Administrative Supplement to 1R03MH105765: Neuropsychiatric Classification via Connectivity and Machine Learning

描述(由申请人提供)：《精神疾病诊断和统计手册》(DSM)中包含的神经精神疾病诊断系统基于症状群，而不是潜在的病因学或病理生理学。30年前，可靠诊断的建立是精神病学进步的关键一步，但现在，它通过隐瞒脑生物学与个别患者症状之间的关系--在最好的情况下，这种关系是模糊的--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)倡议的形式，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还处于初级阶段，需要新的策略来合理地对病理生理状态进行分类。我们已经成功地使用了对功能连接数据的数据驱动分析，这些数据来自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，来量化主要DSM障碍之间和内部的连接障碍模式。我们已经收集了707个静息状态扫描的数据集，在最先进的3T扫描仪上进行，并通过了严格的质量控制标准，包括五种主要的DSM诊断：精神分裂症、双相情感障碍、严重抑郁障碍、强迫症和创伤后应激障碍，每种都有匹配的对照。伴随的症状评估由高技能人员进行。这一大型混合数据集允许前所未有的交叉诊断、数据驱动的搜索，以跨诊断共享或不同的连接障碍。具体地说，我们将使用强大的多层次分析方法：完全数据驱动的连通性分析，重点关注健康受试者通过工作预先定义的网络，以及基于种子的方法，重点关注与构成DSM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得出的患者组确实可能映射到基于症状的DSM诊断。这将是对以症状为重点的病因学的验证，至少在这些情况下是这样。其次，数据驱动的分析可能会识别跨越DSM诊断的新类别。第三，结果可能伴随着连续不断的连接障碍，比如RDoC框架提出的结果。将这些模式融合在一起的更复杂的结果也是可能的。最后，紧急模式将与疾病内部和跨障碍的症状衡量标准相关联。无论最终模式如何，该项目的结果将为正在进行的努力提供关键信息，以完善牢固地植根于其病理生理学基础上的精神障碍诊断方案。此外，该方法将适用于其他数据集。我们预计，这种方法将为发展基于大脑的对精神疾病异质性的理解提供关键支柱。

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.