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; 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 这可以结合1-5至（a）量化项目的神经元活动，连通性和行为的度量 OCDNET及其组件在避免进近决策和强迫症中的功能作用 症状学，以及（b）预测由于 神经和临床干预措施。为了实现这些目标，我们将利用（a）决策动态模型和 它们通过单个电路节点内神经活动的调节，以及（b）相互作用的图理论模型 跨电路节点。为了量化PAAT任务期间的决策动态，我们将使用层次结构贝叶斯 漂移扩散模型（HDDM）的参数估计，该估计可以可靠地估算决策 参数及其通过神经信号中的逐审差异调制，并支持贝叶斯假设 测试这些参数如何与临床状况和神经调节的函数不同。我们有 以前显示了这种“计算生物标志物”如何区分患者状况和 症状比传统的行为和大脑活动措施更好，包括在接近方面 语境。我们将测试PAAT选择如何通过任务变量的组合（例如奖励和 厌恶结局），OCDNET节点的神经活动和OCD症状严重程度。初步结果 表明HDDM捕获了患者之间选择动力学的预期差异（例如，选择偏见） 和健康对照。为了量化该电路上与任务相关的功能相互作用，我们将使用祖先 图模型，测量跨图节点的信息流的强度和方向。我们将使用 建模方法的这种结合，用于测试由 有针对性的干预措施（例如病变，刺激，治疗）。机器学习方法将量化学位 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 干预后行为，电路动力学和疾病过程的变化。建立我们的广泛 在动机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，我们的计算框架不仅会增强歧视的灵敏度 在临床状况之间，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操作进行测试。对整体中心的贡献 目标与其他中心组件的互动。我们的建模框架将应用于所有的数据 项目，包括连通性测量（P1），行为和神经活动（P2-5），临床测量（P3-- 5），以及神经（P2＆5）和行为（P4）干预的影响。内核B＆C将有助于本地化 和神经活动的估计。我们将受益于专家之间的互动 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。