Biomarkers of functional movement disorders — a systematic review
Bibliographic record
Abstract
INTRODUCTION: Functional movement disorders (FMD) are defined by diverse phenotypes of altered movements that lack corresponding pathology in an anatomical region, and are typically characterized by inconsistent findings on neurological examination. STATE OF THE ART: While there are several suggestive clinical features indicating FMD, objective biomarkers are still lacking. We conducted a systematic review of the literature with an emphasis on literature published after February 2019 aiming to summarise current knowledge on biomarkers of FMD. We divided our findings into four main categories: genetic, biofluid, neuroimaging, and electrophysiological biomarkers. For the differential diagnosis of functional tremor, functional tic-like behaviours (FTLB), and functional myoclonus, previous studies support the use of electrophysiological biomarkers. Evidence from neuroimaging research supports the multi-network model of FMD as a condition affecting the attentional, sensorimotor, self-agency/multimodal integration, and limbic/salience circuits. Biomarkers such as neurofilament light chain, inflammatory, and autoimmune factors should still be considered experimental, since results are based on small sample sizes. There is preliminary evidence from a genetic study that in FMD there is a complex interaction between individual predisposing risk genes involved in the serotonergic pathway. CLINICAL IMPLICATIONS: Although the diagnosis of FMD remains challenging, and depends mainly on clinical judgement, research is underway to identify potential biomarkers to improve diagnostic confidence. Previous studies indicate that, in addition to psychological symptoms, biological changes can be detected in patients with FMD. This is evidenced by different patterns of neurotransmission related to stress responses and emotional regulation. FUTURE DIRECTIONS: We believe it is vital to conduct larger trials in diverse populations from different regions of the world in order to find more reliable biomarkers 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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".