Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis
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使用全脑功能连接识别重度抑郁症：多变量模式分析
DOI：
10.1093/brain/aws059
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发表时间：
2012-05-01
期刊：
影响因子：
14.5
通讯作者：
Hu, Dewen
中科院分区：
文献类型：
--
作者：
Zeng, Ling-Li;Shen, Hui;Hu, Dewen
最近的静息状态功能连接磁共振成像研究显示，重度抑郁症患者和健康对照者在几个区域和网络上存在显著的组间差异。本研究的目的是探讨抑郁症患者的全脑静息状态功能连接模式，这可以用于测试从健康对照中识别重度抑郁症个体的可行性。采用多变量模式分析对29名人口统计学匹配的健康志愿者中的24名抑郁症患者进行分类。排列测试用于评估分类器的性能。实验结果表明，通过留一交叉验证，94.3% （P < 0.0001）的受试者被正确分类，包括100%识别所有患者。大多数最具区别性的功能连接位于默认模式网络、情感网络、视觉皮质区和小脑内部或之间，从而表明疾病相关的静息状态网络改变可能引起部分重性抑郁症的情绪和认知障碍复合体。此外，杏仁核、前扣带皮层、海马旁回和海马具有较高的分类能力，可能在该疾病的病理生理中发挥重要作用。本研究可能为重性抑郁症的病理机制提供新的思路，并提示全脑静息状态功能连通性磁共振成像可能为其临床诊断提供潜在有效的生物标志物。
Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis.