Parkinsonism, Psychomotor Slowing, Negative and Depressive Symptoms in Schizophrenia Spectrum and Mood Disorders: Exploring Their Intricate Nexus Using a Network Analytic Approach
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Parkinsonism, psychomotor slowing, negative and depressive symptoms show evident phenomenological similarities across different mental disorders. However, the extent to which they interact with each other is currently unclear. Here, we hypothesized that parkinsonism is an independent motor abnormality showing limited associations with psychomotor slowing, negative and depressive symptoms in schizophrenia spectrum (SSD), and mood disorders (MOD). STUDY DESIGN: We applied network analysis and community detection methods to examine the interplay and centrality (expected influence [EI] and strength) between parkinsonism, psychomotor slowing, negative and depressive symptoms in 245 SSD and 99 MOD patients. Parkinsonism was assessed with the Simpson-Angus Scale (SAS). We used the Positive and Negative Syndrome Scale (PANSS) to examine psychomotor slowing (item #G7), negative symptoms (PANSS-N), and depressive symptoms (item #G6). STUDY RESULTS: In SSD and MOD, PANSS item #G7 and PANSS-N showed the largest EI and strength as measures of centrality. Parkinsonism had small or no influence on psychomotor slowing, negative and depressive symptoms in SSD and MOD. In SSD and MOD, exploratory graph analysis identified one community, but parkinsonism showed a small influence on its occurrence. Network Comparison Test yielded no significant differences between the SSD and MOD networks (global strength p value: .396 and omnibus tests p value: .574). CONCLUSIONS: The relationships between the individual domains followed a similar pattern in both SSD and MOD highlighting their transdiagnostic relevance. Despite evident phenomenological similarities, our results suggested that parkinsonism is more independent of negative and depressive symptoms than psychomotor slowing in both SSD and MOD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".