Evidence map›Paper›PMID 38860214›Full record

ArticleNeuropsychiatric disease and treatment2024

Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression.

Jingyi Lao, Yijie Zeng, Zhangying Wu, Gaohong Lin, Qiang Wang, Mingfeng Yang, Si Zhang, Danyan Xu, Min Zhang, Kexin Yao and 5 more

Abstract read
In one paragraph

Article in Neuropsychiatric disease and treatment, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. [A study on electroencephalogram characteristics of depression in patients with aphasia based on resting state and emotional Stroop task].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Jingyi Lao *Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Yijie Zeng *Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Zhangying WuGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Gaohong LinGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Qiang WangGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Mingfeng YangGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.ORCID 0000-0002-2151-9261
Si ZhangGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Danyan XuGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.ORCID 0009-0008-6717-2620
Min ZhangGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Kexin YaoGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Shuang LiangGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Qin LiuGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Jiafu LiGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Xiaomei ZhongGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.
Yuping NingGeriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear. Methods: Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed. Results: Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items. Conclusion: These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.

Indexed as

electroencephalogramlate-life depressionmicrostateresting-state networks

Identifiers

PMID38860214
PMCPMC11164213

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.