PS73. Changes in neuronal densities underlying transcriptional alterations in psychiatric patients
Bibliographic record
Abstract
Abstract Introduction: High-throughput expression techniques are widely used to study psychiatric disorders. Genes found to be differentially expressed are next subjected to various network-enrichment analyses using resources such as Gene Ontology and protein-protein interaction databases. Such analyses are based on the assumption that the genes are interconnected by functional or physical interactions. However, the brain is composed of numerous cell-types, each expressing specific genes related to their function (e.g. oligodendrocytes express genes involved in synthesis of myelin). Thus, some of the differentially-expressed genes might be specific to different cell-types, rather than represent general transcriptional changes. Alternatively, the observed differential expression might result from alterations in cellular populations, for example as a result of cellular death. Indeed, evidence has accumulated regarding the involvement of neurodegeneration and neuroinflammation in psychiatric disorders (e.g., bipolar disorder and schizophrenia). Therefore, identifying the affected cell-types is crucial for proper analysis and interpretation of the gene expression data. Objectives: Given that the cell-type specific transcripts are known, changes in cellular populations can potentially be inferred from bulk-tissue expression data. However, reliable cellular markers don't exist for numerous cell types in the brain. To address this issue, we used publically available cell-type specific expression data from mouse brain to compile a marker-gene database for major brain cell-types. Interestingly, some of these genes (e.g. Cox6a2), are currently considered to be not expressed in the brain. Results and Conclusions: Using statistical methods, we inferred changes in several cellular populations in bipolar disorder and schizophrenic patients (170 bipolar and 160 schizophrenic samples in total). The inferred changes were similar across six different datasets analyzed, supporting the robustness of our method and are partially supported by previous studies using direct cell counting methods. The inferred changes were much more prominent in schizophrenic patients than in subjects with bipolar disorder.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.007 | 0.001 |
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".