An interpretable machine learning model to explain the interplay between brain lesions and cortical atrophy in multiple sclerosis.

Multiple Sclerosis (MS) is the most common cause, (after trauma) of neurological disability in young adults in Western countries. While several Magnetic Resonance Imaging (MRI) studies have demonstrated a strong association between the presence of cortical grey matter atrophy and the progression of neurological impairment in MS patients, the neurobiological substrates of cortical atrophy in MS, and in particular its relationship with white matter (WM) and cortical lesions, remain unknown. The aim of this study was to investigate the interplay between cortical atrophy and different types of lesions at Ultra-High Field (UHF) 7 T MRI, including cortical lesions and lesions with a susceptibility rim (a feature which histopathological studies have associated with impaired remyelination and progressive tissue destruction). We combined lesion characterization with a recent machine learning (ML) framework which includes explainability, and we were able to predict cortical atrophy in MS from a handful of lesion-related features extracted from 7 T MR imaging. This highlights not only the importance of UHF MRI for accurately evaluating intracortical and rim lesion load, but also the differential contributions that these types of lesions may bring to determine disease evolution and severity. Also, we found that a small subset of features [WM lesion volume (not considering rim lesions), patient age and WM lesion count (not considering rim lesions), intracortical lesion volume] carried most of the prediction power. Interestingly, an almost opposite pattern emerged when contrasting cortical with WM lesion load: WM lesion load is most important when it is small, whereas cortical lesion load behaves in the opposite way.Clinical Relevance- Our results suggest that disconnection and axonal degeneration due to WM lesions and local cortical demyelination are the main factors determining cortical thinning. These findings further elucidate the complexity of MS pathology across the whole brain and the need for both statistical and mechanistic approaches to understanding the etiopathogenesis of lesions.

Relationship between Prolactin Plasma Levels and White Matter Volume in Women with Multiple Sclerosis.

BACKGROUND: The role of prolactin (PRL) on tissue injury and repair mechanisms in multiple sclerosis (MS) remains unclear. The aim of this work was to investigate the relationship between PRL plasma levels and brain damage as measured by magnetic resonance imaging (MRI). METHODS: We employed a chemiluminescence immunoassay for measuring plasma levels of PRL. We used a 1.5 T scanner to acquire images and Jim 4.0 and SIENAX software to analyse them. RESULTS: We included 106 women with relapsing remitting (RR) MS and stable disease in the last two months. There was no difference in PRL plasma levels between patients with and without gadolinium enhancement on MRI. PRL plasma levels correlated with white matter volume (WMV) (rho = 0.284, p = 0.014) but not with grey matter volume (GMV). Moreover, PRL levels predicted changes in WMV (Beta: 984, p = 0.034). CONCLUSIONS: Our data of a positive association between PRL serum levels and WMV support the role of PRL in promoting myelin repair as documented in animal models of demyelination. The lack of an increase of PRL in the presence of gadolinium enhancement, contrasts with the view considering this hormone as an immune-stimulating and detrimental factor in the inflammatory process associated with MS.