Ray Kimberly L, Griffin Nicholas R, Shumake Jason, Alario Alexandra, Allen John J B, Beevers Christopher G, Schnyer David M
University of Texas, Austin, United States.
University of Texas, Austin, United States.
Brain Res. 2023 May 1;1806:148282. doi: 10.1016/j.brainres.2023.148282. Epub 2023 Feb 13.
Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.
缓解期抑郁症患者后续患抑郁症的风险更高，因此可能提供一个独特的机会来了解抑郁症风险背后的神经生理学关联。研究已经确定了与重度抑郁症相关的异常静息态脑电图（EEG）功率指标和功能连接模式，然而对于缓解期抑郁症患者的这些神经特征知之甚少。我们调查了37名抑郁症患者、56名缓解期抑郁症患者和49名健康成年人在静息状态下64通道EEG表面功率和源估计网络连接的频谱动态，这些参与者在年龄、教育程度和认知能力方面在θ、α和β频率上没有差异。平均参考频谱EEG表面功率分析发现，与健康成年人相比，缓解期抑郁症患者左额叶和额中部的θ波更强。使用基于网络的统计方法，我们还展示了在默认模式、额顶叶和突显网络中，LORETA转换后的EEG源空间相干性在网络内部和网络之间的变化，其中缓解期抑郁症患者与抑郁症患者和健康成年人相比表现出更强的相干性。这项工作建立在我们目前对抑郁症静息EEG连接性有限的理解基础上，并有助于弥合这种疾病中异常EEG功率与脑网络连接动态之间的差距。此外，我们对缓解期抑郁症相对于健康成年人和抑郁症成年人的独特研究可能是识别未来或后续抑郁症高风险人群基于大脑的生物标志物的关键。
