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.

我们通过将展开方法的最新进展与使用自我监督对比学习方法获得的深度纹理特征相结合，展示了一种新的方法来分析人类海马体在 3D-PLI 中的区域组织，我们的表示中的聚类与海马体亚区的经典描述相对应，证明了所开发的方法的有效性。

应用对比学习和几何展开的 3D-PLI 技术分析人类海马区的区域组织

Analyzing Regional Organization of the Human Hippocampus in 3D-PLI Using  Contrastive Learning and Geometric Unfolding

A long standing goal in neuroscience has been to elucidate the functional
organization of the brain. Within higher visual cortex, functional accounts
have remained relatively coarse, focusing on regions of interest (ROIs) and
taking the form of selectivity for broad categories such as faces, places,
bodies, food, or words. Because the identification of such ROIs has typically
relied on manually assembled stimulus sets consisting of isolated objects in
non-ecological contexts, exploring functional organization without robust a
priori hypotheses has been challenging. To overcome these limitations, we
introduce a data-driven approach in which we synthesize images predicted to
activate a given brain region using paired natural images and fMRI recordings,
bypassing the need for category-specific stimuli. Our approach -- Brain
Diffusion for Visual Exploration ("BrainDiVE") -- builds on recent generative
methods by combining large-scale diffusion models with brain-guided image
synthesis. Validating our method, we demonstrate the ability to synthesize
preferred images with appropriate semantic specificity for well-characterized
category-selective ROIs. We then show that BrainDiVE can characterize
differences between ROIs selective for the same high-level category. Finally we
identify novel functional subdivisions within these ROIs, validated with
behavioral data. These results advance our understanding of the fine-grained
functional organization of human visual cortex, and provide well-specified
constraints for further examination of cortical organization using
hypothesis-driven methods.

该研究通过结合大规模扩散模型和脑图像合成的方法，介绍了 BrainDiVE（Brain Diffusion for Visual Exploration），该方法有效地从自然图像和功能磁共振成像数据中合成预测激活给定脑区域的图像，并验证了 BrainDiVE 在人类视觉皮层的 fine-grained functional organization 的应用及假设驱动方法的作用。