多尺度比较连接组学
Multiscale Comparative Connectomics
AI总结：
本研究提出多尺度比较连接组学方法，利用基于随机图模型的效应量度量，实现多受试者连接组的稳健分析，揭示与表型相关的层级脑结构，并在模拟和真实数据上优于现有方法。
摘要：
连接组是大脑中结构和/或功能连接的图谱，提供了其所依赖的神经生物学表型的复杂表示。这种信息丰富的数据模态有可能改变我们对大脑连接模式与神经过程、疾病和障碍之间关系的理解。然而，现有的用于分析连接组的计算技术往往不足以处理多受试者连接组数据集：许多当前方法要么仅设计用于分析单个连接组，要么利用启发式图统计量，这些统计量无法捕捉大脑区域之间多尺度连接的完整拓扑结构。为了支持更严谨的连接组学分析，我们引入了一组稳健且可解释的效应量度量，这些度量受随机图模型最新理论进展的启发。这些度量促进了跨网络拓扑不同尺度对多个连接组的同时分析，使得能够稳健且可重复地发现与表型特征相关的层级脑结构。除了解释我们算法的理论基础和保证外，我们还通过广泛的模拟研究和真实数据实验证明了它们相对于当前最先进的连接组学方法的优越性。使用从基因不同的小鼠品系（包括BTBR小鼠——自闭症的标准模型——以及三种行为野生型）获得的一组高分辨率连接组，我们展示了我们的方法如何成功揭示多受试者连接组数据中的潜在信息，并产生其他方法无法捕捉的关于神经表型连接相关性的有价值的见解。重现我们分析所需的数据和代码可在 https://github.com/neurodata/MCC 获取。
英文摘要：
The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This information-rich data modality has the potential to transform our understanding of the relationship between patterns in brain connectivity and neurological processes, disorders, and diseases. However, existing computational techniques used to analyze connectomes are oftentimes insufficient for interrogating multi-subject connectomics datasets: many current methods are either solely designed to analyze single connectomes or leverage heuristic graph statistics that are unable to capture the complete topology of multiscale connections between brain regions. To enable more rigorous connectomics analysis, we introduce a set of robust and interpretable effect size measures motivated by recent theoretical advances in random graph models. These measures facilitate simultaneous analysis of multiple connectomes across different scales of network topology, enabling the robust and reproducible discovery of hierarchical brain structures that vary in relation to phenotypic profiles. In addition to explaining the theoretical foundations and guarantees of our algorithms, we demonstrate their superiority over current state-of-the-art connectomics methods through extensive simulation studies and real-data experiments. Using a set of high-resolution connectomes obtained from genetically distinct mouse strains (including the BTBR mouse -- a standard model of autism -- and three behavioral wild-types), we illustrate how our methods successfully uncover latent information in multi-subject connectomics data and yield valuable insights into the connective correlates of neurological phenotypes that other methods do not capture. The data and code necessary to reproduce our analyses are available at https://github.com/neurodata/MCC.
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