CONNECTOMICS IN PSYCHIATRIC CLASSIFICATION

描述（由申请人提供）：基于现象学的传统精神障碍概念化越来越被认为是有限的，但到目前为止，我们还缺乏一条通向更有效方法的明确途径。临床异质性和不精确的病分学区分从根本上限制了对病因、预防和治疗的更好理解。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通性在研究中一直存在差异，这无疑反映了具有不同病因和重叠临床表现的多种疾病过程。连接组学是一个总称，指的是科学尝试准确地绘制组成大脑的神经元素和连接集，统称为人类连接组。我们的应用程序有望利用神经成像工具发现潜在的、同质的、连通性的表型，这些工具不受传统诊断界限的限制，并且与临床表现相关。

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.