Multiple sclerosis and related disorders · 2026
Walking and strength outcomes of targeted hip resistance training may be predicted by both MRI and clinical measures in multiple sclerosis.
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Fritz Nora E, Eloyan Ani, Keller Jennifer, Zackowski Kathleen M
Аннотация
BACKGROUND: Compromised neural function contributes sensation, strength, and walking deficits in persons with multiple sclerosis (PwMS). Progressive resistance training (PRT) promotes neural adaptations, leading to favorable functional outcomes, while advances in MRI enable the evaluation of white matter tract pathology in vivo. The purpose of this work was to examine the utility of MRI with clinical measures to predict exercise responsiveness to PRT.
METHODS: Twenty-eight PwMS (mean age: 49.3 and EDSS: 3.8) and 26 controls (mean age: 50) participated in 12-weeks of PRT. Primary outcomes were change in walk velocity and hip strength. Model predictors included baseline demographics, falls, walk velocity, strength, cognitive performance, quality of life, vibration sensation and 3T MRI measures of spinal cord area (SCA) and corticospinal tract (CST) fractional anisotropy (FA), mean diffusivity (MD) and magnetization transfer ratio (MTR). A random forest procedure was used as an exploratory analysis for prediction of exercise responsiveness. The predictive accuracy was measured using the normalized mean squared error (MSE) within a leave-one-out cross-validation procedure, but was not externally validated.
RESULTS: Strength change predictors included baseline strength, diagnosis, age, disease severity, falls, cognitive performance, quality of life, vibration, and CST (FA, MD, and MTR) (normalized MSE: 0.1792). Walk velocity change predictors included baseline walk velocity, age, disease severity, sex, cognitive performance, vibration, and CST (FA, MD, and MTR) (normalized MSE: 0.2147).
CONCLUSION: A combination of motor, cognitive, and MRI measures may predict responsiveness to a PRT program in PwMS. These data confirm that optimizing function relies on knowledge of pathology, disability level and intervention selection in pwMS, and that using imaging information in combination with functional performance may offer advantages to optimizing functional recovery for pwMS.
METHODS: Twenty-eight PwMS (mean age: 49.3 and EDSS: 3.8) and 26 controls (mean age: 50) participated in 12-weeks of PRT. Primary outcomes were change in walk velocity and hip strength. Model predictors included baseline demographics, falls, walk velocity, strength, cognitive performance, quality of life, vibration sensation and 3T MRI measures of spinal cord area (SCA) and corticospinal tract (CST) fractional anisotropy (FA), mean diffusivity (MD) and magnetization transfer ratio (MTR). A random forest procedure was used as an exploratory analysis for prediction of exercise responsiveness. The predictive accuracy was measured using the normalized mean squared error (MSE) within a leave-one-out cross-validation procedure, but was not externally validated.
RESULTS: Strength change predictors included baseline strength, diagnosis, age, disease severity, falls, cognitive performance, quality of life, vibration, and CST (FA, MD, and MTR) (normalized MSE: 0.1792). Walk velocity change predictors included baseline walk velocity, age, disease severity, sex, cognitive performance, vibration, and CST (FA, MD, and MTR) (normalized MSE: 0.2147).
CONCLUSION: A combination of motor, cognitive, and MRI measures may predict responsiveness to a PRT program in PwMS. These data confirm that optimizing function relies on knowledge of pathology, disability level and intervention selection in pwMS, and that using imaging information in combination with functional performance may offer advantages to optimizing functional recovery for pwMS.