Association Between Urban Upbringing and Cortical Gyrification in Persons with Schizophrenia
Bibliographic record
Abstract
Background: Many studies suggest that urban upbringing might increase the risk of developing schizophrenia (SCZ). However, the precise brain changes associated with urban upbringing remain poorly understood. In this study, we investigated how urban upbringing might influence cortical gyrification, a brain feature that reflects early structural development. Methods: The study included 70 Healthy Controls (HC) and 87 individuals diagnosed with SCZ, all aged between 18 and 50 years. Participants and their caregivers were interviewed to collect information about birthplace and upbringing location. Based on data from the Indian Census (1971-2011), upbringing locations were categorized into three groups: rural, town, and city. An urbanicity index was calculated using a previously established method. Brain anatomical MRI images were processed using FreeSurfer. Regression analysis was conducted using the QDEC interface, with the gyrification index (GI) as the dependent variable, and urbanicity index, sex, and age as predictors. Results: = .001). Additionally, a significant interaction effect between the diagnosis and urbanicity index was found in multiple brain regions. Conclusions: These findings suggest that urban living has a significant influence on brain development. Identifying such risk factors and underlying mechanisms could help develop prevention strategies and guide improvements in urban planning.
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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) | 0.003 | 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".