Murphy Michael, Whitton Alexis E, Deccy Stephanie, Ironside Manon L, Rutherford Ashleigh, Beltzer Miranda, Sacchet Matthew, Pizzagalli Diego A
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
McLean Hospital, Belmont, MA, USA.
Neuropsychopharmacology. 2020 Nov;45(12):2030-2037. doi: 10.1038/s41386-020-0749-1. Epub 2020 Jun 26.
Neuroimaging studies have shown that major depressive disorder (MDD) is characterized by abnormal neural activity and connectivity. However, hemodynamic imaging techniques lack the temporal resolution needed to resolve the dynamics of brain mechanisms underlying MDD. Moreover, it is unclear whether putative abnormalities persist after remission. To address these gaps, we used microstate analysis to study resting-state brain activity in major depressive disorder (MDD). Electroencephalographic (EEG) "microstates" are canonical voltage topographies that reflect brief activations of components of resting-state brain networks. We used polarity-insensitive k-means clustering to segment resting-state high-density (128-channel) EEG data into microstates. Data from 79 healthy controls (HC), 63 individuals with MDD, and 30 individuals with remitted MDD (rMDD) were included. The groups produced similar sets of five microstates, including four widely-reported canonical microstates (A-D). The proportion of microstate D was decreased in MDD and rMDD compared to the HC group (Cohen's d = 0.63 and 0.72, respectively) and the duration and occurrence of microstate D was reduced in the MDD group compared to the HC group (Cohen's d = 0.43 and 0.58, respectively). Among the MDD group, proportion and duration of microstate D were negatively correlated with symptom severity (Spearman's rho = -0.34 and -0.46, respectively). Finally, microstate transition probabilities were nonrandom and the MDD group, relative to the HC and the rMDD groups, exhibited multiple distinct transition probabilities, primarily involving microstates A and C. Our findings highlight both state and trait abnormalities in resting-state brain activity in MDD.
神经影像学研究表明，重度抑郁症（MDD）的特征是神经活动和连接异常。然而，血流动力学成像技术缺乏解析MDD潜在脑机制动态变化所需的时间分辨率。此外，尚不清楚假定的异常在缓解后是否持续存在。为了填补这些空白，我们使用微状态分析来研究重度抑郁症（MDD）患者静息状态下的脑活动。脑电图（EEG）“微状态”是典型的电压地形图，反映静息状态脑网络各成分的短暂激活。我们使用极性不敏感的k均值聚类将静息状态高密度（128通道）EEG数据分割为微状态。纳入了79名健康对照（HC）、63名MDD患者和30名缓解期MDD（rMDD）患者的数据。这些组产生了相似的五组微状态，包括四个广泛报道的典型微状态（A - D）。与HC组相比，MDD组和rMDD组中微状态D的比例降低（Cohen's d分别为0.63和0.72），与HC组相比，MDD组中微状态D的持续时间和出现频率降低（Cohen's d分别为0.43和0.58）。在MDD组中，微状态D的比例和持续时间与症状严重程度呈负相关（Spearman's rho分别为 - 0.34和 - 0.46）。最后，微状态转换概率是非随机的，相对于HC组和rMDD组，MDD组表现出多种不同的转换概率，主要涉及微状态A和C。我们的研究结果突出了MDD患者静息状态脑活动中的状态和特质异常。
