Section of Clinical and Computational Psychiatry (CompΨ), Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA.
Department of Psychology, University of Rochester, Rochester, NY 14627, USA.
Cereb Cortex. 2022 Jul 21;32(15):3318-3330. doi: 10.1093/cercor/bhab417.
Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25-40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.
尽管情绪在日常互动中无处不在，对心理健康也很重要，但人们对情绪及其神经基础知之甚少。计算模型可以帮助确定在情绪诱导任务中影响自我报告情绪的参数。在这里，我们测试了在金钱赌博期间自我报告的情绪的计算模型动力学是否可用于识别神经元活动的逐次试验变化。为此，我们通过在记录脑磁图 (MEG) 数据的同时传递个性化的奖励预测误差，来改变健康的（N=24）和抑郁的（N=30）青少年的情绪。根据预先注册的分析，我们假设情绪的期望成分将预测β-γ振荡功率（25-40 Hz）。我们还假设奖励反馈的源定位响应的试验变化将由情绪及其奖励预测误差成分来预测。通过我们的多层次统计分析，我们找到了确认性证据，表明β-γ功率与情绪转移期间的奖励期望呈正相关，在后扣带皮层中有局部来源。我们还证实了奖励预测误差可以预测旁中央小叶的反应中的试验水平变化。据我们所知，这是第一项利用情绪计算模型将情绪波动与 MEG 中的神经振荡变化联系起来的研究。
