Identifying Transdiagnostic Functional Connectivity Biomarkers for Cognitive Health and Psychopathology

项目摘要 精神疾病是一生中最常见的疾病之一，超过75%的人患有精神疾病。 从青春期开始出现症状的人。大多数精神疾病包括以下方面： 认知功能障碍被认为是使个体易患精神疾病的原因 并可作为后续疾病的早期标志。认知缺陷和行为障碍， 表明与各种疾病的广泛功能障碍有关，传统的病例对照研究 研究很难捕捉。根据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 基于行为和神经生物学测量的维度来定义精神障碍。在这个项目中， 我们将量化功能连接生物标志物，这些生物标志物可以捕捉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。与高密度 通过脑电图（EEG），我们将量化预测个体认知功能的连接生物标志物， 诊断范围内的行为（目标1）。我们将通过结合相关性来建立一个强大的预测模型 基于向量机和连接组的预测建模，以识别映射的跨诊断神经回路 连接性特征与个体认知缺陷的关系。在目标2中，我们将设计一种基于 多向典型相关分析，以有力地揭示神经回路相关的维度， 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，并比较EEG和fMRI之间的结果，使用 健康大脑网络的大规模跨诊断数据库。拟议的研究将导致一个 用于跨诊断EEG连通性的稳健量化的创新和可推广的解决方案 预测个体认知能力和描述精神病理行为维度的生物标志物 跨精神疾病。该项目的成功结果将产生可翻译的生物标志物交叉 符合RDoC目标的当前诊断边界，并为EEG连接提供新途径- 基于心理病理学的transdiagnosis研究，从而代表了迈向 发展个性化治疗以改善心理健康。我们将发布开发的工具， 公开提供，以促进精神病学中的其他跨诊断神经成像研究。

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.