Machine Learning–Based Motor Reserve Index and Nigral Free Water in Older Adults

TL;DR

This study creates a machine learning index to assess motor reserve in older adults and finds it relates to brain changes linked to aging and amyloid buildup.

Contribution

A novel machine learning–based Motor Reserve Index is developed and linked to posterior nigral free water and amyloid burden.

Findings

Lower Motor Reserve Index scores correlate with higher posterior nigral free water.

Posterior nigral free water is positively associated with amyloid burden.

Anterior nigra shows no significant associations with the index or amyloid.

Abstract

Objective To develop and test a machine learning–based, Motor Reserve Index (MRIx), integrating a comorbidity-based frailty measure, gait parameters, balance measures, and sex, and to evaluate its associations with free water in the anterior/posterior substantia nigra and amyloid burden. Methods De-identified data from the Health and Aging Brain Study: Health Disparities (HABS-HD) were collected from 84 older adults (mean age = 60.6 ± 9.5 years; ∼60% female). Frailty was determined via sum of chronic conditions (e.g., hypertension, cancer, diabetes, stroke, and kidney disease). Lower extremity motor measures included mean gait speed and inter-trial variability, timed up and go trial variability, Short Physical Performance Battery performance and PET SUVr (florbetaben uptake; PET-FBB) were also computed. An unsupervised dimensionality-reduction method yielded the MRIx from these features. Pearson correlations were used to test MRIx against nigral free-water measures, followed by correlations of anterior and posterior nigral free-water with PET FBB. Significance was set at p < 0.05. Results Participants with lower MRIx (suggesting diminished motor reserve) exhibited higher posterior nigral values (r = −0.29, p < 0.05). Additionally, posterior nigra free-water was positively related to Global FBB SUVR (r≈0.26, p≈0.02), indicating that elevated posterior nigral free water corresponded with greater amyloid signal. Anterior nigra showed no significant associations with MRIx or PET FBB SUVRs. Conclusion These preliminary data indicate that a machine learning–derived Motor Reserve Index, reflecting multiple factors including frailty and gait, may offer insights into posterior nigral changes and amyloid pathology in older adults.