CRCNS: Geometry-based Brain Connectome Analysis

成像技术已经取得了显着的进步，在许多人类研究中常规和普遍使用，非侵入性地测量人类大脑结构和功能。弥散磁共振成像（dMRI）和结构磁共振成像（sMRI）被用来推断数百万个相互连接的白色物质纤维束（称为大脑连接体）的位置，这些纤维束是大脑神经活动和通信的高速公路。越来越多的证据表明，一个人的大脑连接体在认知功能，行为以及发展心理健康和神经精神疾病的风险中起着重要作用。对大脑连接体结构与表型和暴露之间关系的机械理解的改善有可能彻底改变心理健康障碍的预防和治疗。然而，图像采集与连接体构建和数据分析的最新技术水平之间存在巨大差距，限制了进展。该项目开发了一个数据处理和分析方法的变革工具箱，用于更好地构建，表示和分析人脑连接体。这些工具将应用于人类连接组项目和英国生物银行数据集，以加强对大脑连接组如何根据个人特征和暴露以及神经精神疾病而变化的理解。该工具箱将经过严格验证，包括基于扫描-再扫描数据的再现性和区分能力评估、样本外预测性能、模拟研究中的功效和I类错误率以及结果的机械可解释性。有四个具体目标：（2）以新颖的方式表征连接体的连接体的几何表示，以编码比依赖于预先指定的感兴趣区域之间的连接强度的单个测量的典型邻接矩阵表示中可用的信息多得多的信息;（3）通过新的多尺度模型和算法将连接组与人类特征联系起来，这些模型和算法提高了将大脑连接组与表型（认知功能，行为，心理健康状况），暴露（物质使用）和协变量（年龄，性别）联系起来的统计分析的能力和机械洞察力; (4)传播可公开获得的、有良好记录的软件， 建议的工具箱

There have been remarkable advances in imaging technology, used routinely and pervasively in many human studies, that non-invasively measures human brain structure and function. Diffusion magnetic resonance imaging (dMRI) and structural MRI (sMRI) are used to infer locations of millions of interconnected white matter fiber tracts-known as the brain connectome-that act as highways for neural activity and communication across the brain. Evidence is increasing that an individual's brain connectome plays a fundamental role in cognitive functioning, behavior, and the risk of developing mental health and neuropsychiatric disorders. Improved mechanistic understanding of relationships between brain connectome structure and phenotypes and exposures has the potential to revolutionize prevention and treatment of mental health disorders. However, large gaps between the state of the art in image acquisition and in connectome construction and data analysis have limited progress. This project develops a transformative toolbox of data processing and analysis methods for better construction, representation, and analysis of human brain connectomes. These tools will be applied to the Human Connectome Project and UK Biobank datasets, to enhance understanding of how the brain connectome varies according to individual traits and exposures and with neuropsychiatric conditions. The toolbox will be rigorously validated, including assessments of reproducibility and discriminative ability based on scan-rescan data, out-of-sample predictive performance, power and type I error rates in simulation studies, and mechanistic interpretability of the results. There are four Specific Aims: (1) Geometric reconstruction of connectomes to reduce measurement errors and enhance robustness, reproducibility and discriminative power; (2) Geometric representation of connectomes characterizing connectomes in novel ways to encode much more information than is available in typical adjacency matrix representations that rely on a single measure of connection strength between pre-specified regions of interest; (3) Relating connectomes to human traits through new multiscale models and algorithms that improve power and mechanistic insight in statistical analyses relating brain connectomes to phenotypes (cognitive functioning, behavior, mental health conditions), exposures (substance use), and covariates (age, gender); (4) Dissemination of publicly available, well-documented software for routine implementation of the proposed toolbox.