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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.

Neurology
|August 31, 2015
PubMed
Summary
This summary is machine-generated.

A new composite MRI measure of motor network efficiency (NE) significantly predicts multiple sclerosis (MS) disability better than conventional MRI methods. This advanced technique offers improved insights into disease progression.

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Area of Science:

  • Neuroimaging
  • Neurology
  • Biomedical Engineering

Background:

  • Multiple sclerosis (MS) is a demyelinating disease affecting the central nervous system.
  • Assessing neurological disability in MS patients often relies on conventional Magnetic Resonance Imaging (MRI) metrics.
  • There is a need for more sensitive MRI measures to capture the impact of MS on neural networks.

Purpose of the Study:

  • To develop a novel composite MRI-based measure of motor network integrity.
  • To evaluate if this composite measure better explains disability in MS patients compared to traditional MRI metrics.

Main Methods:

  • Utilized tract density imaging and constrained spherical deconvolution tractography to map motor networks in controls and MS patients.
  • Calculated Fractional Anisotropy (FA), Magnetization Transfer Ratio (MTR), and tract volume for motor tracts.
  • Employed Principal Component Analysis and graph theory to derive a composite motor network efficiency (NE) metric.

Main Results:

  • The composite motor NE explained 58% of the variance in the Expanded Disability Status Scale (EDSS) in the MS cohort.
  • This predictive power was more than double that of other conventional MRI measures.
  • In multivariable analysis, composite motor NE and disease duration were the sole independent predictors of EDSS.

Conclusions:

  • A composite MRI measure of motor network efficiency demonstrates superior ability in predicting disability in MS.
  • This advanced, network-based approach surpasses conventional, non-network-based MRI measures for disability assessment in MS.