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In Brain connectivity

BACKGROUND : Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS).

METHOD : An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High).

RESULTS : The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class.

CONCLUSION : The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.

Barile Berardino, Marzullo Aldo, Stamile Claudio, Durand-Dubief Francoise, Sappey-Marinier Dominique

2021-Jul-16

Brain connectivity, Diffusion tensor imaging (DTI), Multiple sclerosis, Structural connectivity