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Updated: Dec 28, 2025
11:35
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
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Reduced Global Efficiency and Random Network Features in Patients with Relapsing-Remitting Multiple Sclerosis with
R Hawkins1, A S Shatil1, L Lee2
1From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.).
AJNR. American Journal of Neuroradiology
|February 22, 2020
Summary
Patients with multiple sclerosis (MS) and cognitive impairment exhibit more random brain network patterns and reduced efficiency, impacting cognition. Normalized path length, combined with white matter volume, significantly predicts cognitive performance in MS patients.
Area of Science:
- Neuroscience
- Graph Theory Applications
- Cognitive Impairment Research
Background:
- Graph theory analyzes cortical structural connectivity using network characteristics.
- Relapsing-remitting multiple sclerosis (MS) can be associated with cognitive impairment.
- Understanding the relationship between brain network structure and cognition in MS is crucial.
Purpose of the Study:
- To quantify and assess the relationship between cortical network characteristics and cognition in patients with MS.
- To compare structural gray matter network properties between healthy controls, MS patients with cognitive impairment, and MS patients without cognitive impairment.
- To determine the predictive value of network parameters for cognitive function in MS.
Main Methods:
- Compared subject-specific structural gray matter network properties using graph theory.
- Included 18 healthy controls, 25 MS patients with cognitive impairment, and 55 MS patients without cognitive impairment.
- Utilized backward stepwise multivariable regression to identify predictive factors for 5 neurocognitive domains, adjusting for confounders.
Main Results:
- Greater path length and lower normalized path length correlated with average cognition in MS patients compared to controls.
- Lower normalized path length was associated with reduced average cognition in MS patients with cognitive impairment.
- Increased normalized path length predicted better performance in processing, learning, and executive domains; it also improved prediction models for cognitive scores.
Conclusions:
- Patients with MS and cognitive impairment show more random network features and reduced global efficiency, affecting cognitive domains.
- Normalized path length, particularly when combined with normal-appearing white matter volume, significantly improves the prediction of cognitive performance in MS.
- Network analysis using graph theory offers valuable insights into cognitive deficits in multiple sclerosis.
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