Evidence map›Paper›PMID 40237609›Full record

ArticleCNS neuroscience & therapeutics2025

Prefrontal Internal Event-Driven Analysis of Dynamical Electroencephalographic Biomarkers in Depression During Emotional Auditory Task.

Qinglin Zhao, Kunbo Cui, Hua Jiang, Zhongqing Wu, Lixin Zhang, Mingqi Zhao, Bin Hu

Abstract read
In one paragraph

Article in CNS neuroscience & therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Qinglin ZhaoGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Kunbo CuiGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Hua JiangGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Zhongqing WuGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Lixin ZhangGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Mingqi ZhaoGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.
Bin HuGansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, People's Republic of China.ORCID 0000-0003-3514-5413

Funding

Fundamental Research Funds for the Central Universities lzujbky-2022-kb01Ministry of Science and Technology of the People's Republic of China with the STI2030-Major Projects 2021ZD0200408National Key Research and Development Program of China 2019YFA0706200National Key Research and Development Program of China 2023YFC2506804National Natural Science Foundation of China 62227807
6 · The paper itself

Abstract

aimsThis study for the first time proposed a novel prefrontal internal event-driven analytic framework for electroencepalography (EEG) data, which aim to dynamically resolve neural processes during natural emotional auditory tasks.

methodsThe framework employed a novel unsupervised time-series clustering model for internal prefrontal event extraction, which supports event-related analyses with the absence of external event labeling. The framework was validated using a 64-channel EEG data obtained from 110 (55 depressed) subjects in a three-polar (positive, neutral, and negative) emotional-auditory task.

resultsOur results suggest that anhedonia in depressed patients are associated with high activation levels in multiple brain regions during specific internal events, and we found that cross-frequency modulation of the bilateral prefrontal lobe with other relevant regions revealed completely different unidirectional patterns for the positive and negative tasks.

conclusionOur study confirmed the effectiveness of the framework in resolving fine-grained internal event-driven neural processes without relying on traditional precise event-related data acquisision paradigms that often require high attention on the task events and causes high cognitive load. Our study present new insights for identifying dynamical electroencephalographic biomarkers in depression, which potentially provide EEG signal decoding solutions for EEG feedback-based closed-loop intervention of depression.

Indexed as

Auditory PerceptionDepressionElectroencephalographyEmotionsMajor Depressive DisorderPrefrontal CortexAcoustic StimulationAdultAnhedoniaBiomarkersFemaleHumansMaleMiddle AgedYoung AdultBiomarkersdepressionEEGemotional auditory taskevent‐related analysisprefrontal cortex

Identifiers

PMID40237609
PMCPMC12001428

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.