Cortical Contributions to Distinct Symptom Dimensions of Catatonia
Bibliographic record
Abstract
Catatonia is a central aspect of schizophrenia spectrum disorders (SSD) and most likely associated with abnormalities in affective, motor, and sensorimotor brain regions. However, contributions of different cortical features to the pathophysiology of catatonia in SSD are poorly understood. Here, T1-weighted structural magnetic resonance imaging data at 3 T were obtained from 56 right-handed patients with SSD. Using FreeSurfer version 6.0, we calculated cortical thickness, area, and local gyrification index (LGI). Catatonic symptoms were examined on the Northoff catatonia rating scale (NCRS). Patients with catatonia (NCRS total score ≥3; n = 25) showed reduced surface area in the parietal and medial orbitofrontal gyrus and LGI in the temporal gyrus (P < .05, corrected for cluster-wise probability [CWP]) as well as hypergyrification in rostral cingulate and medial orbitofrontal gyrus when compared with patients without catatonia (n = 22; P < .05, corrected for CWP). Following a dimensional approach, a negative association between NCRS motor and behavior scores and cortical thickness in superior frontal, insular, and precentral cortex was found (34 patients with at least 1 motor and at least 1 other affective or behavioral symptom; P < .05, corrected for CWP). Positive associations were found between NCRS motor and behavior scores and surface area and LGI in superior frontal, posterior cingulate, precentral, and pericalcarine gyrus (P < .05, corrected for CWP). The data support the notion that cortical features of distinct evolutionary and genetic origin differently contribute to catatonia in SSD. Catatonia in SSD may be essentially driven by cortex variations in frontoparietal regions including regions implicated in the coordination and goal-orientation of behavior.
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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".