This title appears in the Scientific Report :
2024
Please use the identifier:
http://dx.doi.org/10.48550/ARXIV.2402.17744 in citations.
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding
Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using Contrastive Learning and Geometric Unfolding
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 capture...
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Personal Name(s): | Oberstrass, Alexander (Corresponding author) |
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DeKraker, Jordan / Palomero-Gallagher, Nicola / Muenzing, Sascha E. A. / Evans, Alan C. / Axer, Markus / Amunts, Katrin / Dickscheid, Timo | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 |
Imprint: |
arXiv
2024
|
DOI: |
10.48550/ARXIV.2402.17744 |
Document Type: |
Preprint |
Research Program: |
EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health Helmholtz International BigBrain Analytics and Learning Laboratory (HIBALL) Human Brain Project Specific Grant Agreement 3 Neuroscientific Data Analytics and AI |
Subject (ZB): | |
Publikationsportal JuSER |
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. |