Neuropsychiatric Classification via Connectivity and Machine Learning

通过连接和机器学习进行神经精神分类

• 批准号: 8808026
• 负责人: ALAN ANTICEVIC
• 金额: $ 7.41万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-27 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8808026
• 关键词: Address; Adult; Algorithms; Amygdaloid structure; Anterior; Arbitration; Architecture; Back; Base of the Brain; Behavior; Behavioral; Biological Neural Networks; Biology; Bipolar Disorder; Brain; Categories; Chronic Schizophrenia; Classification; Clinical; Complex; Corpus striatum structure; Data; Data Analyses; Data Collection; Data Set; Development; Diagnosis; Diagnostic; Diagnostic and Statistical Manual of Mental Disorders; Disease; Etiology; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Heterogeneity; Human; Human Resources; Hybrids; Individual; Intervention; Machine Learning; Major Depressive Disorder; Maps; Measures; Mental disorders; Metabolic; Methodology; National Institute of Mental Health; Neurosciences; Obsessive-Compulsive Disorder; Outcome; Pathology; Patients; Pattern; Performance; Phenotype; Plague; Post-Traumatic Stress Disorders; Process; Psychiatric Diagnosis; Quality Control; Research Domain Criteria; Rest; Scanning; Scheme; Schizophrenia; Science; Secondary to; Seeds; Signal Transduction; Speed; Surrogate Markers; Symptoms; System; Testing; Translating; Validation; Work; base; blood oxygen level dependent; case control; cingulate cortex; clinical Diagnosis; cost; disease classification; infancy; innovation; insight; neuroimaging; neuropsychiatry; novel; public health relevance; relating to nervous system; tool

DESCRIPTION (provided by applicant): The diagnostic system for neuropsychiatric conditions embodied in the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM) is based on clusters of symptoms rather than on underlying etiology or pathophysiology. The establishment of reliable diagnoses was a critical step in the advancement of psychiatric science three decades ago, but now it holds the field back by concealing relationships between brain biology and individual patients' symptoms - relationships that are obscure under the best of circumstances. This realization motivates a search for an alternative, brain-based diagnostic system, in the form of the NIMH's Research Domain Criteria (RDoC) initiative. The development of such an alternative diagnostic framework is in its infancy, and new strategies are needed for the rational categorization of pathophysiological states. We have successfully used data-driven analysis of functional connectivity data, derived from functional neuroimaging of the brain at rest. This approach has revealed neural dysconnectivity across several neuropsychiatric conditions. We will apply these data-driven approaches, in conjunction with leading machine learning algorithms, to quantify dysconnectivity patterns across and within major DSM disorders. We have assembled a dataset of 707 resting-state scans, performed on state-of-the-art 3T scanners and passing rigorous quality control standards, comprising five major DSM diagnoses: schizophrenia, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, and post-traumatic stress disorder, with matched controls for each. Accompanying symptom assessments were administered by highly skilled personnel. This large hybrid dataset permits an unprecedented cross-diagnostic, data-driven search for shared or distinct dysconnectivity across diagnoses. Specifically, we will employ a powerful multi-tiered analytic approach using: fully data-driven connectivity analysis, focusing on networks defined a priori by work in healthy subjects, and a seed-based approach focused on circuits associated with the constituent DSM diagnoses. We hypothesize several possible outcomes. First, patient groups derived from the data-driven connectivity analyses may indeed map onto symptom-based DSM diagnoses. This would be a validation of a symptom- focused nosology, at least across these conditions. Second, data-driven analysis may identify new categories that cut across DSM diagnoses. Third, results may follow continua of dysconnectivity, such as those proposed by the RDoC framework. A more complex outcome that blends these patterns is also probable. Finally, emergent patterns will be correlated against symptom measures, within and across disorders. Irrespective of the ultimate pattern, results of this project will critically inform ongoing effort to refine a diagnostic scheme for psychiatric disorders that is firmly grounded in their pathophysiology. Furthermore, the methodology will be applicable to other datasets. We anticipate that this approach will provide a key pillar to the development of a brain-based understanding of the heterogeneity of psychiatric disease.

描述（由申请人提供）：精神疾病诊断和统计手册（DSM）中体现的神经精神疾病的诊断系统是基于症状的集群，而不是基于潜在的病因学或病理生理学。三十年前，可靠诊断的建立是精神病学进步的关键一步，但现在，由于隐藏了大脑生物学和个体患者症状之间的关系，这种关系在最好的情况下也是模糊的，因此阻碍了这一领域的发展。这种认识促使人们以NIMH的研究领域标准（RDoC）计划的形式，寻找一种替代的、基于大脑的诊断系统。这种替代诊断框架的发展尚处于起步阶段，需要对病理生理状态进行合理分类的新策略。我们已经成功地使用了数据驱动的功能连接数据分析，这些数据来源于休息时大脑的功能神经成像。这种方法揭示了几种神经精神疾病的神经连接障碍。我们将应用这些数据驱动的方法，结合领先的机器学习算法，量化主要DSM障碍之间和内部的连接障碍模式。我们收集了707个静态扫描数据集，使用最先进的3T扫描仪进行扫描，并通过严格的质量控制标准，包括DSM的五种主要诊断：精神分裂症、双相情感障碍、重度抑郁症、强迫症和创伤后应激障碍，每一种都有匹配的对照。伴随的症状评估由高技能人员管理。这个大型混合数据集允许前所未有的交叉诊断，数据驱动搜索跨诊断共享或独特的连接障碍。具体来说，我们将采用一种强大的多层分析方法：完全数据驱动的连通性分析，专注于健康受试者工作中先验定义的网络，以及基于种子的方法，专注于与DSM组成诊断相关的电路。我们假设了几种可能的结果。首先，从数据驱动的连接性分析中得出的患者群体可能确实映射到基于症状的DSM诊断。这将是一个以症状为中心的病分学的验证，至少在这些情况下。其次，数据驱动的分析可能会识别出与DSM诊断相吻合的新类别。第三，结果可能遵循连接障碍的连续性，如RDoC框架提出的那些。混合这些模式的更复杂的结果也是可能的。最后，紧急模式将与症状措施相关联，在内部和跨障碍。无论最终的模式如何，这个项目的结果将为正在进行的完善精神疾病诊断方案的努力提供重要信息，该方案牢固地建立在其病理生理学的基础上。此外，该方法将适用于其他数据集。我们预计，这种方法将为基于大脑的精神疾病异质性理解的发展提供一个关键支柱。