Evidence map›Paper›PMID 42041764›Full record

ArticleBrain sciences2026

Clustering Analysis of Emotional Expression, Personality Traits, and Psychological Symptoms.

Lingping Meng, Mingzheng Li, Xiao Sun

Abstract read
In one paragraph

Article in Brain sciences, 2026. 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. Review
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

3 authors.

Lingping MengSchool of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China.
Mingzheng LiHefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei 230088, China.
Xiao SunSchool of Mental Health and Psychological Sciences, Anhui Medical University, Hefei 230032, China.ORCID 0000-0001-9750-7032

Funding

HEFEI COMPREHENSIVE NATIONAL SCIENCE CENTER 12340000MB1B139622
6 · The paper itself

Abstract

backgroundThis study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10-77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults.

methodsPsychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal-Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model.

resultsThe young adult group (19-35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09,

conclusionsThese findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed.

Indexed as

AffectNetconvolutional neural networkdeep learningemotion expressionfacial expression recognitionk-means cluster analysismental health riskspersonality traits

Identifiers

PMID42041764
PMCPMC13113890

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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.