Objective: To develop a biomarker-based diagnostic algorithm for Functional Motor Disorders (FMD) diagnosis by modeling behavioral, neurophysiological, and MRI biomarkers.

Background: FMD is prevalent yet poorly understood, with inconsistent limb weakness, tremors, dystonia, and gait disturbances [1].Delayed diagnosis and inadequate treatment lead to severe disability.Predictive coding theories suggest symptoms stem from disrupted neural circuits integrating interoception, exteroception, and motor control, distorting bodily perceptions [2].Neuroscience advances support biomarkers for earlier diagnosis and better management.

Method: A cross-sectional study was conducted in a preliminary sample of 50 patients with FMD (mean age: 45.58 years; 76% females) and 50 matched healthy controls (HC).As previously detailed, the selection criteria, behavioral, neurophysiological, and MRI biomarkers were assessed in motor, exteroceptive, interoceptive domains, and with structural and functional MRI [3].The diagnostic algorithm was developed by explainable artificial intelligence (XAI) methods.The European Union funded the study-Next Generation EU-NRRP M6C2-Investment 2.1 Enhancement and strengthening of biomedical research in the NHS (PNRR-MAD-2022-12376826).

Results: FMD exhibited higher levels of depression, anxiety, alexithymia, pain and fatigue, and lower quality of life compared to HC (all p<0.05).Univariate analysis revealed a more negative cognitive dual-task effect on sway area (p=0.003) and gait speed (p=0.033) in FMD, indicating an improvement in postural stability but not in gait performance during a cognitive dual task.A lower R2 blink-reflex magnitude (p=0.036) and an increased ratio of HNCS to basal for N2/P2 laser-evoked potential amplitude in the lower limb (p=0.029) suggest impaired sensorimotor integration and descending pain modulation.The higher proprioceptive error (p=0.037) indicates an overestimating limb position in FMD.MRI findings showed increased left pallidal volume (p=0.034), functional connectivity within Basal Ganglia (p=0.004), and ventral Default Mode (p=0.001) networks in FMD than HC. The diagnostic algorithm achieved approximately 86.6% accuracy [Figure 1]. The interoception domain is still under investigation.

Conclusion: Motor and exteroceptive biomarkers might refine FMD diagnosis and therapeutic strategies, along with abnormal interactions between motor control circuits and emotional processing networks.

Figure 1. Feature Importance Analysis

References: [1] Tinazzi M, Morgante F, Marcuzzo E, Erro R, Barone P, Ceravolo R, Mazzucchi S, Pilotto A, Padovani A, Romito LM, Eleopra R, Zappia M, Nicoletti A, Dallocchio C, Arbasino C, Bono F, Pascarella A, Demartini B, Gambini O, Modugno N, Olivola E, Di Stefano V, Albanese A, Ferrazzano G, Tessitore A, Zibetti M, Calandra-Buonaura G, Petracca M, Esposito M, Pisani A, Manganotti P, Stocchi F, Coletti Moja M, Antonini A, Defazio G, Geroin C. Clinical Correlates of Functional Motor Disorders: An Italian Multicenter Study. Mov Disord Clin Pract. 2020 Sep 22;7(8):920-929. doi: 10.1002/mdc3.13077. PMID: 33163563; PMCID: PMC7604660.
[2] Hallett M, Aybek S, Dworetzky BA, McWhirter L, Staab JP, Stone J. Functional neurological disorder: new subtypes and shared mechanisms. Lancet Neurol. 2022 Jun;21(6):537-550. doi: 10.1016/S1474-4422(21)00422-1. Epub 2022 Apr 14. Erratum in: Lancet Neurol. 2022 Jun;21(6):e6. doi: 10.1016/S1474-4422(22)00179-X. PMID: 35430029; PMCID: PMC9107510.
[3] Gandolfi M, Sandri A, Mariotto S, Tamburin S, Paolicelli A, Fiorio M, et al. (2024) A window into the mind-brain-body interplay: Development of diagnostic, prognostic biomarkers, and rehabilitation strategies in functional motor disorders. PLoS ONE 19(9): e0309408. https://doi.org/10.1371/journal.pone.0309408

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MDS Abstracts - https://www.mdsabstracts.org/abstract/which-biomarker-based-diagnostic-algorithm-for-functional-motor-disorders-diagnosis/