| Title: | Sensory, somatomotor and internal mentation networks emerge dynamically in the resting brain with internal mentation predominating in older age |
| Journal: | NeuroImage |
| Published: | 18 May 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34020018/ |
| DOI: | https://doi.org/10.1016/j.neuroimage.2021.118188 |
| Citations: | 6 (4 in last 2 years) as of 8 Aug 2024 |
Publication 5707
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Abstract
Age-related changes in the brain are associated with a decline in functional flexibility. Intrinsic functional flexibility is evident in the brain's dynamic ability to switch between alternative spatiotemporal states during resting state. However, the relationship between brain connectivity states, associated psychological functions during resting state, and the changes in normal aging remain poorly understood. In this study, we analyzed resting-state functional magnetic resonance imaging (rsfMRI) data from the Human Connectome Project (HCP; N = 812) and the UK Biobank (UKB; N = 6,716). Using signed community clustering to identify distinct states of dynamic functional connectivity, and text-mining of a large existing literature for functional annotation of each state, our findings from the HCP dataset indicated that the resting brain spontaneously transitions between three functionally specialized states: sensory, somatomotor, and internal mentation networks. The occurrence, transition-rate, and persistence-time parameters for each state were correlated with behavioural scores using canonical correlation analysis. We estimated the same brain states and parameters in the UKB dataset, subdivided into three distinct age ranges: 50-55, 56-67, and 68-78 years. We found that the internal mentation network was more frequently expressed in people aged 71 and older, whereas people younger than 55 more frequently expressed sensory and somatomotor networks. Furthermore, analysis of the functional entropy - a measure of uncertainty of functional connectivity - also supported this finding across the three age ranges. Our study demonstrates that dynamic functional connectivity analysis can expose the time-varying patterns of transition between functionally specialized brain states, which are strongly tied to increasing age.</p>
12 Authors
- Lu Zhang
- Jiajia Zhao
- Qunjie Zhou
- Zhaowen Liu
- Yi Zhang
- Wei Cheng
- Weikang Gong
- Xiaoping Hu
- Wenlian Lu
- Edward T Bullmore
- Chun-Yi Zac Lo
- Jianfeng Feng
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