Cortical GABAergic Neuron Dysregulation in Schizophrenia Is Age Dependent
Bibliographic record
Abstract
Background: Cortical GABAergic (gamma-aminobutyric acidergic) neuron dysregulation is implicated in schizophrenia (SCZ), but it remains unclear whether these changes are due to altered cell proportions or per-cell changes in messenger RNA (mRNA) expression. Methods: We analyzed 14 bulk and cell type-specific RNA sequencing (RNA-seq) datasets from 1408 individuals (672 SCZ cases, 736 controls) across 3 neocortical regions. We deconvolved GABAergic cell-subtype proportions from bulk RNA-seq and benchmarked them against single-nucleus RNA-seq and stereological densities from matched donors. We assessed SCZ- and age-associated changes in cell proportions and per-cell gene expression. Results: SCZ was associated with altered proportions of neocortical parvalbumin (PVALB) and somatostatin (SST) cells, depending on the subject's age at death. Younger SCZ cases (age < 70 years) showed reduced PVALB and SST cell proportions, while older cases showed unchanged or increased proportions compared with controls. Earlier-onset SCZ, associated with more severe clinical symptoms, was linked to greater reductions in these cell types. Additionally, there was robust evidence for reduced per-cell SST and vasoactive intestinal peptide mRNA among younger cases with SCZ. Conclusions: These findings suggest that SCZ is associated with complex, age-dependent alterations in GABAergic neurons, particularly affecting PVALB and SST cells. Our study underscores the importance of age-stratified analyses in SCZ, suggesting that distinct pathological processes underlie GABAergic neuron dysregulation across different age and symptom-severity groups and warranting tailored therapeutic approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".