CONNECTOMICS IN PSYCHIATRIC CLASSIFICATION

精神病学分类中的连接组学

• 批准号: 9102252
• 负责人: DANIEL MAMAH
• 金额: $ 49.89万
• 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/9102252
• 关键词: Advisory Committees; 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; Diagnostic and Statistical Manual of Mental Disorders; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Dorsal; Elements; Episodic memory; Etiology; Functional disorder; Health; 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; 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; cognitive system; computerized tools; connectome; disorder control; graph theory; innovation; neuroimaging; novel; phenomenological models; programs; 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）：基于现象学的传统概念化越来越多地被认为是有限的，但迄今为止，我们已经缺乏通往更有效方法的明确途径。临床异质性和牙文学区分的不精确性是从根本上限制了对病因，预防和治疗的更好理解的混淆因素。在整个研究中，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整个研究中都是可变的，这些研究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。连接组学是一个伞术语，它是指准确地绘制包含大脑的神经元素和连接集的科学尝试，这些神经元素和连接集体统称为人类连接组。我们的应用程序有望使用神经影像学工具揭示潜在的，同质的连通性表型，这些工具没有传统诊断边界的局限性，并且与临床表现相关。 SZ，BP和健康控制受试者将使用最先进的“ Connectome Skyra”扫描，这是华盛顿大学NIH人类Connectome Project使用的优化的MRI扫描仪，以获得异常高分辨率的大脑扩散和功能连接性图像。我们旨在确定与精神病，情感和认知缺陷有关的大脑签名和网络模式，使用扩散MRI和静止状态功能连通性MRI。我们的分类方法将采用计算工具，其中包括图理论和支持向量机的模式分类，以得出具有行为/认知概况和大脑连接性独特模式的个体的多个隔离簇。我们还将使用新型的无监督方法的“基于非负基质分解的双簇”的方法，我们开发用于神经成像数据集的方法，以基于整个大脑的白质系的voxelwise解构后整个大脑连接性的模式来识别亚组。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Connectome项目的几位主要调查员。