Tuesday, 5 November 2013

Losing Grey Matter

Brain atrophy ready for prime time? #MSBlog #MSResearch

"Go to any meeting of MS MRI experts and they will tell you that brain atrophy measurements are not ready for prime time in individual MSers. In other words the measurement is too variable and too many factors that can't be controlled affect brain atrophy measurements; for example dehydration, alcohol, normal variation, symptomatic treatments and changes in the MRI scanner. This study below addresses this issue and suggests that we can control for a large number of variables. Worryingly it shows that we underestimate brain atrophy rates and the majority is driven by gray matter changes. Gray matter sit on the surface of the brain the so called cortex and deep in the brain in the so called deep nuclei (thalamus and basal ganglia). The gray matter is where the nerve cell bodies reside and is vital for cognitive function. This study is another stark reminder that gray matter, matters and we need to be addressing its loss ASAP in the course of  the disease. How you may ask? With early highly-effective treatments, a treat-2-target of NEDA, a zero tolerance strategy and by incorporating new and more sensitive outcome measures into our NEDA metric; in particular normalisation of brain atrophy rates and spinal fluid neurofilament levels."


Jones et al. Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling. Neuroimage (Amst). 2013 Aug 13;3:171-9.

Background: Brain atrophy, measured by MRI, has been proposed as a useful surrogate marker for disease progression in MS. However, it is conventionally assumed that the accurate quantification of brain atrophy is made difficult, if not impossible, by changes in the parameters of the MRI acquisition, which are almost inevitable over the course of a longitudinal study since MRI technology changes rapidly. This state of affairs can negatively affect clinical trial design and limit the use of historical data. 

Objective & methods: Here, we investigate whether we can coherently estimate brain atrophy rates in a heterogeneous MS sample via linear mixed-effects multivariable regression, incorporating three critical assumptions: (1) using age at time of scanning, rather than time since baseline, as the regressor of interest; (2) scanning individuals with a variety of techniques; and (3) introducing a simple additive correction for major differences in MRI protocol. We fit the model to several measures of brain volume as the outcome in two MS populations: 1123 scans from 195 cases acquired for over approximately 7 years in two natural history protocols (Cohort 1), and 1331 scans from 69 cases seen for over 11 years who were primarily treated with two specific MS disease-modifying therapies (Cohort 2). We compared the mixed-effects model with additive correction for MRI acquisition parameters to a model fit without this correction and performed sample-size calculations to provide an estimate of the number of participants in an MS clinical trial that might be required to see a therapeutic effect of treatment using the approach described here. 

Results: The results show that without the additive correction for T1-weighted protocol parameters, atrophy was underestimated and subject-specific estimates were more narrowly distributed about the population mean. Ventricular CSF is the most consistently estimated brain volume, with a mean of 2.8%/year increase in Cohort 1 and 4.4%/year increase in Cohort 2. An interesting observation was that gray matter volume decreased and white matter volume remained essentially unchanged in both cohorts, suggesting that changes in ventricular CSF volume are a surrogate for changes in gray matter volume. 

Conclusion: In conclusion, the mixed-effects modeling framework presented here allows effective use of heterogeneously acquired and historical data in the study of brain atrophy in MS, potentially simplifying the design of future single- and multi-site clinical trials and natural history studies.

CoI: multiple

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