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: 10553233
• 负责人: Jared Xavier Van Snellenberg
• 金额: $ 70.2万
• 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/10553233
• 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; Process; Psychiatric Diagnosis; Psychiatry; Recommendation; 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; behavior test; biomarker identification; biotypes; 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; neural circuit; neural correlate; neural network; patient subsets; potential biomarker; psychotic symptoms; 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.

项目总结/摘要 二十多年来的研究一直集中在阐明工作记忆（WM）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 与患者的功能结果密切相关，而不是精神病症状，并且它们被认为是由患者的精神症状引起的。 多巴胺能功能的改变可能是精神分裂症发病机制的基础。 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， 在功能性磁共振成像中， 精神分裂症患者的功能磁共振成像研究。相反，经验证据表明，至少 三种不同的认知能力有助于工作记忆任务的表现：注意控制，短期 记忆容量和长期（或次级）记忆检索。据推测，这些都是可分离的 神经基质，并可能在不同的患者中受到不同的损害，这将有助于模糊 在患者的病例对照fMRI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），尽管这些与 那些基于上述经验工作的，迄今没有经验工作试图确定 不同的工作记忆任务在多大程度上利用了这四个假定的子结构。这个项目的首要目标是 应用程序是进行大规模的潜在变量分析最常用的工作记忆任务中， 精神分裂症的功能磁共振成像文献，沿着广泛的其他认知任务，以澄清 潜在的认知能力（或子结构），有助于WM任务的执行和识别神经 这些经验鉴定的子结构的相关性。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，以便为潜变量分析提供稳健的数据集 使用因素分析和结构方程建模技术，将确定子结构， 在每个WM任务中的表现。接下来，80名精神分裂症患者和80名匹配的对照 参与者将经历相同的任务电池，但将在功能磁共振成像扫描期间执行7个WM任务， 以鉴定在精神分裂症中被特异性破坏的亚结构的神经相关物。这 这项工作将有助于推进我们对精神分裂症WM缺陷的理解， 目标，以及未来调查人员可以用来瞄准它们的最佳任务，这些任务在 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 旨在寻找精神分裂症认知缺陷的治疗方法。