Neurocomputational Modeling Core

神经计算建模核心

• 批准号: 10411714
• 负责人: MICHAEL J. FRANK
• 金额: $ 35.33万
• 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/10411714
• 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; 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; base; cognitive neuroscience; computer framework; experience; improved; improved outcome; interest; machine learning classification; machine learning method; neural circuit; neural correlate; neural network; neuromechanism; neuroregulation; neurotransmission; predictive modeling; predictive test; prospective; reduce symptoms; relating to nervous system; 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首要目标是提供一个通用的形式化框架 它可以将项目1-5中的神经活动，连接和行为的测量结合起来，以（a）量化 OCDnet及其组件在接近-回避决策和OCD中的功能作用 （B）预测决策动态和症状严重程度的变化， 神经和临床干预。为了实现这些目标，我们将利用（a）决策动态模型， 它们通过单个电路节点内的神经活动的调制，以及（B）相互作用的图论模型 跨电路节点。为了量化PAAT任务中的决策动态，我们将使用分层贝叶斯 漂移扩散模型（HDDM）的参数估计，其能够可靠地估计决策 参数和他们的调制试验的方差在神经信号，并支持贝叶斯假设 测试这些参数如何作为临床状态和神经调节的函数而不同。我们有 先前示出了这样的“计算生物标志物”如何能够区分患者状况， 症状比传统的行为和大脑活动指标更好，包括在避免方法中 上下文我们将测试PAAT选择如何被任务变量的组合所调制（例如，奖励和 令人厌恶的结果），OCDnet节点上的神经活动和OCD症状严重程度。初步结果 显示HDDM捕获选择动态中的预期差异（例如，患者之间的选择偏倚 健康的对照。为了量化这个回路中与任务相关的功能相互作用，我们将使用祖先的 图模型，用于测量跨图节点的信息流的强度和方向。我们将使用 这种建模方法的组合用于测试决策和电路动态中的变化， 有针对性的干预措施（例如，损伤、刺激、治疗）。机器学习方法将量化 这种定量模型拟合改进了（1）患者状况的分类和（2）我们映射的能力 干预后的行为、电路动力学和疾病过程的变化。建立在我们广泛的 在神经网络和参与动机学习和决策的计算水平的经验 在物种之间，我们的计算框架不仅有助于提高区分的敏感性， 之间的临床条件，但也将确定有关可能的机制，这将 使用相同的定量框架，通过因果操作进行测试。对整个中心的贡献 目标和与其他中心组件的交互。我们的建模框架将应用于所有数据 项目，包括连通性测量（P1），行为和神经活动（P2-5），临床测量（P3- 神经干预（P2&5）和行为干预（P4）的影响。核心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、工程学和计算建模方面的专业知识。