Computational Modeling Core_Frank

计算建模核心_Frank

• 批准号: 10601139
• 负责人: MICHAEL J. FRANK
• 金额: $ 20.43万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10601139
• 关键词: Acute; Affect; Anhedonia; Animal Experiments; Anxiety; Anxiety Disorders; Basal Ganglia; Behavior; Behavioral; Benchmarking; Biological Markers; Brain; Chronic stress; Classification; Clinical; Complement; Computer Models; Conflict (Psychology); Corpus striatum structure; Data; Decision Making; Deep Brain Stimulation; Diagnosis; Diffusion; Dimensions; Disease; Electrocorticogram; Epilepsy; Goals; Graph; Human; Incentives; Individual; Investigation; Learning; Link; Machine Learning; Maps; Measures; Mental Depression; Methods; Modeling; Mood Disorders; Motivation; Parameter Estimation; Patients; Pharmaceutical Preparations; Phenotype; Physiological; Population; Process; Psychiatry; Reaction Time; Rewards; Rodent; Rodent Model; Sampling; Sensitivity and Specificity; Severities; Shapes; Signal Transduction; Site; Stress; Study models; Symptoms; System; Task Performances; Testing; Therapeutic; Translations; Universities; Validation; Variant; antagonist; approach avoidance behavior; clinical predictors; cognitive neuroscience; computer framework; depressive symptoms; experience; frontal lobe; functional magnetic resonance imaging/electroencephalography; improved; machine learning classification; machine learning method; microstimulation; neural; neural correlate; neural network; neuromechanism; neurotransmission; nociceptin; nonhuman primate; novel; psychologic; response; statistics; stressor; suicidal

PROJECT SUMMARY (Computational Modeling Core, Core Leader: Frank, Brown University) The overarching goal of the Computational Modeling Core is to provide a common formal framework that can quantify dynamic decision processes in approach-avoidance conflict across species in Projects 1-4, including the impact of neural recordings and manipulations. We leverage hierarchical Bayesian parameter estimation of the drift diffusion model (HDDM), which captures not only choice proportions for varying reward, aversion, and conflict, but also the full response time distributions associated with these choices. HDDM facilitates 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, brain state, and manipulations (e.g., nociceptin antagonism, acute/chronic stress, stimulation). We have shown how such “computational biomarkers” can provide enhanced sensitivity to discriminate between patient conditions and symptoms relative to traditional measures of behavior and brain activity. We will leverage neural recordings and stimulation from frontal cortex and basal ganglia across species to assess whether their variability is parametrically related to motivated evidence accumulation, and whether these signals are altered with neural manipulations and in clinical populations. Machine learning methods will quantify the degree to which such quantitative model fitting improves (1) classification of patient condition and brain state relative to the same methods applied to the raw behavioral and neural data or their summary statistics, and (2) our ability to map disease course, including suicidality and symptoms. 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 mechanisms involved, which will be tested via causal manipulations using the same quantitative framework. For example, our preliminary modeling studies indicate that variability in sub- populations within pregenual cingulate activity in non-human primates affects motivated evidence accumulation, and that in humans, the same parameter distinguishes MDD vs. healthy subjects and scales with symptoms. Moreover, these computational biomarkers are critical for predicting whether any individual is in one clinical state or another, whereas classification based on behavior and/or brain activity alone is at chance levels. The causal neural and psychological mechanisms of these effects will be further delineated and greatly expanded by utilizing the same quantitative framework with causal manipulations and more precise temporal recordings. Contribution to Overall Center Goals & Interactions with Other Center Components. As the Computational Modeling Core, our framework applies to approach-avoidance decision making across species and methods, and will be applied across all Projects. We will benefit from interactions amongst experts with complementary expertise in systems and cognitive neuroscience, psychiatry, computational modeling, and machine learning.

项目概要（计算建模核心，核心负责人：Frank，布朗大学） 计算建模核心的首要目标是提供一个通用的形式化框架， 量化项目1-4中跨物种的接近-回避冲突的动态决策过程，包括 神经记录和操纵的影响。我们利用分层贝叶斯参数估计， 漂移扩散模型（HDDM），它不仅捕捉了不同回报，厌恶和 冲突，而且还有与这些选择相关的完整响应时间分布。HDDM有助于可靠 通过神经信号中的逐个试验方差估计决策参数及其调制，并支持 贝叶斯假设检验这些参数如何作为临床状态、大脑状态和 操作（例如，痛敏肽拮抗、急性/慢性应激、刺激）。我们已经展示了这种 “计算生物标志物”可以提供增强的灵敏度以区分患者状况和 症状相对于传统的行为和大脑活动的措施。我们将利用神经记录， 从不同物种的额叶皮层和基底神经节刺激，以评估它们的变异性是否 参数相关的动机证据积累，以及这些信号是否与神经改变 操作和临床人群。机器学习方法将量化这种 定量模型拟合改善了（1）患者状况和相对于患者状况的大脑状态的分类 应用于原始行为和神经数据或其汇总统计的方法，以及（2）我们映射 病程，包括自杀倾向和症状。基于我们在神经网络领域的丰富经验 和跨物种的动机性学习和决策所涉及的计算水平，我们的计算 该框架不仅有助于提高区分临床状况的灵敏度，而且 确定有关所涉机制的假设，将通过因果操作进行测试，使用 相同的量化框架。例如，我们的初步建模研究表明，在亚- 非人灵长类中的前膝扣带活动内的群体影响有动机的证据积累， 在人类中，相同的参数区分MDD与健康受试者，并与症状相关。 此外，这些计算生物标志物对于预测任何个体是否处于一种临床状态至关重要 或另一种，而基于行为和/或大脑活动的分类仅处于偶然水平。的因果 这些影响的神经和心理机制将通过利用 相同的定量框架，因果操纵和更精确的时间记录。 对中心总体目标的贡献以及与其他中心组件的互动。作为计算 建模核心，我们的框架适用于跨物种和方法的接近-回避决策， 并将应用于所有项目。我们将受益于专家之间的互动和互补 系统和认知神经科学、精神病学、计算建模和机器学习方面的专业知识。