Cortex Morphology in First-Episode Psychosis Patients With Neurological Soft Signs
Bibliographic record
Abstract
Schizophrenia is a complex brain disorder associated with numerous etiological factors and pathophysiological pathways leading to multiple clinical outcomes. Compelling evidence suggests that deviations in neurodevelopmental processes are a major risk factor of schizophrenia. The identification of patients with high neurodevelopmental deviance is an important issue as it could help to identify homogeneous subgroups of patients with similar pathophysiological pathways, a key step to decipher the etiology of this complex condition. Several clinical arguments suggest that schizophrenia patients with Neurological Soft Signs (NSS)--ie, observable defects in motor coordination, motor integration, and sensory integration--would have high neurodevelopmental deviance. Based on the analysis of magnetic resonance imaging of 44 first-episode psychosis patients, we compared the cortex morphology, a marker of brain development, in patients with NSS vs patients with nonsignificant NSS. The cortex morphology was automatically assessed from three-dimensional global sulcal index (g-SI, the ratio between total sulcal area and outer cortex area) and regional sulcal indexes (r-SI, the ratio between the area of pooled labeled sulci and the total outer cortex area). Patients with NSS were found to have a lower g-SI in both hemispheres and a lower r-SI in left dorsolateral prefrontal and right lateral occipital cortices. Exploratory analyses revealed correlations between NSS dimensions and r-SI in distinct cortical areas, including dorsolateral and medial prefrontal cortices, lateral temporal, occipital, superior parietal, and medial parieto-occipital cortices. These findings provide evidence of distinct neurodevelopmental pathways in patients with NSS as compared with patients with nonsignificant NSS.
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".