Abstract #0216

Linking Brain Functional Connectivity of Older Adults to Maximum Voluntary Contraction: Towards a Neural Marker of Motor Strength

Amin Ghaffari¹, Vala Masjedizadeh¹, Xu Chen², Jason Langley², Aaron Seitz³, Megan Peters⁴, Weiwei Zhang⁵, Ilana Bennett⁵, and Xiaoping Hu¹

¹Bioengineering, University of California, Riverside, Riverside, CA, United States, ²Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States, ³Psychology, Northeastern University, Boston, MA, United States, ⁴Cognitive Sciences, University of California, Irvine, Irvine, CA, United States, ⁵Psychology, University of California, Riverside, Riverside, CA, United States

Synopsis

Keywords: Functional Connectivity, Functional Connectivity

Motivation: Brain functional connectivity network (FCN) can identify individuals and predict their cognition levels, but their ability to predict individual differences in motor function of older adults remains unexplored.

Goal(s): To determine whether FCN can predict older adults’ motor strength and discover the most effective brain networks in this prediction.

Approach: Forty-eight older adults underwent fMRI scanning and the relationship between their FCN and Maximum Voluntary Contraction (MVC) as an indicator of motor function was investigated.

Results: FCN predicted the MVC scores successfully. The functional connections of the right motor network were the most important predictors of this motor strength indicator.

Impact: Older adults tend to experience impaired motor functions due to aging or certain diseases. Here, we determined the most crucial functional connections assessing the motor abilities of older adults which could be used as the targets for their rehabilitation programs.

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