Zhang Wei, Dutt Rosie, Lew Daphne, Barch Deanna M, Bijsterbosch Janine D
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA.
Psychol Med. 2025 Jan 6;54(16):1-12. doi: 10.1017/S0033291724003167.
Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission ('trait depression group'), those with large longitudinal severity changes in depression symptomatology ('state depression group'), and their respective matched control groups (total analytic = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.
尽管抑郁症是导致全球残疾的主要原因之一，但神经影像学研究一直难以确定可重复的抑郁症神经关联，或解释有限的方差。这一挑战可能部分源于抑郁症相互交织的状态（当前症状；可变）和特质（一般倾向；稳定）体验。在此，我们试图通过利用一个纵向队列，并将个体分为四组来区分抑郁症的状态和特质体验：缓解期个体（“特质抑郁症组”）、抑郁症症状有大幅纵向严重程度变化的个体（“状态抑郁症组”），以及它们各自匹配的对照组（总分析样本 = 1030）。我们假设空间网络组织因其时间稳定性与特质抑郁症有关，而网络之间的功能连接由于其波动能力对状态依赖性抑郁症症状更敏感。我们从静息态功能磁共振成像数据中识别出15个大规模概率功能网络，并以匹配的对照参与者为参照，对这些网络之间的振幅、连接性和空间重叠进行了组间比较。我们的研究结果显示，与对照组相比，特质抑郁症组在缓解期视觉网络的振幅更高。这一观察结果可能表明，随着时间的推移，易患抑郁症的个体的视觉处理发生了改变。在特质 - 对照比较的任何其他网络测量中，以及在状态 - 对照比较的任何测量中，均未观察到显著的组间差异。这些结果强调了视觉网络对抑郁症精神病理学的被忽视的贡献，并为抑郁症的状态和特质体验之间不同的神经关联提供了证据。
