Multi-modal neuroimaging signatures predict cognitive decline in multiple sclerosis: A 5-year longitudinal study
Oun Al-Iedani1, Stasson Lea2, A Alshehri3
1School of Biomedical Sciences and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle, Callaghan, Australia; Hunter Medical Research Institute, New Lambton Heights, Australia.
Multiple Sclerosis and Related Disorders
|December 16, 2023
Summary
Multi-modal MRI accurately predicts cognitive decline in people with MS (pwMS) over five years. This approach identifies predictive signatures for personalized management, even without relapses.
Area of Science:
- Neuroimaging
- Neurology
- Machine Learning
Background:
- Cognitive impairment is a common but under-recorded symptom in multiple sclerosis (MS).
- Predicting cognitive decline in people with MS (pwMS) is crucial for effective treatment strategies.
- Existing studies often use single MRI modalities or cross-sectional designs.
Purpose of the Study:
- To assess the predictive value of multi-modal Magnetic Resonance (MR) imaging for cognitive decline in pwMS over five years.
- To identify multi-modal MRI signatures that predict cognitive decline.
- To compare the predictive power of multi-modal MRI with conventional volumetric measures.
Main Methods:
- A cohort of 43 pwMS was assessed at baseline and 5-year follow-up.
- Utilized 70 multi-modal MRI measures (MRS, DTI, volumetrics) and clinical data (age, sex, disease duration, treatment).
- Employed machine learning (GLMnet penalized regression) to identify predictive signatures for cognitive outcomes (ARCS, SDMT).
Main Results:
- The multi-modal MRI approach accurately predicted cognitive decline in pwMS.
- The best model for ARCS change included 16 features (MRS, volumetric, DTI) and explained 54% of the variation.
- The best model for SDMT change explained 39% of the variation; multi-modal MRI outperformed conventional volumetrics (R²=0.26).
Conclusions:
- Multi-modal MRI signatures can predict cognitive decline in pwMS over 5 years with high accuracy (>90% AUC).
- Future research should incorporate additional MR modalities and other predictors (genetic, environmental).
- Validated signatures could personalize MS cognitive decline management in clinical trials.
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