The functional integration in the sensory-motor system predicts aging in healthy older adults

Hui He, Cheng Luo, Xin Chang, Yan Shan, Weifang Cao, Jinnan Gong, Benjamin Klugah-Brown, Maria A. Bobes, Bharat Biswal, Dezhong Yao

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Healthy aging is typically accompanied by a decrease in the motor capacity. Although the disrupted neural representations and performance of movement have been observed in older age in previous studies, the relationship between the functional integration of sensory-motor (SM) system and aging could be further investigated. In this study, we examine the impact of healthy aging on the resting-state functional connectivity (rsFC) of the SM system, and investigate as to how aging is affecting the rsFC in SM network. The SM network was identified and evaluated in 52 healthy older adults and 51 younger adults using two common data analytic approaches: independent component analysis and seed-based functional connectivity (seed at bilateral M1 and S1). We then evaluated whether the altered rsFC of the SM network could delineate trajectories of the age of older adults using a machine learning methodology. Compared with the younger adults, the older demonstrated reduced functional integration with increasing age in the mid-posterior insula of SM network and increased rsFC among the sensorimotor cortex. Moreover, the reduction in the rsFC of mid-posterior insula is associated with the age of older adults. Critically, the analysis based on two-aspect connectivity-based prediction frameworks revealed that the age of older adults could be reliably predicted by this reduced rsFC. These findings further indicated that healthy aging has a marked influence on the SM system that would be associated with a reorganization of SM system with aging. Our findings provide further insight into changes in sensorimotor function in the aging brain.

Original languageEnglish (US)
Article number306
JournalFrontiers in Aging Neuroscience
Volume8
Issue numberJAN
DOIs
StatePublished - Jan 5 2017

All Science Journal Classification (ASJC) codes

  • Aging
  • Cognitive Neuroscience

Keywords

  • Aging
  • Functional connectivity
  • Machine learning
  • Resting state fMRI
  • Sensory-motor system

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