A large sample machine learning network analysis of vertex cortical thickness measures for high resolution definition of PTSD related cortical structure abnormalities

大样本机器学习网络分析顶点皮质厚度测量，以高分辨率定义 PTSD 相关皮质结构异常

• 批准号: 10551850
• 负责人: Xin Wang
• 金额: $ 18.65万
• 依托单位: UNIVERSITY OF TOLEDO HEALTH SCI CAMPUS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-17 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10551850
• 关键词: Address; Affect; Area; Awareness; Basic Science; Brain; Clinical; Data; Data Aggregation; Databases; Diagnosis; Disease; Emotions; Individual; Label; Lasso; Learning; Linear Models; Link; Location; Logistic Regressions; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Measures; Mental Depression; Mental disorders; Outcome; Pathway Analysis; Patients; Pattern; Persons; Post-Traumatic Stress Disorders; Procedures; Reporting; Reproducibility; Research; Research Personnel; Resolution; Rest; Sample Size; Sampling; Seminal; Site; Structural defect; Structure; Surface; Symptoms; Testing; Thick; Thinking; Thinness; Time; Work; age effect; clinical investigation; cohort; comorbidity; cost; data repository; emotion regulation; imaging study; innovation; insight; interest; linear transformation; nervous system disorder; novel; novel strategies; psychiatric symptom; working group

Project Summary Posttraumatic stress disorder (PTSD) affects millions of people globally. Existing studies link PTSD symptoms to cortical structural changes including, for example, thinning in prefrontal and other cortical regions. Correlations of PTSD changes in different regions of interest (ROIs) suggest involvement of multiple networks. ROI findings use average measures of thickness across large cortical regions, thus making localization of foci of thickness changes very difficult. Recent vertex-based work, using measures of thickness beneath individual vertex surface areas of ~1 mm2, has begun to identify delimited clusters of cortical vertices with focal thinning in PTSD patients, but associations of changes across vertices remain unstudied. Existing studies of ~300,000 vertices per subject with small sample sizes may lack statistical power, reproducibility and the capacity to resolve cross vertex relationships. Thus large sample, vertex-based work is arguably needed to advance understanding of PTSD related cortical thickness changes. This will require innovations in analyses and transformative changes in approach, because current approaches cannot jointly assess cortical thickness and network associations from data incorporating hundreds of thousands of vertices per subject in studies involving thousands of subjects. With the above rationale, the planned work uses novel approaches, first, to compile the largest existing database (~19,000 subjects) of vertex-based cortical thickness and associated demographic and comorbid data for comparing PTSD and non-PTSD subjects and, second, to apply data driven, multi-vertex pattern and network analysis (MVPNA) to jointly identify localized vertices and distributed networks of vertex clusters that have associated structural abnormalities which predict PTSD. This work will advance current understanding by: (1) identifying currently unresolved focal cortical sites of thickness change in PTSD patients, (2) providing seminal insight into vertex-based patterns of PTSD thickness change networks, and (3) developing new MVPNA approaches for systematic assessment of cortical vertex data from large samples. The proposed work will have further implications for basic research and clinical investigation of cortical structural changes that occur with other psychiatric and neurological disorders.

项目摘要 创伤后应激障碍（PTSD）在全球影响着数百万人。现有研究链接PTSD 皮质结构变化的症状，包括在前额叶和其他皮质区域中变薄。 PTSD不同感兴趣区域（ROI）中PTSD变化的相关性表明多个网络的参与。 投资回报率调查结果使用大型皮质区域的平均厚度度量，从而使焦点定位 厚度的变化非常困难。最近的基于顶点的工作，使用个体下方的厚度度量 〜1 mm2的顶点表面积已开始识别具有局灶性变薄的皮质顶点的分界簇 在PTSD患者中，但跨顶点变化的关联仍未研究。现有的研究〜300,000 每个受试者的顶点小样本量可能缺乏统计能力，可重复性和能力 解决跨顶点关系。因此，可以说需要大量样本，基于顶点的工作才能提高 了解PTSD相关的皮质厚度变化。这将需要分析和 方法的变革性变化，因为当前的方法无法共同评估皮质厚度和 来自数据的网络协会在涉及的研究中包含数十万个主题的数十万个顶点 成千上万的主题。 通过上述基本原理，计划的工作首先采用新颖的方法来编译现有的最大现有方法 基于顶点的皮质厚度以及相关的人群和合并症的数据库（约19,000名受试者） 比较PTSD和非PTSD受试者的数据，其次是应用数据驱动的多个Vertex模式和 网络分析（MVPNA）共同识别顶点簇的局部顶点和分布式网络 具有预测PTSD的相关结构异常。这项工作将通过： （1）识别当前未解决的PTSD患者厚度变化的局灶性皮质位点（2）提供 对基于顶点的PTSD厚度改变网络的基于顶点的模式的开创性洞察力，以及（3）开发新的 MVPNA用于系统评估大型样品皮质顶点数据的方法。拟议的工作 将对基础研究和临床研究具有进一步的影响，对皮质结构变化 发生其他精神病和神经系统疾病。