Analyses of AI-generated hippocampal shape deformations in relation to cognition in healthy older adults – Data and Code

Magnetic resonance imaging (MRI)-derived hippocampus measurements have been associated with different cognitive domains. Different morphological hippocampal shape analysis methods have been developed, but it is unclear how their principles relate and how consistent are the published results in relation to cognition in the normal elderly in the light of the new deep-learning-based state-of-the-art modelling methods. We compared results from analysing the hippocampal morphology using manually-generated binary masks and a Laplacian- based deformation shape analysis method, with those resulting from analysing SynthSeg-generated hippocampal binary masks using a deep-learning method based on the PointNet architecture, in relation to different cognitive domains, using data from The Lothian Birth Cohort 1936. Here we provide the MATLAB code used to analyse the deformations generated by the two shape deformation modelling methods, and validate and compare the results. We also provide the files with the deformity vectors for each hippocampal mesh point, and the average template meshes used in the analyses, as well as all the results from the statistical analyses.