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感觉运动网络的稳定性塑造了静息态在整个生命周期中的动态储备。

Stability of sensorimotor network sculpts the dynamic repertoire of resting state over lifespan.

机构信息

Cognitive Brain Dynamics Laboratory, National Brain Research Centre, NH 8, Manesar, Gurgaon 122052, India.

School of Artificial Intelligence & Data Science, Centre for Brain Science & Applications, Indian Institute of Technology, Jodhpur NH 62, Surpura Bypass Rd, Karwar, Rajasthan 342030, India.

出版信息

Cereb Cortex. 2023 Feb 7;33(4):1246-1262. doi: 10.1093/cercor/bhac133.

Abstract

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.

摘要

时间上稳定的大脑区域间神经协调模式对于生存至关重要。最近,许多研究强调了健康老龄化与功能大脑网络组织在不同时间尺度上的改变之间的关联。然而,健康老龄化过程中功能大脑网络时间稳定性的定量特征仍未得到探索。本研究引入了一种数据驱动的无监督方法,通过低维模式来捕捉高维动态功能连接(dFC),并使用定量指标来估计时间稳定性。在一个大型的健康老龄化数据集(18-88 岁,n=645)上,通过静息态、观影和感觉运动任务(SMT),对 dFC 时间稳定性的健康老龄化相关变化进行了特征描述。主要结果表明:(1)dFC 电影观看任务的全脑时间动态与静息态更接近,而与 SMT 不接近,整体趋势是 SMT 期间观察到的时间稳定性最高,其次是电影观看和静息态,在整个生命老化过程中保持不变;(2)在两种任务条件下,年轻人的神经认知网络稳定性高于老年人;(3)整个大脑静息态的时间稳定性沿着寿命呈 U 型曲线,这种模式与感觉运动网络稳定性共享,表明它们之间存在更深层次的关系。总的来说,这些结果可以普遍应用于使用神经影像学工具研究神经障碍队列。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8919/9930636/baa2abff3408/bhac133f1.jpg

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