Troidl Jakob, Warchol Simon, Choi Jinhan, Matelsky Jordan, Dhanyasi Nagaraju, Wang Xueying, Wester Brock, Wei Donglai, Lichtman Jeff W, Pfister Hanspeter, Beyer Johanna
IEEE Trans Vis Comput Graph. 2023 Oct 26;PP. doi: 10.1109/TVCG.2023.3327388.
Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.
高分辨率连接组学的最新进展首次为研究人员提供了获取神经元回路和脑网络精确千万亿字节级重建的途径。神经科学家正在分析这些网络，以更好地理解大脑中的信息处理过程。特别是，科学家们对识别特定的小网络基序感兴趣，即较大脑网络中重复出现的子图，这些子图被认为是神经元的构建模块。尽管这些基序通常很小（例如，由2 - 6个神经元组成），但巨大的数据量和复杂的数据复杂性给搜索和分析过程带来了重大挑战。为了分析这些基序，关键是要审查脑网络中基序的实例，然后将图结构映射到所涉及神经元和突触的详细三维重建上。我们提出了Vimo，一种用于分析大型脑网络中神经元基序和基序链的交互式可视化方法。专家们可以在可视化界面中直观地勾勒网络基序，并指定所涉及神经元和突触的结构属性，以查询大型连接组学数据集。可以在高分辨率三维渲染中探索基序实例（MI）。为了简化对MI的分析，我们设计了一种受视觉抽象启发的连续聚焦与上下文隐喻。这允许用户从解剖结构的高度详细渲染过渡到强调潜在基序结构和突触连接性的视图。此外，Vimo支持识别基序链，其中一个基序被重复使用（例如，2 - 4次）以形成更大的网络结构。我们在一项用户研究和一项深入的案例研究中对Vimo进行了评估，该案例研究涉及七位领域专家，研究对象是果蝇大型连接组中的基序，该连接组包含超过21000个神经元和2000万个突触。我们发现，Vimo通过快速的分析迭代和连接性突出显示实现了假设的生成和确认。
