hongfang, Jiang, Jiuchuan, Gu, Lingyun, Gan, John Q, Wang, Haixian
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.04.2024
• Subjects: Aging; Brain; brain age classification; Brain Mapping - methods; dynamic brain functional network; dynamic community structure; Humans; Magnetic Resonance Imaging - methods; Neural Pathways; resting-state fMRI; brain age classification; dynamic brain functional network; resting-state fMRI; dynamic community structure
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2552/ad33b1

Recent studies have demonstrated that the analysis of brain functional networks (BFNs) is a powerful tool for exploring brain aging and age-related neurodegenerative diseases. However, investigating the mechanism of brain aging associated with dynamic BFN is still limited. The purpose of this study is to develop a novel scheme to explore brain aging patterns by constructing dynamic BFN using resting-state functional magnetic resonance imaging data. A dynamic sliding-windowed non-negative block-diagonal representation (dNBDR) method is proposed for constructing dynamic BFN, based on which a collection of dynamic BFN measures are suggested for examining age-related differences at the group level and used as features for brain age classification at the individual level. The experimental results reveal that the dNBDR method is superior to the sliding time window with Pearson correlation method in terms of dynamic network structure quality. Additionally, significant alterations in dynamic BFN structures exist across the human lifespan. Specifically, average node flexibility and integration coefficient increase with age, while the recruitment coefficient shows a decreased trend. The proposed feature extraction scheme based on dynamic BFN achieved the highest accuracy of 78.7% in classifying three brain age groups. . These findings suggest that dynamic BFN measures, dynamic community structure metrics in particular, play an important role in quantitatively assessing brain aging.