Aberrant limbic brain structures in young individuals at risk for mental illness
Bibliographic record
Abstract
AIM: Alterations in limbic structures may be present before the onset of serious mental illness, but whether subfield-specific limbic brain changes parallel stages in clinical risk is unknown. To address this gap, we compared the hippocampus, amygdala, and thalamus subfield-specific volumes in adolescents at various stages of risk for mental illness. METHODS: MRI scans were obtained from 182 participants (aged 12-25 years) from the Canadian Psychiatric Risk and Outcome study. The sample comprised of four groups: asymptomatic youth at risk due to family history of mental illness (Stage 0, n = 32); youth with early symptoms of distress (Stage 1a, n = 41); youth with subthreshold psychotic symptoms (Stage 1b, n = 72); and healthy comparison participants with no family history of serious mental illness (n = 37). Analyses included between-group comparisons of brain measurements and correlational analyses that aimed to identify significant associations between neuroimaging and clinical measurements. A machine-learning technique examined the discriminative properties of the clinical staging model. RESULTS: Subfield-specific limbic volume deficits were detected at every stage of risk for mental illness. A machine-learning classifier identified volume deficits within the body of the hippocampus, left amygdala nuclei, and medial-lateral nuclei of the thalamus that were most informative in differentiating between risk stages. CONCLUSION: Aberrant subfield-specific changes within the limbic system may serve as biological evidence to support transdiagnostic clinical staging in mental illness. Differential patterns of volume deficits characterize those at risk for mental illness and may be indicative of a risk-stage progression.
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 | 0.000 |
| 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) | 0.001 | 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".