CRCNS: Geometry-based Brain Connectome Analysis

CRCNS：基于几何的脑连接组分析

• 批准号: 9788529
• 负责人: David Brian Dunson
• 金额: $ 31.15万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-19 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788529
• 关键词: Age; Alcohol or Other Drugs use; Algorithms; Base of the Brain; Behavior; Brain; Cells; Characteristics; Collection; Communication; Computer software; Data; Data Analyses; Data Set; Diffusion Magnetic Resonance Imaging; Disease; Documentation; Epidemiologist; Fiber; Gender; Geometry; Human; Image; Imaging technology; Individual; Location; Magnetic Resonance Imaging; Measurement; Measures; Mental Health; Mental disorders; Methods; Nature; Neurologic; Neurosciences; Noise; Performance; Phenotype; Play; Prevention; Process; Reproducibility; Resolution; Risk; Role; Sampling; Sampling Errors; Scanning; Signal Transduction; Source; Specific qualifier value; Statistical Data Interpretation; Structure; Technology; Testing; Training; Work; Writing; animal data; base; biobank; clinical practice; cognitive ability; cognitive function; computerized data processing; connectome; disorder risk; geometric structure; improved; insight; interest; multi-scale modeling; neuropsychiatric disorder; neuropsychiatry; novel; reconstruction; relating to nervous system; simulation; statistics; tool; trait; white matter

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.

成像技术取得了显着的进步，在许多人类研究中常规且普遍使用，可非侵入性地测量人类大脑的结构和功能。扩散磁共振成像 (dMRI) 和结构磁共振成像 (sMRI) 用于推断数百万个相互连接的白质纤维束（称为大脑连接组）的位置，这些纤维束充当大脑神经活动和通信的高速公路。越来越多的证据表明，个体的大脑连接组在认知功能、行为以及发生心理健康和神经精神疾病的风险中发挥着重要作用。提高对大脑连接组结构与表型和暴露之间关系的机械理解有可能彻底改变精神健康疾病的预防和治疗。然而，图像采集、连接组构建和数据分析领域的最新技术之间存在巨大差距，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性工具箱，以更好地构建、表示和分析人脑连接组。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，以加深对大脑连接组如何根据个体特征和暴露以及神经精神状况而变化的理解。该工具箱将经过严格验证，包括基于扫描-重新扫描数据、样本外预测性能、模拟研究中的功效和第一类错误率以及结果的机械解释性来评估再现性和辨别能力。有四个具体目标：（1）连接组的几何重建，以减少测量误差并增强鲁棒性、再现性和辨别能力； (2) 连接组的几何表示以新颖的方式表征连接组，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一测量的典型邻接矩阵表示中可用的更多信息； （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）; (4) 传播公开的、记录齐全的软件，用于日常实施 建议的工具箱。