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Updated: Apr 4, 2026
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Motor network efficiency and disability in multiple sclerosis
Matteo Pardini1, Özgür Yaldizli2, Varun Sethi2
1From the NMR Research Unit (M.P., Ö.Y., V.S., N.M., Z.L., R.S.S., D.R.A., M.A.R., C.A.M.W.-K., D.H.M., D.T.C.), Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Queen Square, London, UK; the Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (M.P.), University of Genoa, Italy; the Department of Neurology (Ö.Y.), University Hospital Basel, Switzerland; the Department of Psychology (N.M.), Cardiff University, UK; the Department of Neurology (Z.L.), Xuanwu Hospital of Capital Medical University, Beijing, China; the Medical Statistics Department (D.R.A.), London School of Hygiene and Tropical Medicine, UK; and the National Institute for Health Research (NIHR) (D.T.C.), University College London Hospitals (UCLH) Biomedical Research Centre, UK. m.pardini@ucl.ac.uk.
Objective:
To develop a composite MRI-based measure of motor network integrity, and determine if it explains disability better than conventional MRI measures in patients with multiple sclerosis (MS).
Methods:
Tract density imaging and constrained spherical deconvolution tractography were used to identify motor network connections in 22 controls. Fractional anisotropy (FA), magnetization transfer ratio (MTR), and normalized volume were computed in each tract in 71 people with relapse onset MS. Principal component analysis was used to distill the FA, MTR, and tract volume data into a single metric for each tract, which in turn was used to compute a composite measure of motor network efficiency (composite NE) using graph theory. Associations were investigated between the Expanded Disability Status Scale (EDSS) and the following MRI measures: composite motor NE, NE calculated using FA alone, FA averaged in the combined motor network tracts, brain T2 lesion volume, brain parenchymal fraction, normal-appearing white matter MTR, and cervical cord cross-sectional area.
Results:
In univariable analysis, composite motor NE explained 58% of the variation in EDSS in the whole MS group, more than twice that of the other MRI measures investigated. In a multivariable regression model, only composite NE and disease duration were independently associated with EDSS.
Conclusions:
A composite MRI measure of motor NE was able to predict disability substantially better than conventional non-network-based MRI measures.
