Age-related cortical thickness trajectories in first episode psychosis patients presenting with early persistent negative symptoms
Bibliographic record
Abstract
Recent work has clearly established that early persistent negative symptoms (ePNS) can be observed following a first episode of psychosis (FEP), and can negatively affect functional outcome. There is also evidence for cortical changes associated with ePNS. Given that a FEP often occurs during a period of ongoing complex brain development and maturation, neuroanatomical changes may have a specific age-related component. The current study examines cortical thickness (CT) and trajectories with age using longitudinal structural imaging. Structural T1 volumes were acquired at three time points for ePNS (N=21), PNS due to secondary factors (N=31), non-PNS (N=45) patients, and controls (N=48). Images were processed using the CIVET pipeline. Linear mixed models were applied to test for the main effects of (a) group, (b) time, and interactions between (c) time and group membership, and (d) age and group membership. Compared with the non-PNS and secondary PNS patient groups, the ePNS group showed cortical thinning over time in temporal regions and a thickening with age primarily in prefrontal areas. Early PNS patients also had significantly different linear and quadratic age relationships with CT compared with other groups within cingulate, prefrontal, and temporal cortices. 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. Individuals with ePNS underline a unique subgroup of FEP patients that are differentiated at the clinical level and who exhibit distinct neurobiological patterns compared with their non-PNS peers.
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 | 0.002 |
| 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 | 0.000 |
| 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".