Neurocomputational Modeling Core

项目总结 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 它可以包含对项目1-5的神经活动、连通性和行为的测量，以(A)量化 强迫网及其组成部分在避让决策和强迫症中的作用 症状学，以及(B)预测决策动态和症状严重性的变化 神经和临床干预。为了实现这些目标，我们将利用(A)决策动态模型和 它们由单个电路节点内的神经活动调节，以及(B)相互作用的图论模型 跨线路节点。为了量化PAAT任务期间的决策动态，我们将使用分层贝叶斯 漂移扩散模型(HDDM)的参数估计，从而能够可靠地估计决策 参数及其通过神经信号中的逐次试验方差进行的调制，并支持贝叶斯假设 测试这些参数作为临床状态和神经调节的函数有何不同。我们有 先前展示了这种“计算生物标记物”如何区分病人的病情和 症状比传统的行为和大脑活动指标更好，包括在一种方法-避免 背景。我们将测试PAAT选择如何受任务变量组合(例如，奖励和 厌恶结果)、强迫症网络节点间的神经活动以及强迫症症状的严重程度。初步结果 显示HDDM捕捉到患者之间在选择动态(例如，选择偏见)方面的预期差异 和健康的对照组。为了量化此电路中与任务相关的功能交互，我们将使用 图形模型，用于测量跨图形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，损害、刺激、治疗)。机器学习方法将量化程度 这样的定量模型拟合提高了(1)患者病情的分类和(2)我们映射的能力 干预后行为、回路动力学和病程的变化。在我们广泛的基础上 具有神经网络方面的经验，以及参与动机学习和决策的计算水平 跨物种，我们的计算框架不仅有助于提高对歧视的敏感度 临床情况之间的关系，但也将确定关于可能涉及的机制的假设，这将 通过使用相同的量化框架的因果操作进行测试。对整体中心的贡献 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 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和C将有助于本地化 以及对神经活动的估计。我们将从专家之间的互动中受益，并与 在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。

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.