Optimized cortical subdivision for classification of Alzheimer's disease with cortical thickness

Abstract

In several studies, brain atrophy measured by cortical thickness has shown to be a meaningful biomarker for Alzheimer’s disease. In this research field, the level of granularity at which values are compared is an important aspect. Vertex- and voxel-based approaches can detect atrophy at a very fine scale, but are susceptible to noise from misregistrations and inter-subject differences in the population. Regional approaches are more robust to these kinds of noise, but cannot detect variances at a local scale. In this work, an optimized classifier is presented for a parcellation scheme that provides a trade-off between both paradigms by increasing the granularity of a regional approach. For this purpose, atlas regions are subdivided into gyral and sulcal parts at different height levels. Using two-stage feature selection, optimal gyral and sulcal subregions are determined for the final classification with sparse logistic regression. The robustness was assessed on clinical data by 10- fold cross-validation and by testing the prediction accuracy for unseen individuals. In every aspect, superior classification performance was observed as compared to the original parcellation scheme which can be<br />explained by the increased locality of cortical thickness measures and<br />the customized classification approach that reveals interacting regions.