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Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes.

Eloy Martinez-Heras1, Elisabeth Solana1, Francesc Vivó1

  • 1Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain.

Journal of Neurology, Neurosurgery, and Psychiatry
|June 15, 2023
PubMed
Summary
This summary is machine-generated.

Brain connectivity changes in multiple sclerosis (MS) vary by clinical phenotype. Secondary progressive MS shows the most widespread disruptions, and machine learning can differentiate MS types based on these network alterations.

Keywords:
MULTIPLE SCLEROSISNEUROIMMUNOLOGY

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Area of Science:

  • Neuroimaging
  • Neurology
  • Medical Physics

Background:

  • Multiple sclerosis (MS) is a chronic neurological disease characterized by demyelination and axonal damage.
  • Understanding the progression of brain network alterations in MS is crucial for diagnosis and treatment.
  • Distinct MS phenotypes exhibit varying degrees of neurological impairment and disease trajectory.

Purpose of the Study:

  • To quantify the severity of diffusion-based brain connectivity changes in multiple sclerosis (MS) as the disease progresses.
  • To identify microstructural network characteristics associated with different MS clinical phenotypes.
  • To evaluate the utility of machine learning in classifying MS phenotypes based on brain connectivity.

Main Methods:

  • Brain magnetic resonance imaging (MRI) and clinical data were collected from 823 individuals with MS and 221 healthy controls across 8 MAGNIMS centers.
  • Patients were categorized into four phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive, and primary progressive MS.
  • Advanced tractography was employed to generate connectivity matrices, followed by analysis of graph-derived measures and fractional anisotropy (FA). Support vector machine (SVM) algorithms were used for group classification.

Main Results:

  • Clinically isolated syndrome and relapsing-remitting MS patients showed similar network changes compared to controls.
  • Secondary progressive MS patients exhibited significant differences in global and local network properties, with widespread reductions in FA.
  • Primary progressive MS patients displayed fewer network alterations and limited FA reductions compared to earlier MS stages.
  • SVM achieved 81% accuracy in discriminating MS patients from controls and 64-74% accuracy in differentiating between MS phenotypes.

Conclusions:

  • Brain connectivity is demonstrably disrupted in MS, with distinct patterns observed across different clinical phenotypes.
  • Secondary progressive MS is characterized by more extensive connectivity alterations.
  • Machine learning classification models can effectively distinguish between MS types, with subcortical connections being key discriminators.