CONNECTOMICS IN PSYCHIATRIC CLASSIFICATION

精神病学分类中的连接组学

• 批准号: 9265953
• 负责人: DANIEL MAMAH
• 金额: $ 49.63万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-04 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9265953
• 关键词: Advisory Committees; Affective; Affective Symptoms; Africa; Anterior; Assessment tool; Attention; Behavior; Behavioral; Bipolar Disorder; Brain; Brain imaging; Characteristics; Classification; Clinical; Cognition; Cognitive; Cognitive deficits; Communities; Data; Data Set; Descriptor; Detection; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Dorsal; Elements; Episodic memory; Etiology; Functional disorder; Heterogeneity; Human; Image; Individual; Inferior; Intervention; Investigation; Language; Machine Learning; Magnetic Resonance Imaging; Manic; Maps; Measures; Mental Depression; Mental disorders; Methods; National Institute of Mental Health; Nature; Neurobehavioral Manifestations; Neurobiology; Parietal; Participant; Pathologic; Pattern; Phenotype; Prevention; Probability; Problem Solving; Process; Psychiatry; Psychometrics; Psychotic Disorders; Research; Research Personnel; Resolution; Rest; Scanning; Schizophrenia; Short-Term Memory; Strategic Planning; Structure; Subgroup; Symptoms; United States National Institutes of Health; Universities; Washington; Youth; base; blood pressure regulation; cognitive system; computerized tools; connectome; graph theory; innovation; multidisciplinary; multimodality; neuroimaging; novel; phenomenological models; programs; public health relevance; relating to nervous system; statistics; thalamocortical tract; tool; white matter

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.

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，但迄今为止，我们缺乏通向更有效方法的明确道路。临床异质性和疾病分类的不精确性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接在不同研究中存在差异，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术语，指的是准确绘制构成大脑的一组神经元素和连接（统称为人类连接组）的科学尝试。我们的应用有望使用神经影像工具揭示潜在的、同质的连接表型，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 和健康对照受试者将使用最先进的“Connectome Skyra”（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，以获得异常高分辨率的大脑扩散和功能连接图像。我们的目标是使用扩散 MRI 和静息态功能连接 MRI 来识别与所有群体的精神病、情感和认知缺陷相关的大脑特征和网络模式。我们的分类方法将采用计算工具，包括图论和基于支持向量机的模式分类，以派生具有独特行为/认知概况和大脑连接模式的个体的多个隔离簇。我们还将使用“基于非负矩阵分解的双聚类”的新型无监督方法，该方法是我们为神经影像数据集开发的，用于在对整个大脑白质束进行体素解构后，根据全脑连接模式来识别子组。我们的应用程序受益于其多学科合作者和顾问，包括来自人类连接组项目的几位主要研究人员。