期刊
CEREBRAL CORTEX
卷 33, 期 4, 页码 1246-1262出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhac133
关键词
dynamic functional connectivity; healthy aging; resting-state networks; temporal stability; Mahalanobis distance
This study investigates the temporal stability of dynamic functional connectivity (dFC) in resting-state, movie-viewing, and sensorimotor tasks across the lifespan. The results demonstrate differences in temporal stability between task conditions and age groups, suggesting an age-related decline in the stability of neurocognitive networks.
Temporally stable patterns of neural coordination among distributed brain regions are crucial for survival. Recently, many studies highlight association between healthy aging and modifications in organization of functional brain networks, across various time-scales. Nonetheless, quantitative characterization of temporal stability of functional brain networks across healthy aging remains unexplored. This study introduces a data-driven unsupervised approach to capture high-dimensional dynamic functional connectivity (dFC) via low-dimensional patterns and subsequent estimation of temporal stability using quantitative metrics. Healthy aging related changes in temporal stability of dFC were characterized across resting-state, movie-viewing, and sensorimotor tasks (SMT) on a large (n = 645) healthy aging dataset (18-88 years). Prominent results reveal that (1) whole-brain temporal dynamics of dFC movie-watching task is closer to resting-state than to SMT with an overall trend of highest temporal stability observed during SMT followed by movie-watching and resting-state, invariant across lifespan aging, (2) in both tasks conditions stability of neurocognitive networks in young adults is higher than older adults, and (3) temporal stability of whole brain resting-state follows a U-shaped curve along lifespan-a pattern shared by sensorimotor network stability indicating their deeper relationship. Overall, the results can be applied generally for studying cohorts of neurological disorders using neuroimaging tools.
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