Computational and electrochemical substrates of social decision-making in humans

人类社会决策的计算和电化学基础

• 批准号: 10640947
• 负责人: Xiaosi Gu
• 金额: $ 99.64万
• 依托单位: VIRGINIA POLYTECHNIC INST AND ST UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640947
• 关键词: Action Potentials; Affect; American Psychiatric Association; Antisocial Personality Disorder; Attention deficit hyperactivity disorder; Behavior; Behavioral; Brain; Catecholamines; Cognition; Collaborations; Computer Models; Conscious; Consensus; Corpus striatum structure; Decision Making; Deep Brain Stimulation; Detection; Diagnostic; Disease; Dopamine; Drug Addiction; Electrodes; Emotional; Emotions; Essential Tremor; Failure; Functional disorder; Goals; Health; Human; Injury; Interpersonal Relations; Investments; Joints; Learning; Machine Learning; Major Depressive Disorder; Measurement; Measures; Mental Depression; Mental disorders; Methodology; Methods; Modeling; Moods; Neuromodulator; Neurotransmitters; Norepinephrine; Obsessive-Compulsive Disorder; Operative Surgical Procedures; Parkinson Disease; Pathologic; Patients; Perception; Persons; Play; Positive Valence; Research Domain Criteria; Rewards; Risk; Role; Safety; Serotonergic System; Serotonin; Signal Transduction; Site; Social Interaction; Social Perception; Social Processes; System; Time; Ventral Striatum; Withdrawal; Work; arm; autism spectrum disorder; cognitive function; diagnostic criteria; dopamine system; human model; implantation; information processing; mental state; neural; neurochemistry; neuroregulation; novel; psychologic; putamen; social; social communication; social deficits; social norm; social relationships

SUMMARY Dopamine and serotonin systems in the human brain represent two key neuromodulatory signalling systems that impact mood, value-based decision-making, learning, and a host of other cognitive functions. Despite their importance, there is no consensus view from a psychological or computational perspective concerning their exact information-processing functions. Consequently, there is a scarceness of strategies to treat disorders that afflict these systems. We believe that this gap exists primarily because of methodological limitations. While there are many fast methods for recording action potential activity and local field potentials, similar progress for tracking `the other end of the problem' - neurochemical dynamics - has lagged far behind. This is unfortunate since just considering the catecholamines dopamine, serotonin, and norepinephrine, the worldwide health burden of dysfunction in these neuromodulatory systems is immense approaching 400 million people worldwide if one includes just Major Depression and Attention-deficit hyperactivity disorders (WHO, 2017). The overall goal of this project is to investigate the computational and neuromodulatory substrates of interactive social processes hypothesized to be trans-diagnostic RDoC constructs. We will utilize two levels of analysis (molecules/circuits and behavioral/computational) across two RDoC constructs (systems for social processes: perception and understanding of others, subconstruct understanding mental states, and positive valence systems: reward learning, subconstruct reward prediction error) to make inroads into understanding the computational and neural underpinnings of social interaction in humans. Crucially, the interactive social tasks used in this proposal generate an important class of learning signal – a reward prediction error signal – but expressed in the context of an inter-personal interaction. Our two levels of analysis employ (1) computational models of human (two-agent) social exchange estimated from detailed, observed behavior and (2) unique sub- second measurements of dopamine and serotonin from human striatum (three separate sites: caudate, putamen, ventral striatum) during the window-of-opportunity afforded by deep brain stimulating (DBS) electrode implantation surgery for Parkinson's Disease, Essential Tremors, Obsessive Compulsive Disorder.

总结 人脑中的多巴胺和5-羟色胺系统代表了两个关键的神经调节信号系统， 影响情绪、基于价值的决策、学习和许多其他认知功能。尽管他们 重要的是，从心理学或计算的角度来看， 精确的信息处理功能。因此，缺乏治疗疾病的策略， 影响这些系统。我们认为，这一差距的存在主要是由于方法上的限制。虽然 有许多快速记录动作电位活动和局部场电位的方法， “问题的另一端”-神经化学动力学-远远落在后面。这很不幸，因为 考虑到儿茶酚胺多巴胺，血清素和去甲肾上腺素， 这些神经调节系统的功能障碍是巨大的，如果一个人在世界范围内接近4亿人， 包括重度抑郁症和注意力缺陷多动障碍（WHO，2017）。 本项目的总体目标是研究交互式神经网络的计算和神经调节底物， 社会过程假设为跨诊断RDoC结构。我们将使用两个层次的分析 （分子/电路和行为/计算）在两个RDoC结构（社会系统） 过程：感知和理解他人，子结构理解心理状态，积极 价系统：奖励学习，子结构奖励预测错误），使理解的进展 人类社会互动的计算和神经基础。关键是， 在该提议中使用的任务生成一类重要的学习信号-奖励预测误差信号- 而是在人际互动的背景下表达的。我们的两个层次的分析采用（1）计算 人类（两个代理）社会交换模型估计从详细的，观察到的行为和（2）独特的子， 对来自人纹状体（三个独立的部位：尾状核，壳核， 腹侧纹状体）在脑深部刺激（DBS）电极提供的机会窗口期间 帕金森病、特发性震颤、强迫症的植入手术。

项目成果

Emotional adaptation during a crisis: decline in anxiety and depression after the initial weeks of COVID-19 in the United States.

• DOI: 10.1038/s41398-021-01552-y
• 发表时间: 2021-08-20
• 期刊: Translational psychiatry
• 影响因子: 6.8
• 作者: Shuster A;O'Brien M;Luo Y;Berner LA;Perl O;Heflin M;Kulkarni K;Chung D;Na S;Fiore VG;Gu X
• 通讯作者: Gu X

Time perception reflects individual differences in motor and non-motor symptoms of Parkinson's disease.

时间感知反映了帕金森病运动和非运动症状的个体差异。

• DOI: 10.1016/j.parkreldis.2023.105800
• 发表时间: 2023
• 期刊: Parkinsonism & related disorders
• 影响因子: 4.1
• 作者: DiMarco,EmilyK;Sadibolova,Renata;Jiang,Angela;Liebenow,Brittany;Jones,RachelE;Haq,IhtshamU;Siddiqui,MustafaS;Terhune,DevinB;Kishida,KennethT
• 通讯作者: Kishida,KennethT

Humans use forward thinking to exploit social controllability.

• DOI: 10.7554/elife.64983
• 发表时间: 2021-10-29
• 期刊: eLife
• 影响因子: 7.7
• 作者: Na S;Chung D;Hula A;Perl O;Jung J;Heflin M;Blackmore S;Fiore VG;Dayan P;Gu X
• 通讯作者: Gu X

Invasive Computational Psychiatry.

• DOI: 10.1016/j.biopsych.2022.09.032
• 发表时间: 2023-04-15
• 期刊: Biological psychiatry
• 影响因子: 10.6

Sub-second Dopamine and Serotonin Signaling in Human Striatum during Perceptual Decision-Making.

• DOI: 10.1016/j.neuron.2020.09.015
• 发表时间: 2020-12-09
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Bang D;Kishida KT;Lohrenz T;White JP;Laxton AW;Tatter SB;Fleming SM;Montague PR
• 通讯作者: Montague PR