Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France; Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy; Corresponding author
A. Pathak
National Brain Research Centre, Manesar, Gurgaon, Haryana, India
A. Ziaeemehr
Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
E. Troisi Lopez
Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy; Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
L. Cipriano
Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy; Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
A. Romano
Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy; Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
M. Sparaco
Department of Advanced Medical and Surgical Sciences, University of Campania Luigi Vanvitelli, Caserta, Italy
M. Quarantelli
Biostructure and Bioimaging Institute, National Research Council, Naples, Italy
A. Banerjee
National Brain Research Centre, Manesar, Gurgaon, Haryana, India
G. Sorrentino
Department of Motor Sciences and Wellness, Parthenope University of Naples, Naples, Italy; Institute for Diagnosis and Cure Hermitage Capodimonte, Naples, Italy
V. Jirsa
Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
M. Hashemi
Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
Summary: Multiple sclerosis (MS) diagnosis typically involves assessing clinical symptoms, MRI findings, and ruling out alternative explanations. While myelin damage broadly affects conduction speeds, traditional tests focus on specific white-matter tracts, which may not reflect overall impairment accurately.In this study, we integrate diffusion tensor immaging (DTI) and magnetoencephalography (MEG) data into individualized virtual brain models to estimate conduction velocities for MS patients and controls. Using Bayesian inference, we demonstrated a causal link between empirical spectral changes and inferred slower conduction velocities in patients. Remarkably, these velocities proved superior predictors of clinical disability compared to structural damage.Our findings underscore a nuanced relationship between conduction delays and large-scale brain dynamics, suggesting that individualized velocity alterations at the whole-brain level contribute causatively to clinical outcomes in MS.
