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Networks of microstructural damage predict disability in multiple sclerosis
Elisa Colato1, Ferran Prados2,3,4,5, Jonathan Stutters2
1Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK elisa.colato.18@ucl.ac.uk.
Background:
Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods.
Methods:
We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures.
Results:
We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001).
Conclusions:
GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials.
Insights
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.
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.
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