AI-based deformable hippocampal mesh reflects hippocampal morphological characteristics in relation to cognition in healthy older adults

Abstract

Magnetic resonance imaging (MRI)-derived hippocampus measurements have been associated with different cognitive domains. The knowledge of hippocampal structural deformations as we age has contributed to our understanding of the overall aging process. 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 (DL) state-of-the-art modeling methods. We compared results from analysing the hippocampal morphology using manually-generated binary masks and a Laplacianbased deformation shape analysis method, with those resulting from analysing SynthSeg-generated hippocampal binary masks using a DL method based on the PointNet architecture, in relation to different cognitive domains. Whilst most previously reported statistically significant associations were also replicated, differences were also observed due to 1) differences in the binary masks and 2) differences in sensitivity between the methods. Differences in the template mesh, number of vertices of the template mesh, and their distribution did not impact the results.

HighlightsO_LINewdeep-learning-based hippocampal 3D-shape modeling method replicates hippocampal shape reported associations with cognition C_LIO_LINew deep-learning-based hippocampal 3D-shape modeling method has increased sensitivity than a conventional Lapalcian-based deformation method C_LIO_LIAccuracy in hippocampal binary masks is crucial in the AI-based shape modeling method C_LI