Identifying Transdiagnostic Functional Connectivity Biomarkers for Cognitive Health and Psychopathology

识别认知健康和精神病理学的跨诊断功能连接生物标志物

• 批准号: 10667086
• 负责人: Yu Zhang
• 金额: $ 18.48万
• 依托单位: LEHIGH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10667086
• 关键词: Adolescence; Adolescent; Anxiety; Architecture; Behavior; Behavioral; Biological Markers; Brain; Case/Control Studies; Categories; Child; Cognition; Cognitive; Cognitive deficits; Complement; Data; Databases; Development; Diagnostic; Dimensions; Disease; Electroencephalography; Foundations; Functional Magnetic Resonance Imaging; Goals; Health; Human; Impaired cognition; Individual; Link; Longevity; Maps; Measures; Mental Health; Mental disorders; Modeling; Moods; National Institute of Mental Health; Neurobiology; Outcome; Pattern; Phenotype; Psychiatry; Psychopathology; Publishing; Research; Research Domain Criteria; Rest; Signal Transduction; Source; Symptoms; Testing; Variant; Youth; analytical tool; behavior measurement; behavioral phenotyping; brain behavior; brain dysfunction; clinical translation; clinically relevant; cognitive ability; cognitive function; comorbidity; connectome based predictive modeling; cost effective; density; design; diagnostic biomarker; dimensional analysis; efficacy evaluation; functional disability; improved; innovation; neural; neural circuit; neural correlate; neuroimaging; open source; personalized therapeutic; predictive marker; predictive modeling; source localization; tool; vector

Project Abstract Psychiatric disorders are among the most common illnesses across the lifespan, with more than 75% of individuals developing symptoms beginning in adolescence. Most psychiatric disorders include aspects of cognitive dysfunctions that have been suggested to predispose individuals to develop the psychiatric conditions and may serve as early markers of subsequent illness. Cognitive deficits and behavioral disturbances were indicated to be related to broad-based functional impairments across disorders, which traditional case-control studies are hard to capture. Following the Research Domain Criteria (RDoC) initiative launched by NIMH, there is an urgent need for developing biomarkers that can cross current diagnostic boundaries and drive new ways of defining psychiatric disorders based on dimensions of behavioral and neurobiological measures. In this project, we will quantify functional connectivity biomarkers that capture brain dysfunctions spanning multiple psychiatric disorders for an improved understanding of cognitive deficits and psychopathology. With high-density electroencephalography (EEG), we will quantify connectivity biomarkers predictive of individual cognitive behavior across the diagnostic spectrum (Aim 1). We will build a robust prediction model by combining relevance vector machine and connectome-based predictive modeling to identify transdiagnostic neural circuits that map the connectivity features to individual cognitive deficits. In Aim 2, we will design a dimensional approach based on multiway canonical correlation analysis to robustly reveal neural circuit-correlated dimensions of psychopathology. This approach allows us to jointly identify brain dysfunctions and dimensional behavioral phenotypes. We will evaluate these tools and compare the obtained results between EEG and fMRI using a large-scale transdiagnostic database from Healthy Brain Network. The proposed research will lead to an innovative and generalizable solution for the robust quantification of transdiagnostic EEG connectivity biomarkers that predict individual cognitive ability and delineate dimensions of psychopathological behavior across psychiatric disorders. Successful outcomes of the project will produce translatable biomarkers crossing current diagnostic boundaries in line with the goals of RDoC and provide a new avenue for EEG connectivity- based transdiagnostic study of psychopathology, thereby representing an important step towards the development of personalized therapeutics for improved mental health. We will release the developed tools to be publicly available to facilitate other transdiagnostic neuroimaging studies in psychiatry.

项目摘要 精神疾病是一生中最常见的疾病之一，超过 75% 个体从青春期开始出现症状。大多数精神疾病包括以下几个方面 认知功能障碍被认为容易导致个体患上精神疾病 并可能作为后续疾病的早期标志。认知缺陷和行为障碍 表明与跨疾病的广泛功能障碍有关，传统的病例对照 研究很难捕捉。遵循 NIMH 发起的研究领域标准 (RDoC) 倡议， 迫切需要开发能够跨越当前诊断界限并推动新方法的生物标志物 根据行为和神经生物学测量的维度来定义精神疾病。在这个项目中， 我们将量化功能连接生物标志物，捕获跨越多种精神疾病的大脑功能障碍 疾病，以提高对认知缺陷和精神病理学的理解。具有高密度 脑电图（EEG），我们将量化预测个体认知的连接生物标志物 跨诊断范围的行为（目标 1）。我们将通过结合相关性来构建稳健的预测模型 向量机和基于连接组的预测模型来识别映射的跨诊断神经回路 个体认知缺陷的连通性特征。在目标 2 中，我们将设计一种基于维度的方法 多路典型相关分析可以稳健地揭示神经回路相关维度 精神病理学。这种方法使我们能够共同识别大脑功能障碍和维度行为 表型。我们将评估这些工具并使用脑电图和功能磁共振成像来比较获得的结果 来自健康大脑网络的大规模跨诊断数据库。拟议的研究将导致 用于跨诊断脑电图连接的稳健量化的创新且可推广的解决方案 预测个体认知能力并描绘精神病理行为维度的生物标志物 跨越精神疾病。该项目的成功成果将产生跨领域的可翻译生物标志物 当前的诊断边界符合 RDoC 的目标，并为 EEG 连接提供了新的途径 - 基于精神病理学的跨诊断研究，从而代表了迈向精神病理学的重要一步 开发个性化疗法以改善心理健康。我们将发布开发的工具 公开可促进精神病学中的其他跨诊断神经影像学研究。