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.

在许多人类研究中常规和普遍使用的成像技术已经有了显著的进步，这种技术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)被用来推断数百万相互连接的白质纤维束-称为大脑连接体-的位置，这些纤维束充当神经活动和跨大脑通信的高速公路。越来越多的证据表明，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障碍的风险方面发挥着基础性作用。改进对大脑连接体结构与表型和暴露之间关系的机械性理解，有可能彻底改变精神健康障碍的预防和治疗。然而，在图像采集、连接体构建和数据分析方面的最新水平之间的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。这些工具将被应用于人类连接体项目和英国生物库数据集，以加强对大脑连接体如何根据个人特征和暴露以及神经精神疾病的变化的理解。该工具箱将经过严格验证，包括基于扫描-重新扫描数据的重复性和区分性评估、样本外预测性能、模拟研究中的功率和I类错误率以及结果的机械可解释性。有四个具体目标：(1)连接的几何重建，以减少测量误差，并增强稳健性、再现性和辨别力；(2)连接的几何表示，以新颖的方式表征连接，编码比典型的邻接矩阵表示所提供的更多的信息，该表示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；(3)通过新的多尺度模型和算法将连接与人类特征联系起来，这些模型和算法提高了统计分析中大脑连接与表型(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)之间的能力和机械洞察力； (4)传播公开提供的、有充分文件记录的软件，用于日常执行 建议的工具箱。