More than just statics: temporal dynamics of intrinsic brain activity predicts the suicidal ideation in depressed patients
More than just statics: temporal dynamics of intrinsic brain activity predicts the suicidal ideation in depressed patients
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不仅仅是静态：内在大脑活动的时间动态可以预测抑郁症患者的自杀意念
DOI：
10.1017/s0033291718001502
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发表时间：
2019-04-01
影响因子：
6.9
通讯作者：
Liao, Wei
中科院分区：
文献类型：
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作者：
Li, Jiao;Duan, Xujun;Liao, Wei
摘要背景重性抑郁障碍（MDD）与自杀的高风险相关。传统的神经影像学检查显示伴有自杀意念（SI）的MDD患者的静态脑活动和连接异常。然而，关于脑动力学的改变知之甚少。更广泛地说，目前还不清楚是否大脑活动的时间动态可以预测SI的预后。方法我们纳入了48例伴或不伴SI的MDD患者和30例年龄、性别和教育程度相匹配的健康对照者，这些患者均接受了静息态功能磁共振成像。我们首先使用滑动窗口分析评估低频波动（dALFF）的动态幅度-内在脑活动（iBA）的代理。此外，iBA的时间变异性（动力学）被量化为dALFF随时间的方差。此外，使用一般线性模型从时间变异性预测SI的严重程度。结果与不伴SI的MDD相比，SI组的背侧前扣带皮层、左侧眶额皮层、左侧颞下回和左侧海马的脑动力学变化较低。重要的是，这些时间变异性可以用来预测SI的严重程度（r = 0.43，p = 0.03），而静态ALFF不能在当前的数据集。结论MDD患者执行和情绪加工相关脑区的时间变异性改变与SI相关。这种使用iBA动力学的新型预测模型可能有助于开发用于临床应用的神经标志物。
Abstract Background Major depressive disorder (MDD) is associated with high risk of suicide. Conventional neuroimaging works showed abnormalities of static brain activity and connectivity in MDD with suicidal ideation (SI). However, little is known regarding alterations of brain dynamics. More broadly, it remains unclear whether temporal dynamics of the brain activity could predict the prognosis of SI. Methods We included MDD patients (n = 48) with and without SI and age-, gender-, and education-matched healthy controls (n = 30) who underwent resting-state functional magnetic resonance imaging. We first assessed dynamic amplitude of low-frequency fluctuation (dALFF) – a proxy for intrinsic brain activity (iBA) – using sliding-window analysis. Furthermore, the temporal variability (dynamics) of iBA was quantified as the variance of dALFF over time. In addition, the prediction of the severity of SI from temporal variability was conducted using a general linear model. Results Compared with MDD without SI, the SI group showed decreased brain dynamics (less temporal variability) in the dorsal anterior cingulate cortex, the left orbital frontal cortex, the left inferior temporal gyrus, and the left hippocampus. Importantly, these temporal variabilities could be used to predict the severity of SI (r = 0.43, p = 0.03), whereas static ALFF could not in the current data set. Conclusions These findings suggest that alterations of temporal variability in regions involved in executive and emotional processing are associated with SI in MDD patients. This novel predictive model using the dynamics of iBA could be useful in developing neuromarkers for clinical applications.