title: The Functional Integration in the Sensory-Motor System Predicts Aging in Healthy Older Adults.
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.
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Hui
authors.initials: H
authors.lastname: He
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Luo
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Chang
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Shan
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Weifang
authors.initials: W
authors.lastname: Cao
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Jinnan
authors.initials: J
authors.lastname: Gong
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Klugah-Brown
authors.affiliation: Department of Biological Psychiatry, Cuban Neuroscience Center La Habana, Cuba.
authors.forename: Maria A
authors.initials: MA
authors.lastname: Bobes
authors.affiliation: Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark NJ, USA.
authors.forename: Bharat
authors.initials: B
authors.lastname: Biswal
authors.affiliation: The Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in BioMedicine, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China.
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
keywords: aging; functional connectivity; machine learning; resting state fMRI; sensory-motor system