We are very interested in investigating the relationship between MRI-derived biomarkers and clinical outcomes in MS, particularly early in the disease evolution, when an accurate prediction of long-term prognosis is crucial to better plan therapeutic interventions. Our final goal is to establish a more complete characterization of the patient’s stage and evolution based on MRI measures. Because MS is such a heterogeneous disease, we expect that a single biomarker will not be enough to achieve such a goal. We have solid experience in quantifying atrophy and delineating brain lesions, and we decided to investigate if both approaches are spatially related. Consequently, we focused on juxtacortical lesions and brain cortical thickness. Our working hypothesis was that cortical thickness would show a decrease in the areas close to a juxtacortical lesion.
Our findings indicate that a relationship exists between the presence of juxtacortical lesions and a thinning of the cortex in specific areas, but no spatial overlap could be observed. Thus, the presence of juxtacortical lesions might be an indicator of an already ongoing cortical neurodenegerative process in MS. Cortical damage has already shown a high correlation with clinical disability and cognitive impairment in patients with MS. Getting a reliable and accurate MRI-derived measure of the neurodegenerative component in MS is crucial now that new treatments are being developed with clear effects that promote neuroprotection and neurorepair. Brain volume is currently considered the biomarker of neurodegeneration, and large efforts are being done in order to bring brain volume and brain lesion measures into clinical routine. In the near future, it is likely that specific measures of cortical damage will also be translated into clinical routine.
This work was partially presented at the ECTRIMS and ESNR meetings in 2014 and published in AJNR in December 2015. We are now examining the relationship between brain lesions and gray matter connectivity. We are also starting to work with machine learning techniques to predict MS conversion in patients at an early stage of the disease by using MRI-derived parameters. Results will be presented at ASNR, ESNR, and ECTRIMS meetings.