Identifying Transdiagnostic Functional Connectivity Biomarkers for Cognitive Health and Psychopathology

项目摘要 精神障碍是一生中最常见的疾病之一，超过75%的 从青春期开始出现症状的个体。大多数精神障碍包括以下方面 认知功能障碍已被认为易使个人发展成精神疾病 并可作为后续疾病的早期标志。认知缺陷和行为障碍是 表明与跨障碍的广泛功能障碍有关，传统的病例对照 研究是很难捕捉的。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记物 根据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记物，这些标记物捕捉跨越多个精神疾病的大脑功能障碍 为了更好地理解认知缺陷和精神病理学，精神障碍。具有高密度 脑电(EEG)，我们将量化预测个体认知的连接生物标记物 诊断范围内的行为(目标1)。我们将结合相关性建立一个稳健的预测模型 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 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 多向典型相关分析稳健揭示神经回路相关维度的研究 精神变态学。这种方法使我们能够联合识别大脑功能障碍和维度行为 表型。我们将对这些工具进行评估，并使用 来自健康大脑网络的大型跨诊断数据库。拟议的研究将导致 用于健壮量化跨诊断EEG连接性的创新和可推广的解决方案 预测个体认知能力和描绘精神病理行为维度的生物标记物 所有的精神疾病。该项目的成功成果将产生可翻译的生物标记物 目前的诊断界限与RDoC的目标一致，并为脑电连接提供了一条新的途径- 以精神病理学的跨诊断研究为基础，从而代表着朝着 为改善心理健康而开发个性化治疗方法。我们将把开发的工具发布为 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.