Neuroimaging in Multiple Sclerosis: Predicting Longitudinal Disability and Cognition

Brain imaging findings may offer predictive value for long-term disability progression and cognitive decline in patients with multiple sclerosis (MS), according to findings from a meta-analysis published in Multiple Sclerosis and Related Disorders.

Previous cross-sectional magnetic resonance imaging (MRI) studies have found a correlation between neuroimaging abnormalities and clinical and cognitive decline in MS.

Assessing the predictive value of neuroimaging on future cognitive decline or clinical impairment in MS, through reviewing longitudinal studies, may lead to earlier interventions and better disease management, according to the current study researchers.

Researchers conducted a multilevel random-effects meta-analysis to identify an estimate overall effect size for the predictive ability of neuroimaging for longitudinal cognitive and clinical decline in MS. The meta-analysis included 64 unique, relevant articles comprising 105 distinct sub-analyses. All studies included in the meta-analysis assessed the association between neuroimaging variables and long-term cognition and disability outcomes in patients with MS.

In the disability analyses that relied on the Expanded Disability Status Scale (EDSS), there was a medium overall pooled effect size (Pearson’s correlation coefficient r=0.42, 95% CI 0.37-0.46). In the meta-regression, the researchers found that analyses which exclusively evaluated gray matter tissue possessed significantly stronger effect sizes compared with analyses of white matter tissue (rz= 0.11; 95% CI, 0.01-0.21; se=0.05, t(87)=2.19; P =.031) or whole brain analyses (rz=0.18; 95% CI, 0.04-0.31; se=0.07, t(87)=2.60; P =.011).

The meta-analysis for cognition, which focused on Symbol Digits Modalities Test (SDMT) analyses, also yielded a medium overall pooled effect size (r=0.47; 95% CI, 0.32-0.60). In contrast to the meta-regression for EDSS, the meta-regression for SDMT did not identify significant study factors which influenced the pooled effect size.

According to the researchers, possible publication bias may have led to the low number of studies in the meta-analysis, which represents a study limitation. Additionally, the meta-analysis features selection bias, since the researchers “chose the largest effect size reported for each sub-analysis.”

The longitudinal predictive validity of neuroimaging in MS “can be better assessed through the community’s adoption of best data practices,” and MS data consortiums may assist “this endeavor by giving scientists broader access to clinical data for evaluating these methods’ reproducibility,” they concluded. Additionally, the researchers wrote that while “the future of neuroimaging predictive models is bright,” the future of the modality “offers growth in areas for continued improvement.”

Reference

Pike AR, James GA, Drew PD, Archer RL. Neuroimaging predictors of longitudinal disability and cognition outcomes in multiple sclerosis patients: A systematic review and meta-analysis. Published online December 6, 2021. Mult Scler Relat Disord. doi:10.1016/j.msard.2021.103452