VIMO - Visual Analysis of Neuronal Connectivity Motifs
VIMO - Visual Analysis of Neuronal Connectivity Motifs
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DOI：
10.1109/tvcg.2023.3327388
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发表时间：
2024-01-01
影响因子：
5.2
通讯作者：
Beyer，Johanna
中科院分区：
文献类型：
--
作者：
Troidl，Jakob;Warchol，Simon;Beyer，Johanna
高分辨率连接组学的最新进展使研究人员首次能够精确重建神经元回路和大脑网络。神经科学家正在分析这些网络，以更好地了解大脑中的信息处理。特别是，科学家们对识别特定的小网络基序感兴趣，即较大的大脑网络的重复子图，这些子图被认为是神经元构建块。尽管此类基序通常很小（例如 2-6 个神经元），但庞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，至关重要的是检查大脑网络中基序的实例，然后将图形结构映射到所涉及神经元和突触的详细 3D 重建。我们提出了 Vimo，一种交互式视觉方法，用于分析大型大脑网络中的神经元基序和基序链。专家可以在视觉界面中直观地绘制网络图案，并指定所涉及的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 3D 渲染中探索主题实例 (MI)。为了简化 MI 的分析，我们受视觉抽象的启发，设计了一个连续的焦点和上下文隐喻。这使得用户可以从高度详细的解剖结构渲染过渡到强调潜在主题结构和突触连接的视图。此外，Vimo 支持识别主题链，其中主题被重复使用（例如 2-4 次）以形成更大的网络结构。我们通过一项用户研究和七位领域专家对果蝇大型连接组（包括超过 21,000 个神经元和 2000 万个突触）基序的深入案例研究来评估 Vimo。我们发现 Vimo 通过快速分析迭代和连接性突出来实现假设生成和确认。
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 presentVimo, 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,Vimosupports the identification of motif chains where a motif is used repeatedly (e.g., 2–4 times) to form a larger network structure. We evaluateVimoin 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 thatVimoenables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.