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Updated: Jun 20, 2026
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Predicting disability progression and cognitive worsening in multiple sclerosis using patterns of grey matter volumes
Elisa Colato1, Jonathan Stutters2, Carmen Tur2
1NMR Research Unit, Queen 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.
Objective:
In multiple sclerosis (MS), MRI measures at the whole brain or regional level are only modestly associated with disability, while network-based measures are emerging as promising prognostic markers. We sought to demonstrate whether data-driven patterns of covarying regional grey matter (GM) volumes predict future disability in secondary progressive MS (SPMS).
Methods:
We used cross-sectional structural MRI, and baseline and longitudinal data of Expanded Disability Status Scale, Nine-Hole Peg Test (9HPT) and Symbol Digit Modalities Test (SDMT), from a clinical trial in 988 people with SPMS. We processed T1-weighted scans to obtain GM probability maps and applied spatial independent component analysis (ICA). We repeated ICA on 400 healthy controls. We used survival models to determine whether baseline patterns of covarying GM volume measures predict cognitive and motor worsening.
Results:
We identified 15 patterns of regionally covarying GM features. Compared with whole brain GM, deep GM and lesion volumes, some ICA components correlated more closely with clinical outcomes. A mainly basal ganglia component had the highest correlations at baseline with the SDMT and was associated with cognitive worsening (HR=1.29, 95% CI 1.09 to 1.52, p<0.005). Two ICA components were associated with 9HPT worsening (HR=1.30, 95% CI 1.06 to 1.60, p<0.01 and HR=1.21, 95% CI 1.01 to 1.45, p<0.05). ICA measures could better predict SDMT and 9HPT worsening (C-index=0.69-0.71) compared with models including only whole and regional MRI measures (C-index=0.65-0.69, p value for all comparison <0.05).
Conclusions:
The disability progression was better predicted by some of the covarying GM regions patterns, than by single regional or whole-brain measures. ICA, which may represent structural brain networks, can be applied to clinical trials and may play a role in stratifying participants who have the most potential to show a treatment effect.
Insights
Spatial independent component analysis (ICA) of grey matter (GM) patterns better predicts disability progression in multiple sclerosis (MS) than traditional MRI measures. These network-based insights can aid clinical trials and patient stratification.
Area of Science:
- Neuroimaging
- Neurology
- Biostatistics
Background:
- Magnetic resonance imaging (MRI) measures of whole brain or regional grey matter (GM) volume show limited association with disability in multiple sclerosis (MS).
- Network-based MRI measures are emerging as more sensitive prognostic markers for predicting disease progression.
Purpose of the Study:
- To investigate whether data-driven patterns of covarying regional GM volumes can predict future disability in people with secondary progressive multiple sclerosis (SPMS).
Main Methods:
- Utilized cross-sectional structural MRI and longitudinal clinical data (Expanded Disability Status Scale, Nine-Hole Peg Test, Symbol Digit Modalities Test) from 988 individuals with SPMS.
- Applied spatial independent component analysis (ICA) to GM probability maps derived from T1-weighted scans.
- Employed survival models to assess the predictive power of baseline GM patterns on cognitive and motor worsening.
Main Results:
- Identified 15 patterns of covarying GM features, with some ICA components showing stronger correlations with clinical outcomes than whole brain or regional GM volumes.
- A basal ganglia-centric ICA component strongly correlated with Symbol Digit Modalities Test (SDMT) performance and predicted cognitive worsening (HR=1.29).
- Two ICA components predicted Nine-Hole Peg Test (9HPT) worsening (HR=1.30 and HR=1.21), and ICA measures demonstrated superior prediction of SDMT and 9HPT worsening (C-index=0.69-0.71) compared to conventional MRI models (C-index=0.65-0.69).
Conclusions:
- Covarying GM region patterns, identified through ICA, offer superior prediction of disability progression in SPMS compared to single regional or whole-brain MRI measures.
- ICA, representing structural brain networks, is applicable to clinical trials for stratifying participants likely to exhibit treatment effects.
