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Dynamic brain representations underlying emotional experience

情绪体验背后的动态大脑表征

基本信息

批准号：
10380111
负责人：
Luke Joseph Chang
金额：
$ 65.95万
依托单位：
DARTMOUTH COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-01 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10380111
关键词：
Affect Amygdaloid structure Anger Arousal Base of the Brain Basic Science Behavior Behavioral Belief Brain Brain Diseases Brain region Categories Clinical Research Cognitive Colorado Communities Data Data Set Depressed mood Diagnosis Dimensions Disease Early Diagnosis Early Intervention Emotional Emotions Event Face Feeling Foundations Fright Functional Magnetic Resonance Imaging Future Generations Goals Guilt Health Heart Hour Human Individual Insula of Reil Interoception Intervention Judgment Lead Measurement Measures Mediating Memory Modality Modeling Moths Neurologic Neurosciences Participant Pathology Pattern Pattern Recognition Personal Satisfaction Pharmacology Play Population Prevention Psychopathology Radio Reading Reporting Research Research Personnel Risk Factors Role Sampling Sensitivity and Specificity Sensory Series Shapes Site System Techniques Technology Testing Time Training Validation Visual Work affective neuroscience base biobehavior computational neuroscience contextual factors emotion dysregulation emotional experience emotional stimulus experience experimental study functional disability indexing innovation insight midbrain central gray substance neurobiological mechanism neurofeedback novel predictive modeling psychologic reconstruction relating to nervous system response sensory cortex statistics theories tool translational study treatment response university student virtual

项目摘要

Emotions play a critical role in organizing human experience and behavior, and emotion dysregulation lies at the heart of psychopathology and functional impairment across disorders. To measure and understand emotion dysregulation, advances in understanding the fundamentals of how the brain generates and represents emotional states are vitally needed. This proposal develops and validates models of the brain representations that give rise to emotional states in naturalistic, narrative contexts. This will provide normative models of emotion to ground future translational studies, measurement models for specific emotional brain representations, and targets for interventions. We combine Functional Magnetic Resonance Imaging (fMRI), multi-dimensional measures of behavior, and pattern recognition techniques to develop models of brain activity that characterize and differentiate discrete categories of emotion experience (joy, anger, sadness, pride, and others) and blends of emotion. We place particular emphasis on the predictive validity (sensitivity and specificity) and generalizability of these models across sensory modalities, evaluative judgments, contextual narratives, and populations. We elicit emotional experiences in an ecologically valid paradigm using narratives (stories) experienced via listening, reading, or watching video. We measure multiple types of emotional experience in parallel with fMRI, using innovative collaborative filtering approaches to infer continuous moment-by-moment experience. The resulting brain models of specific emotion categories afford several potentially transformative advantages. Such models can (a) provide insight into which systems are necessary and sufficient for emotion generation (Aim 1); (b) be shared and tested across studies, allowing us to evaluate their generalizability across contexts (Aim 2); and (c) provide targets for psychological and neurological interventions (Aim 3). Six experiments focus on developing and validating emotional brain representations that are generalizable across individuals, research sites (Dartmouth and Colorado), and populations (college students and more diverse community samples). Expt. 1 develops models that predict the intensity of discrete emotional states. Expts. 2-4 establish the context sensitivity and generalizability of these. Expt. 2 examines the role of evaluative judgments in shaping emotional experience. Expt. 3 assesses the impact of background contextual narratives. Expt. 4 evaluates the role of sensory processing in emotion representations. Expts. 5-6 establish whether or not the brain models mediate emotional experiences. Expt. 5 uses cognitive appraisal and Expt. 6 uses real-time fMRI neurofeedback to manipulate emotion category-specific brain representations, testing for causal effects of these psychological and brain manipulations on emotional experience. Together, these studies will yield generalizable models of the dynamic brain patterns underlying specific emotional experiences. Such models could transform clinical research by allowing investigators to test emotion- focused interventions and assess emotion-related risk factors, permitting early detection and intervention.

情绪在组织人类经验和行为中起着至关重要的作用，而情绪失调则在于精神病理学的核心和跨疾病的功能障碍。测量和理解情绪调节失调，在理解大脑如何生成和表达的基础知识方面取得进展情绪状态是非常需要的。该提案开发并验证了大脑表征模型在自然主义的叙事背景下产生情绪状态。这将提供情感的规范模型为未来的转化研究、特定情绪大脑表征的测量模型奠定基础，以及干预措施的目标。我们结合功能磁共振成像 (fMRI)、多维行为测量和模式识别技术，以开发表征大脑活动的模型区分不同类别的情绪体验（快乐、愤怒、悲伤、骄傲等）并进行混合的情感。我们特别强调预测的有效性（敏感性和特异性）和普遍性这些模型跨越感官模式、评价判断、情境叙事和人群。我们使用经历过的叙述（故事）以生态有效的范式引发情感体验通过听、读或看视频。我们同时测量多种类型的情感体验 fMRI，使用创新的协作过滤方法来推断连续的时刻体验。由此产生的特定情绪类别的大脑模型提供了一些潜在的变革性优势。这些模型可以 (a) 深入了解哪些系统对于情绪生成是必要且充分的（目标 1）； (b) 在研究中共享和测试，使我们能够评估它们在不同背景下的普遍性（目标 2）； (c) 提供心理和神经干预的目标（目标 3）。六个实验侧重于开发和验证情感大脑表征可以推广到个人、研究地点（达特茅斯和科罗拉多州）和人群（大学生以及更多样化的社区样本）。出口。 1 开发模型来预测离散情绪的强度州。经验。 2-4 建立了这些内容的上下文敏感性和普遍性。出口。 2 考察作用塑造情感体验的评价性判断。出口。 3 评估背景语境的影响叙述。出口。图 4 评估了感觉处理在情绪表征中的作用。经验。 5-6建立大脑模型是否介导情绪体验。出口。 5 使用认知评估和Expt。 6 使用实时功能磁共振成像神经反馈来操纵情绪类别特定的大脑表征，测试这些心理和大脑操纵对情绪体验的因果影响。总之，这些研究将产生特定特定行为背后的动态大脑模式的通用模型。情感经历。这些模型可以通过允许研究人员测试情绪来改变临床研究集中干预并评估与情绪相关的危险因素，以便及早发现和干预。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

The psychological, computational, and neural foundations of indebtedness.

DOI：
10.1038/s41467-023-44286-9
发表时间：
2024-01-02
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Gao, Xiaoxue;Jolly, Eshin;Yu, Hongbo;Liu, Huiying;Zhou, Xiaolin;Chang, Luke J.
通讯作者：
Chang, Luke J.

Movie viewing elicits rich and reliable brain state dynamics.