Correlation between the different ways disease progression is monitored in multiple sclerosis (MS) can be improved by looking at brain networks, according to research published in Neurology.
Researchers led by BRC-supported Dr Declan Chard developed a composite MRI-based measure of motor network integrity to determine if it could explain disability better than conventional MRI measures in people with MS.
Sensitively measuring disease progression is very important when showing that treatments for MS work and disease progression in MS is monitored in two major ways: MRI; and physical examination using disability rating scales such as the Expanded Disability Status Scale (EDSS).
The EDSS is the most commonly used clinical score used in MS treatment trials and is heavily weighted towards mobility. However, when compared with MRI it is relatively insensitive to change. Whilst MRI measures, such as white matter lesion load, offer greater sensitivity they are not accepted as outcome measures in MS as their correlation with disability is modest.
To see if correlation between MRI measures and EDSS scores could be improved the team studied 71 MS patients using a neuroimaging technique called tractography, which allowed them to see 3D visualisations of neural pathways; principal components of several MRI measures to reduce the dimensionality of multivariate data whilst preserving as much of the relevant information as possible; and graph theory to integrate them.
The team found that this composite measure of motor network efficiency explained 58% of the variation in EDSS, more than twice that of the other MRI measures investigated. As a result, the composite MRI measure of motor network integrity was able to predict disability substantially better than conventional non-network-based MRI measures.
The results may have significant implications for use of MRI in MS clinical trials, as they suggest that multiparameter MRI-based measures of motor network integrity may have a useful role in the assessment of clinically relevant pathology. MRI is increasingly used to assess the likely efficacy of new treatments, before researchers commit to large (and often expensive) trials based on clinical outcome measures.
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