Optimized Cortical Subdivision for Classification of Alzheimer’s Disease With Cortical Thickness

Zusammenfassung

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 sceme which can be explained by the increased locality of cortical thickness measures and the customized classification approach that reveals interacting regions.