Neurocomputational Approaches to Emotion Representation

保持情绪的适应性平衡是幸福的核心，情绪失调有助于 广泛地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，美国国立卫生研究院研究领域标准 (RDoC) 矩阵包含负价、正价和唤醒的总体域。然而，矩阵 未明确说明悲伤、焦虑或渴望等特定情感状态如何在内部和外部组织起来 这些领域，部分是因为不知道离散情感的表示是否可靠 差异化。其他 RDoC 构造，例如反思和担忧，修改了时间参数 带来精神病理学风险并加剧症状维持的情绪。尽管如此，仍不得而知 这些过程如何与情绪脑回路相互作用以影响动态，特别是当它们经常发生时 走神时会自然发生。拟议的研究有望改善 RDoC 描述 通过采用情感计算方法来分析这些与情感相关的结构。合并录制期间 心理生理学和功能磁共振成像（fMRI），成人参与者将体验 情感到跨越唤醒和效价维度的小插曲和电影剪辑，并将报告它们的情况 静息态功能磁共振成像扫描期间的自发情绪。机器学习算法将解码情感—— 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。安 分类器错误的分析将测试情感理论关于最佳分类器的竞争预测 情感空间的结构。使用图论工具，我们将描述神经网络架构 离散情感表征，用于识别可用作新目标的省级和连接器中心 分别用于未来的症状特异性或共病神经调节干预。我们将应用 将特定情绪映射到同一参与者的静息状态数据，以创建神经生理学指数 自发的情绪，并将其频率与特征和状态影响的测量联系起来，作为验证步骤。 使用静息态数据的随机建模，我们将推导出时间动态指标来测试 假设沉思和担忧会在走神时促进情绪惰性。最后，我们将使用 现有的数据存储库来证明我们的新型影响动态指数可以跨诊断 区分精神健康障碍和健康对照组的静息态功能磁共振成像活动模式。的 拟议的研究将改进当前 RDoC 的消极情感、积极情感和 通过告知离散情绪如何在这些领域内和跨这些领域进行组织来唤醒领域 通过告知沉思和思考如何整合跨多个 RDoC 分析单元的情感表征 忧虑影响自发情绪的神经生理学特征，并通过建立忧虑的临床效用 计算得出的情绪动态指标。

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.