Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evaluating the effectiveness of simvastatin in slowing the progression of disability in secondary progressive multiple sclerosis: a synopsis of MS-STAT2, a multicentre, randomised controlled, double-blind, phase 3 clinical trial.

Health technology assessment (Winchester, England)·2026
Same author

Moving artificial intelligence from research to real-world clinical use in neurology.

Nature reviews. Neurology·2026
Same author

Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype.

Brain communications·2026
Same author

Concerns regarding the 2024 revisions of the McDonald criteria for diagnosis of multiple sclerosis - Authors' reply.

The Lancet. Neurology·2026
Same author

Anti-Nogo-A NG101 treatment induces changes in spinal cord micro- and macrostructure following spinal cord injury.

Nature communications·2026
Same author

Performance of the 2024 McDonald Criteria in Patients Under Evaluation for Suspected Multiple Sclerosis.

Neurology·2026

Related Experiment Video

Updated: Mar 12, 2026

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

10.0K

Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest.

Arman Eshaghi1, Viktor Wottschel2, Rosa Cortese2

  • 1From the Queen Square MS Centre, Institute of Neurology (A.E., V.W., R.C., O.C.), Centre for Medical Image Computing (CMIC), Department of Computer Science (A.E., V.W., D.C.A.), and Faculty of Brain Sciences (A.J.T.), University College London, UK; MS Research Centre (A.E., M.A.S.), Neuroscience Institute, Tehran University of Medical Sciences, Iran; Advanced Neuroimaging Lab (M.C.), Neurology Clinic B, Department of Neurological and Movement Sciences, University of Verona; Neuroimaging Unit (M.C.), Euganea Medica, Padua, Italy; and National Institute of Health Research (NIHR) (A.J.T., O.C.), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK. arman.eshaghi.14@ucl.ac.uk.

Neurology
|November 4, 2016
PubMed
Summary

Brain gray matter imaging can differentiate multiple sclerosis (MS) from neuromyelitis optica (NMO) with 74% accuracy. Combining thalamic volume and white matter lesion volume improved MS vs. NMO classification to 80%.

More Related Videos

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4
09:29

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4

Published on: August 21, 2017

12.1K
Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients
12:23

Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients

Published on: April 14, 2014

14.6K

Related Experiment Videos

Last Updated: Mar 12, 2026

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

10.0K
Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4
09:29

Induction of Paralysis and Visual System Injury in Mice by T Cells Specific for Neuromyelitis Optica Autoantigen Aquaporin-4

Published on: August 21, 2017

12.1K
Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients
12:23

Dynamic Visual Tests to Identify and Quantify Visual Damage and Repair Following Demyelination in Optic Neuritis Patients

Published on: April 14, 2014

14.6K

Area of Science:

  • Neuroimaging
  • Neurology
  • Machine Learning in Medicine

Background:

  • Multiple sclerosis (MS) and neuromyelitis optica (NMO) are distinct neurological conditions.
  • Accurate differentiation between MS and NMO is crucial for appropriate treatment.
  • Current diagnostic methods can sometimes be challenging for distinguishing these diseases.

Purpose of the Study:

  • To evaluate the efficacy of brain gray matter (GM) imaging measures in differentiating between MS and NMO.
  • To assess the performance of random-forest classification models in distinguishing these conditions.

Main Methods:

  • Utilized T1 and T2/fluid-attenuated inversion recovery MRI scans from 90 participants in Tehran and 54 in Padua.
  • Calculated volume, thickness, and surface of cortical GM regions and deep GM nuclei.
  • Constructed three random-forest models to classify MS vs. NMO and each disease group against healthy controls (HCs).

Main Results:

  • The random-forest classifier distinguished MS from NMO with 74% accuracy (77% sensitivity, 72% specificity).
  • Thalamic volume was identified as the most discriminating GM measure.
  • Incorporating thalamic volume and white matter lesion volume improved MS vs. NMO classification accuracy to 80%.

Conclusions:

  • Automated GM imaging biomarkers can effectively differentiate NMO from MS.
  • These imaging biomarkers may aid in the differential diagnosis of MS and NMO in clinical practice.
  • The findings were consistent across a two-center study setting.