Gopalakrishnan Vivek, Chung Jaewon, Bridgeford Eric, Pedigo Benjamin D, Arroyo Jesús, Upchurch Lucy, Johnson G Allan, Wang Nian, Park Youngser, Priebe Carey E, Vogelstein Joshua T
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States.
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States.
Imaging Neurosci (Camb). 2025 May 16;3. doi: 10.1162/IMAG.a.2. eCollection 2025.
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 often 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 statistical hypothesis tests motivated by recent theoretical advances in random graph models. These tests 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 the analyses, simulations, and figures presented in this work are available athttps://github.com/neurodata/MCC.
连接组是大脑中结构和/或功能连接的图谱，它提供了一种复杂的神经生物学表型表征，连接组就建立在这些表型之上。这种信息丰富的数据模式有可能改变我们对大脑连接模式与神经过程、障碍及疾病之间关系的理解。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主体连接组学数据集：许多当前方法要么仅设计用于分析单个连接组，要么利用启发式图形统计，而这些统计方法无法捕捉大脑区域之间多尺度连接的完整拓扑结构。为了实现更严格的连接组学分析，我们引入了一组基于随机图模型近期理论进展的稳健且可解释的统计假设检验。这些检验有助于同时分析跨不同网络拓扑尺度的多个连接组，从而能够稳健且可重复地发现与表型特征相关的分层脑结构。除了解释我们算法的理论基础和保障措施外，我们还通过广泛的模拟研究和实际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——一种自闭症标准模型——和三种行为野生型）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地在多主体连接组学数据中发现潜在信息，并对其他方法未捕捉到的神经表型的连接相关性产生有价值的见解。本研究中用于重现分析、模拟和图表所需的数据和代码可在https://github.com/neurodata/MCC获取。
