Evidence map›Paper›PMID 42396602›Full record

SynthesisJournal of neurochemistry2026

The Converging Effects of Different Categories of Antidepressants on the Brain: A Systematic Meta-Analysis of Public Transcriptional Profiling Data From the Hippocampus and Cortex.

Eva M Geoghegan, Megan H Hagenauer, Erin Hernandez, Sophia Espinoza, Elizabeth I Flandreau, Phi T Nguyen, Adrienne N Santiago, Mubashshir Ra'eed Bhuiyan, Sophie Mensch, Stanley J Watson and 2 more

Abstract readMeta-AnalysisReview
In one paragraph

Synthesis in Journal of neurochemistry, 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Eva M GeogheganColumbia University, New York, New York, USA.
Megan H HagenauerUniversity of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0000-0002-3715-9475
Erin HernandezUniversity of Chicago, Chicago, Illinois, USA.
Sophia EspinozaMichigan State University, East Lansing, Michigan, USA.
Elizabeth I FlandreauGrand Valley State University, Allendale, Michigan, USA.
Phi T NguyenColumbia University, New York, New York, USA.
Adrienne N SantiagoColumbia University, New York, New York, USA.
Mubashshir Ra'eed BhuiyanUniversity of Michigan, Ann Arbor, Michigan, USA.ORCID https://orcid.org/0009-0004-8432-472X
Sophie MenschUniversity of Michigan, Ann Arbor, Michigan, USA.
Stanley J WatsonUniversity of Michigan, Ann Arbor, Michigan, USA.
Huda AkilUniversity of Michigan, Ann Arbor, Michigan, USA.
René HenColumbia University, New York, New York, USA.

Funding

Grinnell College Center for Careers, Life, and ServiceHope for Depression Research FoundationNIDA NIH HHS NIDA U01 DA043098The Pritzker Neuropsychiatric Disorders Research FoundationUniversity of Michigan Undergraduate Research Opportunities Program
6 · The paper itself

Abstract

Depression can be treated with traditional pharmaceuticals targeting monoaminergic function, nontraditional drug classes and neuromodulatory interventions. To identify mechanisms of action shared across clinically-effective antidepressant treatment categories, we performed two systematic meta-analyses of public transcriptional profiling data from adult laboratory rodents (rats, mice). The outcome variable was gene expression, measured by microarray or RNA-Seq from bulk-dissected tissue from two depression-related brain regions (hippocampus, cortex). Relevant datasets were identified in the Gemma database of curated, reprocessed transcriptional profiling data using predefined search terms and inclusion/exclusion criteria (hippocampus: June 24, 2024, cortex: July 10, 2024). Differential expression results were extracted for all genes, minimizing bias. For each gene, a random effects meta-analysis model was fit to antidepressant vs. control effect sizes (Log2 Fold Changes) from each study for each brain region, with follow-up analyses exploring sources of effect heterogeneity. For the hippocampus, 15 relevant studies were identified, containing 22 antidepressant vs. control group comparisons (collective n = 313 samples), with approximately half representing traditional versus nontraditional antidepressants. Of 16 439 analyzed genes, 58 were consistently differentially expressed (False Discovery Rate (FDR) < 0.05) following treatment. Antidepressant effects were enriched in the dentate gyrus and in gene sets related to stress regulation, brain growth and plasticity, vasculature and glia, and immune function. Comparisons with single nucleus RNA-Seq confirmed effects on specific hippocampal cell types, including potential rejuvenation of dentate granule neurons. For the cortex, 13 studies were identified, containing 16 antidepressant vs. control group comparisons (collective n = 233 samples). Of 15 583 analyzed genes, only one was consistently differentially expressed (FDR < 0.05: Atp6v1b2), but overall expression patterns moderately resembled the hippocampus. These genes and pathways showing consistent differential expression across treatment categories may be promising targets for novel therapies. Future work should explore relevance to human clinical populations and potential heterogeneity introduced by sex and subregion.

Indexed as

Antidepressive AgentsCerebral CortexGene Expression ProfilingHippocampusAnimalsMiceRatsAntidepressive Agentsantidepressanthippocampusmeta‐analysisRNA‐seq

Identifiers

PMID42396602
PMCPMC13329748

What OpenQuestion holds

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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 OpenQuestion graph.