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为是有限的，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。临床异质性和疾病分类学差异的不精确性是限制更好地理解病因、预防和治疗的根本性混杂因素。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研究中是可变的，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。连接组学是一个总括性术语，指的是科学尝试准确地映射组成大脑的神经元件和连接的集合，这些神经元件和连接统称为人类连接组。我们的应用程序有望发现潜在的，同质的，连接表型使用神经影像学工具，这是免费的传统诊断边界的限制，并与临床表现。 SZ、BP和健康对照受试者将使用最先进的“Connectome Skyra”（一种由华盛顿大学NIH人类连接组项目使用的优化MRI扫描仪）进行扫描，以获得极高分辨率的脑扩散和功能连接图像。我们的目标是确定大脑签名和网络模式，涉及精神病，情感和认知缺陷在所有群体中使用扩散MRI和静息状态功能连接MRI。我们的分类方法将采用计算工具，包括图论和基于支持向量机的模式分类，以获得具有独特的行为/认知特征和大脑连接模式的多个隔离的个体集群。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”，我们开发了该方法用于神经成像数据集，以识别基于全脑连接模式的亚组，然后对整个大脑的白色物质束进行体素解构。我们的应用程序受益于其多学科的合作者和顾问，包括来自人类连接组项目的几位关键研究人员。

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.