Age-dependent effects of schizophrenia genetic risk on cortical thickness and cortical surface area: Evaluating evidence for neurodevelopmental and neurodegenerative models of schizophrenia.
Bibliographic record
Abstract
Risk for schizophrenia peaks during early adulthood, a critical period for brain development. Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure. To address this question, we introduce a framework to estimate the effects of schizophrenia genetic variation on brain structure phenotypes across the life span. Five-hundred and six participants, including 30 schizophrenia probands, 200 of their relatives (aged 12-85 years) from 32 families with at least two first-degree schizophrenia relatives, and 276 unrelated controls, underwent MRI to assess regional cortical thickness (CT) and cortical surface area (CSA). Genetic variance decomposition analyses were conducted to distinguish among schizophrenia neurogenetic effects that are most salient before schizophrenia peak age-of-risk (i.e., early neurodevelopmental effects), after peak age-of-risk (late neurodevelopmental effects), and during the later plateau of age-of-risk (neurodegenerative effects). Genetic correlations between schizophrenia and cortical traits suggested early neurodevelopmental effects for frontal and insula CSA, late neurodevelopmental effects for overall CSA and frontal, parietal, and occipital CSA, and possible neurodegenerative effects for temporal CT and parietal CSA. Importantly, these developmental neurogenetic effects were specific to schizophrenia and not found with nonpsychotic depression. Our findings highlight the potentially dynamic nature of schizophrenia genetic effects across the lifespan and emphasize the utility of integrating neuroimaging methods with developmental behavior genetic approaches to elucidate the nature and timing of risk-conferring processes in psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".