A data-driven reconceptualization of the RDoC construct of working memory: Neural correlates of underlying factors and implications for schizophrenia

工作记忆 RDoC 结构的数据驱动重新概念化：潜在因素的神经相关性及其对精神分裂症的影响

• 批准号: 10339446
• 负责人: Jared Xavier Van Snellenberg
• 金额: $ 70.12万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-03-17 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10339446
• 关键词: Area; Attention; Behavioral; Biological Assay; Brain; Brain region; Chronic; Clinical Trials; Cognition; Cognitive; Cognitive deficits; Collection; Data; Data Set; Economic Burden; Equation; Exhibits; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Homelessness; Impairment; Imprisonment; Individual; Life Expectancy; Link; Literature; Maintenance; Maps; Memory; Memory impairment; Modeling; Motivation; National Institute of Mental Health; Neural Network Simulation; Outcome; Participant; Pathogenesis; Patients; Pattern; Performance; Psychiatric Diagnosis; Psychiatry; Reporting; Research; Research Domain Criteria; Research Personnel; Risk; Role; Sampling; Schizophrenia; Short-Term Memory; Specific qualifier value; Structure; Symptoms; Task Performances; Techniques; Testing; Unemployment; Update; Work; attentional control; base; behavior test; biomarker identification; case control; cognitive ability; cognitive neuroscience; cognitive process; cognitive task; convolutional neural network; disability; effective therapy; flexibility; functional MRI scan; functional outcomes; imaging study; improved; improved outcome; interest; long term memory; memory retrieval; neural circuit; neural correlate; neural network; patient subsets; potential biomarker; psychotic symptoms; relating to nervous system; repetitive transcranial magnetic stimulation; social; treatment research

Project Summary / Abstract Over two decades of research has focused on elucidating the pathophysiology of working memory (WM) deficits in patients with schizophrenia in the hopes of finding effective treatments, as these deficits are more closely linked to functional outcomes in patients than are psychotic symptoms, and they are presumed to arise from alterations in dopaminergic function that may be fundamental to the pathogenesis of schizophrenia. However, the overwhelming majority of research in this area has treated WM as a unitary cognitive ability that is assayed equally well by any of the wide range of tasks commonly employed in functional Magnetic Resonance Imaging (fMRI) studies of patients with schizophrenia. In contrast, empirical evidence suggests that at least three distinct cognitive abilities contribute to performance on WM tasks: attentional control, short-term memory capacity, and long-term (or secondary) memory retrieval. These are presumed to have dissociable neural substrates and may be differentially impaired in different patients, which would serve to obscure potential biomarkers of WM deficit in case-control fMRI studies of patients. The NIMH Research Domain Criteria (RDoC) Matrix attempts to capture some of this complexity by defining four subconstructs of WM (active maintenance, flexible updating, limited capacity, and interference controls), although these differ from those based on empirical work described above, and no empirical work to date has attempted to determine the extent to which various WM tasks tap these four putative subconstructs. The overarching goal of this application is to conduct a large-scale latent-variable analysis of the most commonly employed WM tasks in the fMRI literature of schizophrenia, along with a broad array of other cognitive tasks, in order to clarify the underlying cognitive abilities (or subconstructs) that subserve WM task performance and to identify neural correlates of these empirically identified subconstructs. To this end, 500 participants will undergo behavioral testing on 9 WM and 12 other cognitive tasks in order to provide a robust dataset for latent variable analysis using factor analytic and structural equation modeling techniques that will identify the subconstructs that underlie performance on each of the WM tasks. Next, 80 patients with schizophrenia and 80 matched control participants will undergo the same battery of tasks, but will perform 7 of the WM tasks during fMRI scanning, in order to identify neural correlates of the subconstructs that are specifically disrupted in schizophrenia. This work will help to advance our understanding of WM deficits in schizophrenia and will identify specific neural targets, and the optimal tasks that future investigators can employ to target them, that are disrupted in schizophrenia. These neural targets can then form the basis for establishing target engagement in clinical trials aimed at finding treatments for cognitive deficits in schizophrenia.

项目摘要/摘要 二十多年来的研究主要集中在阐明工作记忆的病理生理学。 精神分裂症患者的缺陷，希望找到有效的治疗方法，因为这些缺陷更多 与精神病症状相比，精神症状与患者的功能结果密切相关，并且它们被推定为 多巴胺能功能的改变可能是精神分裂症发病的基础。 然而，这一领域的绝大多数研究都将工作记忆视为一种单一的认知能力，即 在功能磁共振中通常使用的广泛任务中的任何一项都同样能很好地进行分析 精神分裂症患者的功能磁共振成像研究。相比之下，经验证据表明，至少 三种不同的认知能力对工作记忆任务的表现有贡献：注意控制、短期 记忆容量和长期(或二次)记忆提取。这些被认为是可分离的 神经底物，并可能在不同的患者中受到不同程度的损害，这将有助于掩盖 病例对照功能磁共振研究中WM缺陷的潜在生物标志物。NIMH研究领域 标准(RDoC)矩阵试图通过定义WM的四个子结构来捕捉这种复杂性 (主动维护、灵活更新、有限容量和干扰控制)，尽管这些不同于 基于上述经验性工作，到目前为止还没有经验性工作试图确定 不同的工作记忆任务在多大程度上利用了这四个假定的亚结构。这件事的首要目标是 应用程序是对最常用的WM任务进行大规模的潜变量分析 精神分裂症的fMRI文献，以及一系列其他认知任务，以澄清 潜在的认知能力(或子结构)，为工作记忆任务绩效服务，并识别神经 这些经验性识别的亚结构之间的相关性。为此，500名参与者将接受行为 对9个WM和12个其他认知任务进行测试，以便为潜在变量分析提供可靠的数据集 使用因子分析和结构方程建模技术，以确定 为每项WM任务的绩效奠定基础。接下来，80名精神分裂症患者和80名对照组 参与者将经历相同的一组任务，但将在fMRI扫描期间执行7个WM任务， 以确定精神分裂症中特定被破坏的亚结构的神经关联。这 这项工作将有助于增进我们对精神分裂症患者WM缺陷的理解，并将识别特定的神经 目标，以及未来调查人员可以用来针对这些目标的最佳任务，这些目标在 精神分裂症。然后，这些神经靶点可以形成在临床试验中建立靶点参与的基础 旨在寻找治疗精神分裂症认知缺陷的方法。