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. 2025 Jun 25;42(3):488-495.
doi: 10.7507/1001-5515.202503034.

[A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task]

[Article in Chinese]
Siyuan Ding  1 , Yan Zhu  2 , Chang Shi  2 , Banghua Yang  1   3   4
Affiliations

[A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task]

[Article in Chinese]
Siyuan Ding et al. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. .

Abstract
in English, Chinese

Post-stroke aphasia is associated with a significantly elevated risk of depression, yet the underlying mechanisms remain unclear. This study recorded 64-channel electroencephalogram data and depression scale scores from 12 aphasic patients with depression, 8 aphasic patients without depression, and 12 healthy controls during resting state and an emotional Stroop task. Spectral and microstate analyses were conducted to examine brain activity patterns across conditions. Results showed that depression scores significantly negatively explained the occurrence of microstate class C and positively explained the transition probability from microstate class A to B. Furthermore, aphasic patients with depression exhibited increased alpha-band activation in the frontal region. These findings suggest distinct neural features in aphasic patients with depression and offer new insights into the mechanisms contributing to their heightened vulnerability to depression.

卒中后失语症患者的抑郁风险显著上升，但这一风险增加是源于外部心理社会因素还是大脑结构或功能变化目前尚不明确。本研究采集了12名失语抑郁患者、8名失语非抑郁患者以及12名健康志愿者在静息态与面孔情绪词任务下的64导联湿电极脑电信号，并进行了抑郁量表评估。研究结合频谱分析与微状态分析方法，比较三组人群在不同任务条件下的脑区神经活动模式。结果显示，受试者的抑郁量表评分能够负向解释C类微状态的发生率，正向解释A类到B类微状态的转换概率；此外，失语抑郁患者前额区域Alpha频段激活程度较为明显。上述差异可能反映了失语抑郁患者特有的神经活动特征，为理解其抑郁风险上升的潜在机制提供了新的研究路径。.

Keywords: Aphasia; Depression; Electroencephalogram; Microstate.

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Conflict of interest statement

利益冲突声明：本文全体作者均声明不存在利益冲突。

Figures

图 1
图 1
Data acquisition process 数据获取流程
图 2
图 2
Experimental procedure 试验流程
图 3
图 3
Microstate classification results of three groups 三类人群的微状态分类结果
图 4
图 4
Box plot of microstate indicators 微状态关键数据箱线图
图 5
图 5
Box plot of microstate transition probability 微状态转换概率箱线图
图 6
图 6
PSD of the three groups in different frequency bands for correct or incorrect identification 不同频带下三类人群识别正确与否的PSD脑地形图
图 7
图 7
Regression analysis between microstate parameters and depression assessment scale 微状态参数与抑郁评估量表的回归性分析
图 8
图 8
The classification effect of depression state by PSD combinations 各频带PSD组合抑郁状态分类效果
See this image and copyright information in PMC

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