Brain Networks Underlying Catatonia
Bibliographic record
Abstract
Since the initial clinical delineation of catatonia in 1874, two distinct conceptual frameworks have emerged: one viewing catatonia as a psychomotor disorder, rooted in Kahlbaum’s legacy, and the other interpreting it as a primarily motor phenomenon, aligned with the perspectives of Kraepelin and Bleuler. This historical dichotomy is reflected in contemporary investigations into the pathophysiological mechanisms of catatonia. Neuroimaging studies utilizing motor and behavioral rating scales, such as the Bush-Francis Catatonia Rating Scale, have highlighted alterations in dopamine-mediated cortical and subcortical motor circuits – specifically the basal ganglia, supplementary motor area, primary motor cortex, and cerebellum – as key neuronal correlates of catatonia. In contrast, studies employing the Northoff Catatonia Rating Scale, a scale integrating affective, motor, and behavioral criteria within a psychomotor framework, have identified disruptions in higher-order frontoparietal networks, including the prefrontal, orbitofrontal, and primary motor cortices, as well as the limbic system. These networks appear to be dysregulated by imbalances in glutamatergic and gamma-aminobutyric acid transmission, underscoring the complex interplay between motor, affective, and cognitive domains in the pathophysiology of catatonia. This chapter introduces the pathophysiology of catatonia from a systemic (network-based) perspective, irrespective of its association with mental or medical disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".