Neurocomputational Modeling Core

神经计算建模核心

• 批准号: 10594028
• 负责人: MICHAEL J. FRANK
• 金额: $ 34.09万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594028
• 关键词: Anatomy; Automobile Driving; Behavior; Behavioral; Biological Markers; Brain; Classification; Clinical; Computer Models; Data; Decision Making; Diagnosis; Diagnostic; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Engineering; Functional Magnetic Resonance Imaging; Goals; Graph; Individual; Intervention; Learning; Lesion; Link; Maps; Measures; Methods; Modeling; Motivation; Outcome; Parameter Estimation; Pathway interactions; Patients; Pattern; Probability; Process; Property; Psychiatry; Reaction Time; Rewards; Role; Sampling; Sensitivity and Specificity; Severities; Shapes; Site; Symptoms; System; Testing; Theoretical model; Validation; Variant; clinical predictors; cognitive neuroscience; computer framework; experience; improved; improved outcome; interest; machine learning classification; machine learning method; neural; neural circuit; neural correlate; neural network; neuromechanism; neuroregulation; neurotransmission; predictive modeling; prospective; reduce symptoms; response; symptomatology

PROJECT SUMMARY The overarching goal of the Neurocomputational Modeling Core is to provide a common formal framework that can incorporate measures of neural activity, connectivity, and behavior across Projects 1-5 to (a) quantify the functional roles of the OCDnet and its components in approach-avoidance decision-making and OCD symptomatology, and (b) predict changes in decision-making dynamics and symptom severity as a result of neural and clinical interventions. To achieve these goals, we will leverage (a) models of decision dynamics and their modulation by neural activity within individual circuit nodes, and (b) graph-theoretic models of interactions across circuit nodes. To quantify decision dynamics during the PAAT task, we will use hierarchical Bayesian parameter estimation of the drift diffusion model (HDDM), which enables reliable estimation of decision parameters and their modulation by trial-by-trial variance in neural signals, and supports Bayesian hypothesis testing for how these parameters may differ as a function of clinical status and neuromodulation. We have previously shown how such “computational biomarkers” can discriminate between patient conditions and symptoms better than traditional measures of behavior and brain activity, including in an approach-avoid context. We will test how PAAT choices are modulated by a combination of task variables (e.g., rewarding and aversive outcomes), neural activity across OCDnet nodes, and OCD symptom severity. Preliminary results show that the HDDM captures expected differences in choice dynamics (e.g., choice bias) between patients and healthy controls. To quantify task-related functional interactions across this circuit, we will use ancestral graph models, which measure the strength and direction of information flow across graph nodes. We will use this combination of modeling approaches to test for changes in decision and circuit dynamics resulting from targeted interventions (e.g., lesions, stimulation, treatment). Machine learning methods will quantify the degree to which such quantitative model fitting improves (1) classification of patient condition and (2) our ability to map changes in behavior, circuit dynamics, and disease course following interventions. Building on our extensive experience in neural networks and levels of computation involved in motivated learning and decision making across species, our computational framework will facilitate not only enhanced sensitivity to discriminate between clinical conditions, but will also identify hypotheses about the likely mechanisms involved, which will be tested via causal manipulations using the same quantitative framework. Contribution to Overall Center Goals & Interactions with Other Center Components. Our modeling framework will be applied to data across all Projects, including measures of connectivity (P1), behavioral and neural activity (P2-5), clinical measures (P3- 5), and influences of neural (P2&5) and behavioral (P4) interventions. Cores B & C will help with localization and estimation of neural activity. We will benefit from interactions amongst experts with complementary expertise in systems and cognitive neuroscience, psychiatry, engineering, and computational modeling.

项目摘要 神经计算建模核心的总体目标是提供一个共同的正式框架 可以结合1-5至（a）量化项目的神经活动，连通性和行为的度量 OCDNET的功能作用，是避免进近决策的组成部分 症状学，以及（b）预测由于 神经和临床干预措施。 它们通过iThin单个电路节点中的神经活动调节，以及（b）相互作用的图理论模型 在PAAT任务中量化决策动力学的跨电路节点 漂移扩散模型（HDDM）的参数估计，该估计可以可靠地估算决策 参数和通过逐试神经信号的试验差异的模块化，并支持贝叶斯假说 这些参数的测试可能是我们拥有的临床状况和神经调节的函数 以前显示了这种“计算生物标志物”如何在患者分区和 症状更好的传统行为和大脑行为措施，包括在避免进场 上下文。 厌恶结果），神经活动ACDNET节点和OCD症状严重程度 表明HDDM捕获的选择动力学的预期差异（例如，选择偏见） 和健康的对照。 图形模型，测量跨图节点的强度和方向 建模方法的这种结合，以测试由 有针对性的干预措施（例如，病变，刺激，治疗方法）。 此类定量模型拟合可以改善（1）患者状况的分类和（2）我们映射的能力 建立在我们广泛的基础上。 在主动学习和决策中涉及的神经网络和计算水平的经验 在整个物种中，我们的计算框架将有助于注释不仅增强了歧视的敏感性 在临床条件之间，但也将确定有关可能机制的假设，这将 使用相同的定量框架通过因果关系进行测试。 与其他中心组件的目标和互动。 项目，包括连接措施（P1），行为和神经行为（P2-5），临床措施（P3-- 5）以及神经（P2＆5）和行为（P4）的影响。 和估计神经活动。 系统和认知神经科学，精神病学，工程和计算建模方面的专业知识。