Cell-Specific Gene-Expression Profiles and Cortical Thickness in the Human Brain
Bibliographic record
Abstract
Neurobiological underpinnings of cortical thickness in the human brain are largely unknown. Here we use cell-type-specific gene markers to evaluate the contribution of 9 neural cell-types in explaining inter-regional variations in cortical thickness and age-related cortical thinning in the adolescent brain. Gene-expression data were derived from the Allen Human Brain Atlas (and validated using the BrainSpan Atlas). Values of cortical thickness/thinning were obtained with magnetic resonance imaging in a sample of 987 adolescents. We show that inter-regional profiles in cortical thickness relate to those in the expression of genes marking CA1 pyramidal cells, astrocytes, and microglia; taken together, the 3 cell types explain 70% of regional variation in cortical thickness. We also show that inter-regional profiles in cortical thinning relate to those in the expression of genes marking CA1 and S1 pyramidal cells, astrocytes and microglia. Using Gene Ontology analysis, we demonstrate that the difference in the contribution of CA1 and S1 pyramidal cells may relate to biological processes such as neuronal plasticity and potassium channel activity, respectively. This "virtual histology" approach (scripts provided) can be used to examine neurobiological underpinnings of cortical profiles associated with development, aging, and various disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".