Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of neurology
Identifying diagnostic biomarkers in functional motor disorders through multimodal behavioral, neurophysiological, and imaging assessment using explainable machine learning
BACKGROUND: Functional motor disorders (FMDs) represent a frequent and disabling neurological condition. The lack of reliable diagnostic biomarkers and their heterogeneity might affect diagnosis. We identified multimodal biomarkers distinguishing FMDs from healthy controls (HCs) using machine-learning approaches. METHODS: In this multicenter cross-sectional study, consecutive adults with a clinically established FMDs diagnosis (n = 75, 74.7% female; mean age 44.20 ± 12.92) and age- and sex-matched HCs (n = 75; 58.6% female; 48.42 ± 11.67) were recruited. All participants underwent standardized behavioral, neurophysiological, and brain MRI assessment exploring motor, exteroceptive, and interoceptive domains. A Random Forest (RF) classifier combined with repeated stratified k-fold cross-validation was trained on the collected features. Predictive performance was evaluated using accuracy, sensitivity, specificity, precision, F1-score, and AUC-ROC. SHapley Additive exPlanations interpreted feature importance. RESULTS: The strongest diagnostic biomarkers were lower dual-task effect scores for postural sway area under eyes-closed motor and cognitive conditions, and gait speed during the motor dual-task, followed by increased vDMN and basal ganglia networks functional connectivity, reduced baseline ipsilateral-contralateral R2 blink reflex area, and higher DNIC-to-baseline N2P2 amplitude ratios for the lower limb. The RF classifier achieved robust performance (accuracy 85.0%, sensitivity 83.9%, specificity 86.1%, F1-score 85.7%, AUC-ROC 0.921). CONCLUSIONS: Motor, functional neuroimaging, and neurophysiological markers demonstrated diagnostic value in distinguishing FMDs from healthy controls, addressing the current lack of objective tools and supporting more confident and accurate diagnosis of these heterogeneous conditions. TRIAL REGISTRATION: Trial registration number NCT06328790. Registered on 26 March 2024.
3 May 2026
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Systematic review of movement disorders mislabeled as functional: when incongruence misleads
BACKGROUND: Misdiagnosis of functional movement disorders (FMD) remains a concern for clinicians. We sought to review the phenomenology and clinical features associated with FMD misdiagnosis. METHODS: We conducted a systematic review of case reports, case series, and observational studies of adults diagnosed with FMD and subsequently reclassified as having another neurological or medical condition. PubMed was searched from inception to September 13, 2025, focusing on reported features of inconsistency, incongruence with known diseases, and low-validity features. Isolated (iMD) and mixed (mMD) movement disorders were analyzed separately. RESULTS: Forty-two included studies comprised 150 patients, of which 73 cases underwent detailed analysis. Gait disturbance (35%) and dystonia (27.5%) were the most common iMD phenomenologies (n = 40), while jerks and rigidity (24%) were the most common mMD phenomenology (n = 33). Incongruence with known diseases (50.7%), psychological factors (34.2%), and symptom variability (31.5%) were the main factors associated with misdiagnosis. Application of incongruence alone was associated with the highest rate of FMD misdiagnosis, similar to that of applying neither incongruence nor inconsistency (49% in both cases). Conversely, reliance on inconsistency alone was least associated with an FMD misdiagnosis (13.7%). Misdiagnoses of iMD were more likely in the absence of documented inconsistency (OR 3.94, 95% CI 1.32-12.82). CONCLUSIONS: FMD misdiagnoses, particularly of gait disorders and dystonia, were most commonly associated with the application of incongruence as diagnostic criterion. The lowest rate of FMD misdiagnosis was associated with ascertaining at least two positive neurological signs of inconsistency.
9 Apr 2026
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