Neurocomputational Approaches to Emotion Representation

保持情绪的适应性平衡是幸福的核心，而失调的情绪对此也有贡献 泛指给个人和社会带来沉重负担的临床疾病。认识跨诊断 情绪对心理健康的重要性，美国国家卫生研究院的研究领域标准(RDoC) 矩阵包含负价、正价和唤醒的主域。然而，矩阵 低估了特定的情感状态，如悲伤、焦虑或渴望是如何在内部和横向组织起来的 这些领域，部分是因为尚不清楚离散情绪的表征是否可靠 差异化。其他RDoC构造，如冥想和担忧，修改了 增加精神疾病风险并加剧症状维持的情绪。尽管如此，它仍然是未知的。 这些过程是如何与情绪大脑回路相互作用来影响动态的，特别是当它们经常 在走神的过程中自发地发生。拟议的研究承诺改善RDoC的描述 通过采用情感计算的方法对这些与情感相关的结构进行分析。在组合记录期间 心理生理学和功能磁共振成像(FMRI)，成人参与者将体验 情感到小插曲和跨越唤醒和价态维度的电影剪辑，并将报告他们的 静息状态fMRI扫描时的自发情绪。机器学习算法将对情感进行解码- 跨分析级别的特定信号，将使用贝叶斯状态空间建模进行集成。一个 对分类器错误的分析将检验来自情感理论的关于最优 情感空间结构。利用图论工具，我们将描述神经网络的体系结构 识别可用作新目标的省枢纽和联系枢纽的离散情感表征 分别用于未来针对症状的或共病的神经调节干预。我们将应用 特定于情绪的映射到来自相同参与者的休息状态数据，以创建 并将其频率与特质和状态情感的测量联系起来，作为验证步骤。 使用休息状态数据的随机建模，我们将推导出时间动力学度量来测试 假设在走神时，沉思和担忧会促进情绪惰性。最后，我们将使用 现有的数据存储库，以证明我们的新的影响动力学指标具有跨诊断功能 区分精神健康障碍患者和健康对照的静息状态fMRI活动模式。这个 拟议的研究将改进目前RDoC关于负面影响、积极影响和 通过告知离散情绪是如何在这些领域内和跨这些领域组织的，通过 跨多个RDoC分析单元集成情绪表示，通过告知沉思和 焦虑影响自发情绪的神经生理特征，并通过建立 通过计算得出的情绪动力学指标。

Maintaining an adaptive balance of emotions is central to well-being, and dysregulated emotions contribute broadly to clinical disorders that impart high personal and societal burdens. Recognizing the transdiagnostic importance of emotion to mental health, the National Institute of Health's Research Domain Criteria (RDoC) matrix contains overarching domains of Negative Valence, Positive Valence, and Arousal. However, the matrix underspecifies how specific affective states like sadness, anxiety, or craving are organized within and across these domains, in part because it is unknown whether representations of discrete emotions are reliably differentiated. Other RDoC constructs, such as rumination and worry, modify the temporal parameters of emotions that confer psychopathology risk and exacerbate symptom maintenance. Nonetheless, it is unknown how these processes interface with emotional brain circuits to impact affect dynamics, particularly as they often occur spontaneously during mind wandering. The proposed research promises to improve the RDoC depiction of these emotion-related constructs by taking an affective computing approach. During combined recording of psychophysiology and functional magnetic resonance imaging (fMRI), adult participants will experience emotions to vignettes and movie clips spanning the arousal and valence dimensions, and will report on their spontaneous emotions during resting-state fMRI scans. Machine learning algorithms will decode emotion- specific signals across the levels of analysis, which will be integrated using Bayesian state-space modeling. An analysis of classifier errors will test competing predictions from emotion theories regarding the optimal structure of affective space. Using graph theoretic tools, we will characterize the neural network architecture of the discrete emotion representations to identify provincial and connector hubs that can be used as novel targets for future symptom-specific or co-morbid neuromodulation interventions, respectively. We will apply the emotion-specific maps to resting-state data from the same participants to create neurophysiological indices of spontaneous emotions and to relate their frequencies to measures of trait and state affect as a validation step. Using stochastic modeling of the resting-state data, we will derive temporal dynamics metrics to test the hypothesis that rumination and worry promote emotional inertia during mind wandering. Finally, we will use existing data repositories to demonstrate that our novel indices of affect dynamics transdiagnostically differentiate resting-state fMRI activity patterns in mental health disorders from healthy controls. The proposed research will improve upon current RDoC formulations of Negative Affect, Positive Affect, and Arousal domains by informing how discrete emotions are organized within and across these domains, by integrating emotion representations across multiple RDoC units of analysis, by informing how rumination and worry impact neurophysiological signatures of spontaneous emotions, and by establishing the clinical utility of computationally-derived metrics of emotion dynamics.