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.

项目总结(计算建模核心，核心负责人：Frank，Brown University) 计算建模核心的首要目标是提供一个通用的形式化框架，该框架可以 量化项目1-4中跨物种接近-避免冲突的动态决策过程，包括 神经记录和操作的影响。我们利用分层贝叶斯参数估计 漂移扩散模型(HDDM)，它不仅捕捉了不同奖励、厌恶和 冲突，以及与这些选择相关的全部响应时间分布。HDDM有助于实现可靠 通过神经信号中的逐次尝试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作为临床状态、脑状态和 操作(例如，伤害素拮抗、急性/慢性应激、刺激)。我们已经证明了这一点 “计算生物标记物”可以提供更高的灵敏度，以区分患者的病情和 症状与行为和大脑活动的传统衡量标准相关。我们将利用神经记录和 来自不同物种的额叶皮质和基底节的刺激，以评估它们的变异性是否 与动机证据积累有关的参数，以及这些信号是否随着神经改变而改变 在操作和临床人群中。机器学习方法将量化这种情况的程度 定量模型拟合改善了(1)患者病情和脑状态的分类 方法应用于原始的行为和神经数据或它们的汇总统计数据，以及(2)我们映射的能力 病程，包括自杀和症状。基于我们在神经网络方面的丰富经验 以及涉及跨物种的动机学习和决策的计算级别，我们的计算 框架不仅将有助于提高区分临床情况的敏感性，而且还将 确定关于涉及的机制的假设，这些假设将通过使用 同样的量化框架。例如，我们的初步建模研究表明，子空间中的可变性 非人类灵长类动物先天扣带回活动中的种群影响动机证据的积累， 在人类中，相同的参数区分MDD和健康受试者，并根据症状进行衡量。 此外，这些计算生物标记物对于预测个人是否处于一种临床状态至关重要。 或者另一种分类，而仅基于行为和/或大脑活动的分类是随机水平的。因果关系 这些效应的神经和心理机制将被进一步描述和极大地扩展 同样的量化框架与因果操作和更精确的时间记录。 对中心整体目标的贡献以及与其他中心组件的互动。作为计算性的 建模核心，我们的框架适用于跨物种和方法的接近-避免决策， 并将应用于所有项目。我们将从专家之间的互动中受益，并与 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。