Personalized MRI-based characterization of subcortical anomalies in Ataxia-Telangiectasia using deep-learning.

backgroundCerebellar atrophy is a known feature of ataxia-telangiectasia (A-T). However, basal ganglia dysfunction contributing to extrapyramidal movement disorders in A-T remains understudied.

objectivesTo characterize basal ganglia abnormalities in A-T using a normative self-supervised deep autoencoder trained on MRI-based diffusion and perfusion features from healthy children.

methodsMean values of apparent diffusion coefficient and cerebral blood flow perfusion maps were extracted from seven regions-of-interest: caudate, hippocampus, pallidum, putamen, thalamus, cerebellar gray matter and cerebellar white matter. A normative deep autoencoder that reconstructs these features was trained on healthy subjects. Reconstruction errors for healthy and A-T participants were computed. We used Shapley Additive Explanations (SHAP) to identify the most influential features contributing to the features' reconstruction predictions. Correlations between reconstruction errors and clinical scores in A-T patients were evaluated.

resultsFeatures were correctly reconstructed in controls but not A-T participants, who showed significantly higher reconstruction errors. Hippocampus, caudate and putamen diffusion, and caudate and putamen perfusion were overestimated, and cerebellar diffusion and pallidum perfusion underestimated, in participants with A-T. SHAP scores revealed that caudate, putamen, and hippocampus perfusion had the greatest influence on the reconstruction of perfusion features. In contrast, cerebellar diffusion and caudate perfusion had the greatest influence on the reconstruction of diffusion features. Exploratory analysis showed that extrapyramidal movement sub-scores from A-T participants correlated with perfusion and diffusion reconstruction errors from cerebellar and subcortical structures.

conclusionOur findings suggest that pallidum, caudate, and cerebellar gray matter are potential targets for novel treatment approaches for A-T. The approach enables identification of subtle tissue anomalies at an individual level, allowing tailored approaches.