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Networks of microstructural damage predict disability in multiple sclerosis.

Elisa Colato1, Ferran Prados2,3,4,5, Jonathan Stutters2

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Network-based MRI markers in white and grey matter predict disability and cognitive decline in multiple sclerosis (MS). This approach can identify patients at higher risk for worsening symptoms and aid clinical trial stratification.

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Area of Science:

  • Neuroimaging
  • Neurology
  • Biomarkers

Background:

  • Network-based magnetic resonance imaging (MRI) measures are increasingly recognized as important markers in multiple sclerosis (MS).
  • Identifying specific white matter (WM) and grey matter (GM) networks affected by damage is crucial for understanding disease progression.

Purpose of the Study:

  • To identify WM and GM networks of microstructural damage that predict disability progression and cognitive worsening in MS patients.
  • To utilize data-driven methods for discovering predictive network markers.

Main Methods:

  • Analysis of data from 1836 MS participants across discovery and replication cohorts.
  • Calculation of standardized T1w/T2w ratio maps in GM and WM, followed by spatial independent component analysis to define damage networks.
  • Utilizing Cox proportional hazard models to assess the predictive value of network measures for confirmed disability progression (CDP) and Symbol Digit Modalities Test (SDMT) worsening.

Main Results:

  • Identification of 8 WM and 7 GM networks showing regional covariation in sT1w/T2w measures in both cohorts.
  • Specific network loadings in the anterior corona radiata and temporo-parieto-frontal regions predicted higher risk of CDP.
  • Network loadings in the arcuate fasciculus, corpus callosum, deep GM, and cortico-cerebellar areas, along with lesion load, predicted higher risk of SDMT worsening.

Conclusions:

  • Networks of microstructural changes in both GM and WM serve as reliable predictors of disability and cognitive worsening in MS.
  • This network-based approach can be valuable for identifying high-risk MS patients and stratifying participants in clinical trials.