Linking persistent negative symptoms to amygdala–hippocampus structure in first-episode psychosis
Bibliographic record
Abstract
Early persistent negative symptoms (PNS) following a first episode of psychosis (FEP) are linked to poor functional outcome. Reports of reduced amygdalar and hippocampal volumes in early psychosis have not accounted for heterogeneity of symptoms. Age is also seldom considered in this population, a factor that has the potential to uncover symptom-specific maturational biomarkers pertaining to volume and shape changes within the hippocampus and amygdala. T1-weighted volumes were acquired for early (N=21), secondary (N=30), non-(N=44) PNS patients with a FEP, and controls (N=44). Amygdalar-hippocampal volumes and surface area (SA) metrics were extracted with the Multiple Automatically Generated Templates (MAGeT)-Brain algorithm. Linear mixed models were applied to test for a main effect of group and age × group interactions. Early PNS patients had significantly reduced left amygdalar and right hippocampal volumes, as well as similarly lateralized negative age × group interactions compared to secondary PNS patients (P<0.017, corrected). Morphometry revealed decreased SA in early PNS compared with other patient groups in left central amygdala, and in a posterior region when compared with controls. Early and secondary PNS patients had significantly decreased SA as a function of age compared with patients without such symptoms within the right hippocampal tail (P<0.05, corrected). Significant amygdalar-hippocampal changes with age are linked to PNS after a FEP, with converging results from volumetric and morphometric analyses. Differential age trajectories suggest an aberrant maturational process within FEP patients presenting with PNS, which could represent dynamic endophenotypes setting these patients apart from their non-symptomatic peers. Studies are encouraged to parse apart such symptom constructs when examining neuroanatomical changes emerging after a FEP.
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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 | 0.002 |
| 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 | 0.001 |
| 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".