Neuropsychiatric phenotypes in functional movement disorder
Bibliographic record
Abstract
OBJECTIVE: Functional movement disorder (FMD), the motor-dominant subtype of functional neurological disorder, is a complex neuropsychiatric condition. Patients with FMD also manifest non-motor symptoms. Given that patients with FMD are diagnosed based on motor phenotype, the contribution of non-motor features to the neuropsychiatric syndrome is not well characterized. The objective of this hypothesis-generating study was to explore potential novel, neuropsychiatric FMD phenotypes by combining movement disorder presentations with non-motor comorbidities including somatic symptoms, psychiatric diagnoses, and psychological traits. METHODS: This retrospective chart review evaluated 158 consecutive patients with a diagnosis of FMD who underwent deep phenotyping across neurological and psychiatric domains. Demographic, clinical, and self-report features were analyzed. A data-driven approach using cluster analysis was performed to detect patterns when combining the movement disorder presentation with somatic symptoms, psychiatric diagnoses, and psychological factors. These new neuropsychiatric FMD phenotypes were then tested using logistic regression models. RESULTS: Distinct neuropsychiatric FMD phenotypes emerged when stratifying by episodic vs. constant motor symptoms. Episodic FMD was associated with hyperkinetic movements, hyperarousal, anxiety, and history of trauma. In contrast, constant FMD was associated with weakness, gait disorders, fixed dystonia, activity avoidance, and low self-agency. Pain, fatigue, somatic preoccupation, and health anxiety were common across all phenotypes. CONCLUSION: This study found patterns spanning the neurological-psychiatric interface that indicate that FMD is part of a broader neuropsychiatric syndrome. Adopting a transdisciplinary view of illness reveals readily identifiable clinical factors that are relevant for the development and maintenance of FMD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".