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Related Experiment Video

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Confounder-adjusted MRI-based predictors of multiple sclerosis disability.

Yujin Kim1, Mihael Varosanec1, Peter Kosa1

  • 1Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States.

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Summary

Multiple sclerosis (MS) causes central nervous system (CNS) atrophy through mechanisms distinct from aging. Confounder-adjusted MRI models better predict disability, highlighting MS-specific CNS injury for future clinical trials.

Keywords:
disability outcomesgradient boosting machinemachine learningmagnetic resonance imagingmultiple sclerosisphysiological confounders

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Area of Science:

  • Neuroimaging
  • Neurology
  • Biostatistics

Background:

  • Aging and multiple sclerosis (MS) both lead to central nervous system (CNS) atrophy.
  • Previous research often interprets MS-related atrophy as accelerated aging.
  • This study investigates whether MS causes CNS atrophy via mechanisms independent of physiological aging.

Purpose of the Study:

  • To test the hypothesis that MS causes CNS atrophy through mechanisms different from physiological aging.
  • To isolate MS-specific effects on CNS structures by accounting for physiological confounders.
  • To develop predictive models of disability using MS-specific CNS changes.

Main Methods:

  • Prospective acquisition of brain MRI and neurological data from 646 participants (NCT00794352).
  • Retrospective, blinded measurement of CNS volumes using Lesion-TOADS and Spinal Cord Toolbox.
  • Stepwise multiple linear regression to identify and remove physiological confounders from 80 healthy volunteers.
  • Gradient Boosting Machine (GBM) models trained on MS patient data (n=408) using adjusted and unadjusted MRI features to predict disability scales.

Main Results:

  • Confounder adjustment revealed progressive loss of CNS white matter specific to MS.
  • GBM models, while showing decreased performance across validation sets, accurately predicted cognitive and physical disability.
  • Models utilizing confounder-adjusted MRI predictors demonstrated superior performance in the validation cohort compared to unadjusted models.

Conclusions:

  • Confounder-adjusted volumetric MRI features in GBM models reflect MS-specific CNS injury.
  • These adjusted models show stronger correlations with clinical outcomes than general brain atrophy measures.
  • The findings support exploring these refined MRI-based models in future multiple sclerosis clinical trials.