Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding
This work addresses the need for interpretable analysis of 3D-PLI data in neuroscience, offering a method to characterize hippocampal organization, though it appears incremental as it builds on existing unfolding and contrastive learning techniques.
The paper tackled the challenge of analyzing the complex texture in 3D-PLI images of the human hippocampus by developing a novel method that combines geometric unfolding with deep texture features from self-supervised contrastive learning, resulting in clusters that align with classical hippocampal subfield descriptions.
Understanding the cortical organization of the human brain requires interpretable descriptors for distinct structural and functional imaging data. 3D polarized light imaging (3D-PLI) is an imaging modality for visualizing fiber architecture in postmortem brains with high resolution that also captures the presence of cell bodies, for example, to identify hippocampal subfields. The rich texture in 3D-PLI images, however, makes this modality particularly difficult to analyze and best practices for characterizing architectonic patterns still need to be established. In this work, we demonstrate a novel method to analyze the regional organization of the human hippocampus in 3D-PLI by combining recent advances in unfolding methods with deep texture features obtained using a self-supervised contrastive learning approach. We identify clusters in the representations that correspond well with classical descriptions of hippocampal subfields, lending validity to the developed methodology.