Assessing the Role of Volumetric Brain Information in Multiple Sclerosis Progression

TL;DR

This study leverages deformation-based morphometry and advanced modeling to identify brain regions linked to multiple sclerosis progression, enhancing prediction accuracy and clinical understanding.

Contribution

It introduces a robust feature importance metric for voxel-level brain data, improving prediction of MS progression and identifying clinically relevant brain regions.

Findings

Identified brain regions significantly associated with MS progression.

Achieved superior prediction performance with the selected features.

Features generalize across different clinical progression definitions.

Abstract

Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes at the voxel level, providing insight into how each brain region contributes to clinical progression with regards to neurodegeneration. Utilizing such voxel-level data from a relapsing multiple sclerosis clinical trial, we extend a model-agnostic feature importance metric to identify a robust and predictive feature set that corresponds to clinical progression. These features correspond to brain regions that are clinically meaningful in MS disease research, demonstrating their scientific relevance. When used to predict progression using classical survival models and 3D convolutional neural networks, the identified regions led to the best-performing models, demonstrating their prognostic strength. We also find that these features generalize well to other definitions of clinical progression and can compensate for the omission of highly prognostic clinical features, underscoring the predictive power and clinical relevance of deformation-based morphometry as a regional identification tool.