Computational Core

计算核心

• 批准号: 8074013
• 负责人: JOSEPH T. COYLE
• 金额: $ 4.7万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8074013
• 关键词: Address; Behavior; Behavioral; Biological; Biological Assay; California; Categories; Characteristics; Clinical; Cognitive; Computer Simulation; Cues; Dopamine; Etiology; Failure; Funding; Grant; Hippocampus (Brain); Individual; Interneurons; Light; Link; Memory; Modeling; N-Methylaspartate; Patients; Performance; Process; Psychological Tests; Research; Schizophrenia; Semantics; Symptoms; System; Testing; Training; Verbal Learning; Work; analytical tool; cognitive function; endophenotype; entorhinal cortex; indexing; psychologic

Detailed modeling in the Computational Core will address hypotheses concerning the specific projects in this grant, linking the cellular, systems, and behavioral levels. Our central analytical tool will be the hippocampus-entorhinal cortex model that was developed in the last funding period. We will increase its degree of biological realism in order to accommodate new research findings generated by the individual projects. Its purpose is to shed light on the hippocampal etiology of schizophrenia at an intermediate or "endophenotype" level. Because it is important that our model manifest behaviors that are clinically verifiable and that capture important symptomatic features of schizophrenia, a number of psychological tests will be used as assays of schizophrenia vs. normal functioning, including The California Verbal Learning Task (CVLT), modified free recall tasks with semantic cueing, the paired associate (PA) task, and the crossing sequences (CS) task. The computational work related to this grant we have done to date has indicated that a failure to appropriately utilize context is a core cognitive abnormality in schizophrenia; the category clustering indices that are part of the CVLT tap this cognitive function. The PA task is thought to index declarative memory function generally, and schizophrenic patients have consistently shown difficulties with it. It is thought that inability to recall and process sequential information, as revealed by deficienies in the CS task, may underlay the tangentiality and looseness characteristic of schizophrenic thought. After training the network on these tasks, we will subject it to the the many neurophysiologic or neuroanatomic abormalities thought to cause or contribute to schizophrenia (e.g., interneuron deficiencies and related connectivity disturbances, decreased NMDA activity as a result of abnormal levels of NAAG, excessive dopamine levels). We predict that schizophrenogenic changes will produce decreased performance of the model on the aforementioned psychological tasks. We then will examine model behaviors to test hypotheses, as described in the individual projects of the grant, concerning the mechanisms by which the cellular level abnormalities interact to produce clinical symptoms. Even if particular hypothesized mechanisms do not appear to be operative, this is instructive also--as biologically realistic in silico modeling is completely transparent, its behaviors can generate new hypotheses which can then be tested experimentally.

计算核心中的详细建模将解决有关本中特定项目的假设 grant，将细胞、系统和行为层面联系起来。我们的核心分析工具将是 在上一个资助期间开发的海马-内嗅皮层模型。我们将增加其 生物现实主义程度，以适应个人产生的新研究发现 项目。其目的是阐明中间或中期精神分裂症的海马病因学。 “内表型”水平。因为我们的模型表现出可临床验证的行为非常重要 为了捕捉精神分裂症的重要症状特征，将进行一些心理测试 用作精神分裂症与正常功能的分析，包括加州言语学习任务 (CVLT)、带有语义提示的修改自由回忆任务、配对关联 (PA) 任务和交叉任务 序列（CS）任务。我们迄今为止所做的与这笔赠款相关的计算工作表明： 未能正确利用情境是精神分裂症的核心认知异常；类别 作为 CVLT 一部分的聚类索引利用了这种认知功能。 PA任务被认为是索引 陈述性记忆功能普遍受到影响，而精神分裂症患者一直表现出困难。 人们认为无法回忆和处理连续信息，正如 CS 的缺陷所揭示的那样 任务，可能是精神分裂症思想的离题性和松散性特征的基础。训练后 网络在这些任务上，我们将使它受到许多神经生理学或神经解剖学异常的影响 被认为会导致或促成精神分裂症（例如，中间神经元缺陷和相关连接 紊乱、NAAG 水平异常导致 NMDA 活性降低、多巴胺水平过高）。 我们预测，精神分裂症的变化将导致模型在 上述心理任务。然后我们将检查模型行为来检验假设，如 在赠款的各个项目中描述了关于细胞水平的机制 异常相互作用产生临床症状。即使特定的假设机制不 似乎是有效的，这也很有启发性——因为计算机模型中的生物现实是完全真实的 透明的，它的行为可以产生新的假设，然后可以通过实验进行测试。