CONNECTOMICS IN PSYCHIATRIC CLASSIFICATION

描述(由申请人提供)：基于现象学的精神障碍的传统概念化越来越被认识到是有限的，但到目前为止，我们还缺乏一条通往更有效方法的明确道路。临床异质性和病因学区别的不精确本质是从根本上限制更好地了解病因、预防和治疗的混杂因素。精神分裂症(SZ)和双相情感障碍(BP)等主要精神疾病的神经连接在不同的研究中一直是不同的，这些研究无可争议地反映了具有不同病因和重叠临床表现的多种疾病过程。连接学是一个总括的术语，指的是准确绘制组成大脑的一组神经元素和连接的科学尝试，统称为人类连接组。我们的应用程序承诺使用神经成像工具发现潜在的、同质性的、连接的表型，这些工具不受传统诊断边界的限制，并且与临床表现相关。 SZ、BP和健康对照组受试者将使用最先进的“Connectome Skyra”进行扫描，这是华盛顿大学NIH人类连接项目使用的一种优化的MRI扫描仪，以获得异常高分辨率的脑扩散和功能连接图像。我们的目标是使用弥散磁共振成像和静息状态功能连接磁共振成像，在所有组中识别与精神病、情感和认知缺陷相关的大脑特征和网络模式。我们的分类方法将使用包括图论和基于支持向量机的模式分类在内的计算工具来推导出具有独特的行为/认知简档和大脑连接模式的多个分离的个体集群。我们还将使用新的无监督方法“基于非负矩阵分解的双聚类”，这是我们开发的用于神经成像数据集的方法，用于在体视图解构整个大脑的白质束后，基于全脑连接的模式识别子组。我们的应用程序得益于它的多学科合作者和顾问，包括来自人类连接组项目的几名关键研究人员。

DESCRIPTION (provided by applicant): Traditional conceptualization of mental disorders based on phenomenology is increasingly recognized as limited, but to date, we have lacked a clear path forward toward a more valid approach. Clinical heterogeneity and the imprecise nature of nosological distinctions represent fundamentally confounding factors limiting a better understanding of etiology, prevention and treatment. Neural connectivity of the major psychiatric disorders such as schizophrenia (SZ) and bipolar disorder (BP) has been variable across studies, which inarguably reflect multiple disease processes with distinct etiologies and overlapping clinical manifestations. Connectomics is an umbrella term that refers to scientific attempts to accurately map the set of neural elements and connections comprising the brain collectively referred to as the human connectome. Our application promises to uncover latent, homogenous, connectivity phenotypes using neuroimaging tools, which are free from the limitations of traditional diagnostic boundaries, and which correlate with clinical manifestations. SZ, BP and healthy control subjects will be scanned using the state-of-the-art "Connectome Skyra", an optimized MRI scanner used by the NIH Human Connectome Project at Washington University, to obtain exceptionally high-resolution brain diffusion and functional connectivity images. We aim to identify brain signatures and network patterns that relate to psychosis, affectivity and cognitive deficits across all groups using diffusion MRI and resting-state functional connectivity MRI. Our classification methods will employ computational tools that include graph theory and support vector machine based pattern classification to derive multiple segregate clusters of individuals with unique patterns of behavioral/cognitive profiles and brain connectivity. We will also use the novel unsupervised method of "Non-Negative Matrix Factorization-Based Biclustering", which we developed for use on neuroimaging datasets to identify subgroups based on patterns of whole brain connectivity following voxelwise deconstruction of the entire brain's white matter tracts. Our application benefits from its multi-disciplinary collaborators and consultants, including several key investigators from the Human Connectome Project.