Functional connectivity markers of depression in advanced Parkinson's disease
Functional connectivity markers of depression in advanced Parkinson's disease
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DOI：
10.1016/j.nicl.2019.102130
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发表时间：
2020-01-01
影响因子：
4.2
通讯作者：
Li, Weiping
中科院分区：
文献类型：
--
作者：
Lin, Hai;Cai, Xiaodong;Li, Weiping
背景：抑郁症是帕金森病的常见合并症，也是导致生活质量差的主要原因。尽管如此，由于症状重叠以及认知障碍老年患者抑郁评估的困难，PD 抑郁症的诊断率较低。 目的：本研究旨在利用静息态 fMRI 探索 PD 患者抑郁症的功能连接标志物，帮助诊断患者是否患有抑郁症。 方法：我们回顾了 156 名晚期 PD 患者（病程 > 5 年；59 名抑郁患者）和 45 名接受静息态 fMRI 扫描的健康对照受试者。功能连接分析用于使用组独立分量分析来表征内在连接网络并提取连接特征。将特征放入交叉验证循环内的所有相关特征选择程序中，以识别具有显着分类区分能力的特征。根据识别的特征，建立随机森林分类器用于抑郁症诊断。结果：识别出 42 个内在连接网络，并将其排列为皮质下网络、听觉网络、躯体运动网络、视觉网络、认知控制网络、默认模式网络和小脑网络。有六个特征与分类显着相关。它们是后扣带皮层内、岛叶内、后扣带皮层与岛叶/海马+杏仁核之间、岛叶与楔前叶之间以及顶上小叶与内侧前额叶皮层之间的连接。使用分类器区分抑郁症患者和非抑郁症患者的平均准确率为 82.4%。结论：我们的研究结果提供了初步证据，表明静息态功能连接可以表征抑郁症 PD 患者，并有助于将他们与非抑郁症患者区分开来。
Background: Depression is a common comorbid condition in Parkinson's disease and a major contributor to poor quality of life. Despite this, depression in PD is under-diagnosed due to overlapping symptoms and difficulties in the assessment of depression in cognitively impaired old patients.Objectives: This study is to explore functional connectivity markers of depression in PD patients using restingstate fMRI and help diagnose whether patients have depression or not.Methods: We reviewed 156 advanced PD patients (duration > 5 years; 59 depressed ones) and 45 healthy control subjects who underwent a resting-state fMRI scanning. Functional connectivity analysis was employed to characterize intrinsic connectivity networks using group independent component analysis and extract connectivity features. Features were put into an all-relevant feature selection procedure within cross-validation loops, to identify features with significant discriminative power for classification. Random forest classifiers were built for depression diagnosis, on the basis of identified features.Results: 42 intrinsic connectivity networks were identified and arranged into subcortical, auditory, somatomotor, visual, cognitive control, default-mode and cerebellar networks. Six features were significantly relevant to classification. They were connectivity within posterior cingulate cortex, within insula, between posterior cingulate cortex and insula/hippocampus + amygdala, between insula and precuneus, and between superior parietal lobule and medial prefrontal cortex. The mean accuracy achieved with classifiers to discriminate depressed patients from the non-depressed was 82.4%.Conclusions: Our findings provide preliminary evidence that resting-state functional connectivity can characterize depressed PD patients and help distinguish them from non-depressed ones.