Department of Old age Psychiatry, GGZ inGeest Specialized Mental Health Care and Amsterdam University Medical center, location VU University medical center (VUmc), Amsterdam, the Netherlands.
Amsterdam Neuroscience, Vu/Vumc/UVA/AMC, Amsterdam, the Netherlands.
PLoS One. 2019 Jan 17;14(1):e0209908. doi: 10.1371/journal.pone.0209908. eCollection 2019.
Severe depression is associated with high morbidity and mortality. Neural network dysfunction may contribute to disease mechanisms underlying different clinical subtypes. Here, we apply resting-state functional magnetic resonance imaging based measures of brain connectivity to investigate network dysfunction in severely depressed in-patients with and without psychotic symptoms.
A cohort study was performed at two sites. Older patients with major depressive disorder with or without psychotic symptoms were included (n = 23 at site one, n = 26 at site two). Resting state 3-Tesla functional MRI scans, with eyes closed, were obtained and Montgomery-Åsberg Depression Rating Scales were completed. We denoised data and calculated resting state networks in the two groups separately. We selected five networks of interest (1. bilateral frontoparietal, 2.left lateralized frontoparietal, 3.right lateralized frontoparietal, 4.default mode network (DMN) and 5.bilateral basal ganglia and insula network) and performed regression analyses with severity of depression, as well as presence or absence of psychotic symptoms.
The functional connectivity (FC) patterns did not correlate with severity of depression. Depressed patients with psychotic symptoms (n = 14, 61%) compared with patients without psychotic symptoms (n = 9, 39%) from site one showed significantly decreased FC in the right part of the bilateral frontoparietal network (p = 0.002). This result was not replicated when comparing patients with (n = 9, 35%) and without (n = 17, 65%) psychotic symptoms from site two.
Psychotic depression may be associated with decreased FC of the frontoparietal network, which is involved in cognitive control processes, such as attention and emotion regulation. These findings suggest that FC in the frontoparietal network may be related to the subtype of depression, i.e. presence of psychotic symptoms, rather than severity of depression. Since the findings could not be replicated in the 2nd sample, replication is needed before drawing definite conclusions.
重度抑郁症与高发病率和死亡率相关。神经网络功能障碍可能导致不同临床亚型的疾病机制。在这里,我们应用基于静息态功能磁共振成像的脑连接测量方法来研究伴有和不伴有精神病症状的重度住院抑郁症患者的网络功能障碍。
在两个地点进行了队列研究。纳入了伴有或不伴有精神病症状的老年重性抑郁障碍患者(一个地点 23 例,另一个地点 26 例)。获得静息状态 3T 功能磁共振成像扫描,闭眼,并完成蒙哥马利-阿斯伯格抑郁评定量表。我们对数据进行了去噪,并分别在两组中计算静息状态网络。我们选择了五个感兴趣的网络(1.双侧额顶叶网络,2.左侧额顶叶网络,3.右侧额顶叶网络,4.默认模式网络(DMN)和 5.双侧基底节和岛叶网络),并进行了回归分析,分析抑郁严重程度以及是否存在精神病症状的影响。
功能连接(FC)模式与抑郁严重程度无关。与无精神病症状的患者(n = 9,39%)相比,来自一个地点的伴有精神病症状的抑郁患者(n = 14,61%)的双侧额顶叶网络右侧部分的 FC 显著降低(p = 0.002)。当比较来自另一个地点的伴有(n = 9,35%)和不伴有(n = 17,65%)精神病症状的患者时,未复制出这一结果。
精神病性抑郁症可能与额顶叶网络的 FC 降低有关,该网络参与认知控制过程,如注意力和情绪调节。这些发现表明,额顶叶网络的 FC 可能与抑郁症的亚型有关,即是否存在精神病症状,而不是抑郁的严重程度。由于在第二个样本中无法复制这些发现,因此在得出明确结论之前,需要进行复制。