Evidence map›Paper›PMID 39185029›Full record

ArticleBrain communications2024

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder.

Wei Liu, Jian-Po Su, Ling-Li Zeng, Hui Shen, De-Wen Hu

Abstract read
In one paragraph

Article in Brain communications, 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

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Wei LiuCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
Jian-Po SuCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
Ling-Li ZengCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.ORCID https://orcid.org/0000-0002-0515-256X
Hui ShenCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.
De-Wen HuCollege of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan 410073, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Indexed as

brain imaginggene expressionmajor depression disordertemporal–spatial expression specificity

Identifiers

PMID39185029
PMCPMC11342243

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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.