改变动态网络稳定性与抑郁症复发相关的晚期抑郁缓解。

IF 4.8
Damek Homiack, Brian Boyd, Aifeng Zhang, J Patrick Begnoche, Meryl Butters, Carmen Andreescu, Warren D Taylor, Olusola Ajilore
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引用次数: 0

摘要

背景:老年抑郁症(LLD)与包括高复发率和认知能力下降在内的负面结果相关。然而,影响LLD这种结果的神经生物学变化尚不清楚。大尺度脑网络的不平衡可能导致与低密度相关的认知能力下降。方法:从未抑郁的老年人和LLD早期缓解的参与者被招募为伦勃朗研究的一部分。在研究开始时,参与者完成了静息状态fMRI扫描和神经心理测试,并随后监测了两年多的抑郁症复发情况。使用先前描述的算法,通过k-means共识聚类识别反复出现的全脑空间共激活状态。然后将从未抑郁的参与者(n = 40)的共同发生的网络状态属性与持续缓解的LLD参与者(n = 50)或经历抑郁复发的LLD参与者(n = 33)进行比较。结果:与默认模式网络、认知控制网络和前显性网络在解剖学上重叠的三网络解决方案最好地解释了反复出现的网络状态。与从未抑郁的老年人相比,从LLD中解脱出来的参与者表现出较低的网络弹性和网络之间转换的改变。在从未抑郁和持续缓解的参与者中,特定网络的稳定性与基线临床和神经心理学标志物相关,但在经历抑郁复发的参与者中则变得迟钝。结论:总的来说,这些数据表明LLD改变了持续到缓解期的动态网络稳定性。此外,特定网络状态的稳定性与临床和神经心理学标志物有关,这些标志物可以预测LLD复发的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Altered Dynamic Network Stability in Remitted Late Life Depression Associated with Depression Recurrence.

Background: Late-life depression (LLD) is associated with negative outcomes including high rates of recurrence and cognitive decline. However, the neurobiological changes influencing such outcomes in LLD are not well understood. Disequilibrium in large-scale brain networks may contribute to LLD-related cognitive decline.

Methods: Never-depressed older adults and participants in early remission from LLD were recruited as part of the REMBRANDT study. At study entry, participants completed a resting-state fMRI scan and neuropsychological testing and were subsequently monitored over two years for depression recurrence. Using a previously described algorithm, recurring whole-brain states of spatial co-activation were identified by k-means consensus clustering. Co-occurring network state properties from never-depressed participants (n = 40) were then compared to LLD participants who remained in remission (n = 50) or experienced depression recurrence (n = 33).

Results: A three-network solution overlapping anatomically with the Default Mode Network, Cognitive Control Network, and Anterior Salience Network best explained recurring network states. Compared with never-depressed older adults, participants who remitted from LLD exhibited decreased network resilience and altered transitions between networks. Stability of specific networks were associated with baseline clinical and neuropsychological markers in never-depressed and sustained remission participants but were blunted for participants who experienced depression recurrence.

Conclusions: Collectively, these data suggest that LLD alters dynamic network stability lasting into remission. Furthermore, stability of specific networks states is associated with clinical and neuropsychological markers which may predict the likelihood of a recurrent episode of LLD.

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