Evidence map›Paper›PMID 39741530›Full record

ArticleFrontiers in neuroscience2024

Exploring neural mechanisms of gender differences in bodily emotion recognition: a time-frequency analysis and network analysis study.

Tingwei Feng, Mingdi Mi, Danyang Li, Buyao Wang, Xufeng Liu

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Tingwei Feng *Department of Military Medical Psychology, Fourth Military Medical University, Xi'an, China.
Mingdi Mi *Weinan Vocational and Technical College Student Office, Weinan, China.
Danyang Li *College of Education Science, Changji University, Changji, China.
Buyao WangMental Health Education and Consultation Center, Tarim University, Alaer, China.
Xufeng LiuDepartment of Military Medical Psychology, Fourth Military Medical University, Xi'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to explore the neural mechanisms underlying gender differences in recognizing emotional expressions conveyed through body language. Utilizing electroencephalogram (EEG) recordings, we examined the impact of gender on neural responses through time-frequency analysis and network analysis to uncover gender disparities in bodily emotion recognition. Methods: The study included 34 participants, consisting of 18 males and 16 females. A 2 × 2 mixed design was employed, with gender (male and female) and bodily emotion (happy and sad) as the independent variables. Both behavioral and EEG data were collected simultaneously. Results: Males demonstrated more stable brain activity patterns when recognizing different bodily emotions, while females showed more intricate and highly interconnected brain activity networks, especially when identifying negative emotions like sadness. Differences based on gender were also observed in the significance of brain regions; males had greater importance in central brain areas, whereas females exhibited higher significance in the parietal lobe. Conclusion: Gender differences do influence the recognition of bodily emotions to some extent. The primary aim of this study was to explore the neural mechanisms underlying gender differences in bodily emotion recognition, with a particular focus on time-frequency analysis and network analysis based on electroencephalogram (EEG) recordings. By elucidating the role of gender in cognitive development, this study contributes to early detection and intervention.

Indexed as

network analysisphysical emotionssource analysistime domaintime-frequency

Identifiers

PMID39741530
PMCPMC11685152

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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.