Elucidating Cell Types Associated with Case-control Differences in Cortical Thickness in Neuropsychiatric Disorders
Bibliographic record
Abstract
Case-control differences in cortical thickness are hallmarks of numerous neuropsychiatric disorders; however, the cortical cell types that underlie these differences remain largely unknown. Virtual histology methods aim to identify such cell types through correlating interregional profiles of thickness differences to healthy gene expression. We propose a new method, case-control virtual histology (CCVH), that leverages multi-region, case-control gene expression to find cell types that are differentially abundant in regions with largest differences in cortical thickness. In AD, CCVH found that in such regions, excitatory and inhibitory neurons were relatively less abundant, and astrocytes, oligodendrocytes, oligodendrocyte precursor cells and endothelial cells relatively more abundant in cases relative to controls. Traditional virtual histology indicated that healthy control excitatory neuron abundance was associated with thinner cortex in AD, but inhibitory neuron abundance was not. Our findings imply that CCVH might better identify cell types that are directly responsible for case-control differences in cortical thickness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".