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Background <p>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.</p> Methods <p>In this multicenter cross-sectional study, consecutive adults with a clinically established FMDs diagnosis (<i>n</i> = 75, 74.7% female; mean age 44.20 ± 12.92) and age- and sex-matched HCs (<i>n</i> = 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 <i>k</i>-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.</p> Results <p>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).</p> Conclusions <p>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.</p> Trial Registration <p>Trial registration number NCT06328790. Registered on 26 March 2024.</p>

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Identifying diagnostic biomarkers in functional motor disorders through multimodal behavioral, neurophysiological, and imaging assessment using explainable machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月30日

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  • Marialuisa Gandolfi,
  • Angela Sandri,
  • Michela Russo,
  • Elisabetta Sarasso,
  • Andrea Gardoni,
  • Silvia Basaia,
  • Roberto Erro,
  • Sofia Cuoco,
  • Immacolata Carotenuto,
  • Carlo Ricciardi,
  • Francesco Amato,
  • Claudia Vinciguerra,
  • Annibale Botto,
  • Marianna Amboni,
  • Daniele Romano,
  • Ilaria Antonella Di Vico,
  • Mirta Fiorio,
  • Giulia Pedrotti,
  • Anna Paolicelli,
  • Mauro Crestani,
  • Anna Fratucello,
  • Giancarlo Mansueto,
  • Francesca Benedetta Pizzini,
  • Marco Barillari,
  • Matteo Francesco Lauriola,
  • Mariachiara Tozzi,
  • Francesca Rusciano,
  • Christian Geroin,
  • Melania Fasoli,
  • Angela Marotta,
  • Francesca Salaorni,
  • Giovanna Maddalena Squintani,
  • Sara Mariotto,
  • Stefano Tamburin,
  • Fabio Paio,
  • Giuseppe De Biasi,
  • Giuseppe Piscosquito,
  • Lucia Zenere,
  • Elisa Canu,
  • Paolo Barone,
  • Massimo Filippi,
  • Federica Agosta,
  • Maria Teresa Pellecchia,
  • Michele Tinazzi

摘要

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.