An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis

Allegra Conti; Constantina Andrada Treaba; Ambica Mehndiratta; Valeria Teresa Barletta; Caterina Mainero; Nicola Toschi

doi:10.3390/brainsci13020198

An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis

Brain Sci. 2023 Jan 24;13(2):198. doi: 10.3390/brainsci13020198.

Authors

Allegra Conti¹, Constantina Andrada Treaba^{2

3}, Ambica Mehndiratta^{2

3}, Valeria Teresa Barletta^{2

3}, Caterina Mainero^{2

3}, Nicola Toschi^{1

2

3}

Affiliations

¹ Department of Biomedicine and Prevention, University of Rome 'Tor Vergata', Via Montpellier 1, 00133 Rome, Italy.
² Massachusetts General Hospital, Boston, MA 02114, USA.
³ A. A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA.

Abstract

To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.

Keywords: cortical atrophy; explainability; leukocortical lesions; machine learning; multiple sclerosis; rim lesions.

Grants and funding

This research was funded by the National Multiple Sclerosis Society (grant number NMSS 4281-RG-A1, NMSS RG 4729A2/1 and NMSS RG 1802-30468), National Institutes of Health R01NS078322-01-A1, and United States Army W81XWH-13-1-0122.