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Structural brain network metrics better explain multiple sclerosis (MS) disability and processing speed deficits than traditional MRI measures. Network density and efficiency are key indicators of MS pathology and clinical outcomes.

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Area of Science:

  • Neuroimaging
  • Neurology
  • Network Science

Background:

  • Multiple sclerosis (MS) is a chronic neurological disease characterized by demyelination and axonal damage.
  • Conventional MRI metrics like atrophy and white matter lesions incompletely capture the complex pathophysiology of MS.
  • Understanding the relationship between brain structure and clinical impairment is crucial for managing MS.

Purpose of the Study:

  • To determine if structural brain network metrics correlate better with clinical disability and information processing speed in MS than atrophy measures.
  • To identify which network metrics best explain clinical impairment and cognitive function in patients with MS.
  • To compare the explanatory power of network metrics versus conventional MRI metrics for MS-related dysfunction.

Main Methods:

  • Cross-sectional study of 122 MS patients (relapsing-remitting, primary progressive, secondary progressive) and 51 healthy controls.
  • Structural brain networks reconstructed from diffusion-weighted MRI, calculating density, efficiency, and clustering coefficients.
  • Stepwise linear regression used to model the contribution of network and conventional MRI metrics to Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT) scores.

Main Results:

  • MS patients exhibited reduced network efficiency and clustering coefficient compared to controls; secondary progressive MS showed further reductions.
  • Structural network metrics significantly increased the variance explained for clinical disability and information processing speed.
  • Reduced network density and global efficiency, along with increased age, were associated with higher EDSS scores; lower gray matter volume, reduced efficiency, and male gender predicted worse SDMT performance.

Conclusions:

  • Structural brain network topology differs significantly across MS patient groups and healthy controls.
  • Network density and global efficiency are significant predictors of disability in MS, offering insights beyond conventional MRI.
  • Network metrics provide clinically relevant information about MS pathology and its impact on neurological function.