Progressive Brain Changes Associated with Persistent Negative Symptoms Following a First Episode of Psychosis
Bibliographic record
Abstract
Early persistent negative symptoms (ePNS) refer to the presence of potentially idiopathic or primary negative symptoms and have been observed following a first episode of psychosis (FEP). There is evidence for cortical changes associated with ePNS and given that a FEP often occurs during a period of ongoing brain development and maturation, neuroanatomical changes may have a specific age related component. The current study examined cortical thickness (CT), hippocampal/amygdala volume and shape as a function of clinical trajectories and age using longitudinal structural imaging in FEP. T1-MRI scans were acquired for early (n = 21), secondary (n = 30), non-(n = 44) PNS patients with a FEP, and controls (n = 44). Cortical thickness and amygdalar–hippocampal volumes and surface area (SA) metrics were extracted from three time points over a two-year period. Linear mixed models were applied to test for a main effect of group, and age group interactions. Relative to the other groups, ePNS patients showed cortical thinning over time in temporal regions and a thickening with age primarily in prefrontal areas. They also exhibited reduced left amygdalar and right hippocampal volumes. Morphometry revealed decreased surface area in ePNS compared to other groups in left central amygdala. The current study demonstrates that FEP patients with ePNS show significantly different CT trajectories with age. Increased CT may be indicative of disruptions in cortical maturation processes within higher-order brain regions. Amygdalar-hippocampal changes with age are also linked to ePNS with converging results from volumetric and morphometric analyses. Taken together, these results could represent dynamic endophenotypes setting these ePNS patients apart from their non-symptomatic peers. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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) | 0.001 | 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".