There is considerable controversy about the causes of cognitive decline after stroke, with evidence for both the absence and coexistence of Alzheimer pathology. A reduction in cortical thickness has been shown to be an important biomarker for the progression of many neurodegenerative diseases, including Alzheimer's disease (AD). However, brain volume changes following stroke are not well described. Cortical thickness estimation presents an ideal way to detect regional and global post-stroke brain atrophy. In this study, we imaged a group of patients in the first month after stroke and at 3 months. We compared three methods of estimating cortical thickness on unmasked images: one surface-based (FreeSurfer) and two voxel-based methods (a Laplacian method and a registration method, DiRecT). We used three benchmarks for our analyses: accuracy of segmentation (especially peri-lesional performance), reproducibility, and biological validity. We found important differences between these methods in cortical thickness values and performance in high curvature areas and peri-lesional regions, but similar reproducibility metrics. FreeSurfer had less reliance on manual boundary correction than the other two methods, while reproducibility was highest in the Laplacian method. A discussion of the caveats for each method and recommendations for use in a stroke population is included. We conclude that both surface- and voxel-based methods are valid for estimating cortical thickness in stroke populations.

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Highlights Machine learning and artificial intelligence have gained popularity for medical applications. We applied support vector machine (SV) and deep learning (DL) in termporal lobe epilepsy (TLE) Structural and diffusion-based models showed similar classification accuracies. Diffusion-based models to diagnose TLE performed better or similar compared to models to lateralize TLE. Models for patients with hippocampal sclerosis were more accurate than models that stratified non-lesional patients.