Neurocomputational Approaches to Emotion Representation

保持情绪的适应性平衡是幸福的核心，而失调的情绪有助于 广泛地应用于给个人和社会带来沉重负担的临床疾病。认识转诊断 情绪对心理健康的重要性，国家卫生研究所的研究领域标准（RDoC） 矩阵包含负价、正价和唤醒的支配域。然而，矩阵 具体的情感状态，如悲伤，焦虑或渴望，是如何组织内部和跨 这些领域，部分原因是不知道离散情感的表征是否可靠， 差异化。其他RDoC结构，如反刍和担心，修改的时间参数 这些情绪会带来精神病理学风险并加剧症状的维持。尽管如此， 这些过程是如何与情绪大脑回路相互作用以影响情感动态的，特别是当它们经常 会在走神时自发出现拟议的研究有望改善RDoC描述 通过情感计算的方法来分析这些与情感相关的结构。在组合记录期间 心理生理学和功能性磁共振成像（fMRI），成年参与者将体验 情绪的小插曲和电影剪辑跨越唤醒和效价维度，并将报告他们的 在静息状态功能磁共振成像扫描中的自发情绪。机器学习算法将解码情感- 在不同层次的分析中，特定的信号将使用贝叶斯状态空间建模进行整合。一个 分类器错误的分析将测试来自情感理论的关于最佳 情感空间结构。使用图论工具，我们将描述神经网络的结构， 离散情感表示，以识别可用作新目标的省级和连接器中心 分别用于未来的特定于神经系统的或共病的神经调节干预。我们将应用 情绪特异性映射到来自相同参与者的静息状态数据，以创建 自发的情绪，并将其频率与特质和状态影响的测量相关联，作为验证步骤。 使用静态数据的随机建模，我们将推导出时间动态度量来测试 沉思和忧虑会在走神时促进情绪惰性假说。最后，我们将使用 现有的数据库，以证明我们的新指数的影响动力学transdiagnosis 区分精神健康障碍患者和健康对照者的静息状态fMRI活动模式。的 拟议的研究将改善目前的RDoC公式的负面影响，积极影响， 唤醒领域，通过告知离散情绪如何在这些领域内和跨这些领域组织， 整合多个RDoC分析单元的情绪表征，通过告知反刍和 担心影响自发情绪的神经生理学特征，并通过建立 情绪动态的计算衍生度量。

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.