A comprehensive understanding of the organizational principles in the human
brain requires, among other factors, well-quantifiable descriptors of nerve
fiber architecture. Three-dimensional polarized light imaging (3D-PLI) is a
microscopic imaging technique that enables insights into the fine-grained
organization of myelinated nerve fibers with high resolution. Descriptors
characterizing the fiber architecture observed in 3D-PLI would enable
downstream analysis tasks such as multimodal correlation studies, clustering,
and mapping. However, best practices for observer-independent characterization
of fiber architecture in 3D-PLI are not yet available. To this end, we propose
the application of a fully data-driven approach to characterize nerve fiber
architecture in 3D-PLI images using self-supervised representation learning. We
introduce a 3D-Context Contrastive Learning (CL-3D) objective that utilizes the
spatial neighborhood of texture examples across histological brain sections of
a 3D reconstructed volume to sample positive pairs for contrastive learning. We
combine this sampling strategy with specifically designed image augmentations
to gain robustness to typical variations in 3D-PLI parameter maps. The approach
is demonstrated for the 3D reconstructed occipital lobe of a vervet monkey
brain. We show that extracted features are highly sensitive to different
configurations of nerve fibers, yet robust to variations between consecutive
brain sections arising from histological processing. We demonstrate their
practical applicability for retrieving clusters of homogeneous fiber
architecture and performing data mining for interactively selected templates of
specific components of fiber architecture such as U-fibers.

应用自我监督表示学习的完全数据驱动方法，以 3D-PLI 图像为基础，对神经纤维结构进行特征化。

自我监督表示学习在 3D-PLI 中用于神经纤维分布模式的研究

Self-Supervised Representation Learning for Nerve Fiber Distribution  Patterns in 3D-PLI

Networked structures arise in a wide array of different contexts such as
technological and transportation infrastructures, social phenomena, and
biological systems. These highly interconnected systems have recently been the
focus of a great deal of attention that has uncovered and characterized their
topological complexity. Along with a complex topological structure, real
networks display a large heterogeneity in the capacity and intensity of the
connections. These features, however, have mainly not been considered in past
studies where links are usually represented as binary states, i.e. either
present or absent. Here, we study the scientific collaboration network and the
world-wide air-transportation network, which are representative examples of
social and large infrastructure systems, respectively. In both cases it is
possible to assign to each edge of the graph a weight proportional to the
intensity or capacity of the connections among the various elements of the
network. We define new appropriate metrics combining weighted and topological
observables that enable us to characterize the complex statistical properties
and heterogeneity of the actual strength of edges and vertices. This
information allows us to investigate for the first time the correlations among
weighted quantities and the underlying topological structure of the network.
These results provide a better description of the hierarchies and
organizational principles at the basis of the architecture of weighted
networks.

研究加权网络的结构以揭示其统计性质和不同元素之间连接的组织原则。通过研究科学合作和全球航空运输网络，同时考虑连接的权重和拓扑结构，提出了新的度量方法，发现这些加权网络的组织结构具有层级性和异质性，参数化的方法便于进一步研究。