Multiple sclerosis (MS) is an immune-mediated disorder of the CNS manifested by recurrent attacks of neurological symptoms (related to focal inflammation) and gradual disability accrual (related to...

Introduction: Multiple sclerosis (MS) is a chronic central nervous system immune-mediated disease with an important inflammatory component associated with focal demyelination and widespread neurodegeneration. In most cases, the clinical presentation is relapsing-remitting, followed by a secondary progressive phase, characterized by disability accrual unrelated to relapses. In a minority, the phenotype is progressive from the beginning. Major therapeutic achievements have been made concerning the relapsing phase but modifying the evolution of progressive MS remains an unmet need. Areas covered: This review covers siponimod (BAF312), a new sphingosine 1-phosphate receptor modulator, and its role in the treatment of secondary progressive MS. The authors reviewed PubMed English literature using the keywords ‘siponimod’ or ‘BAF312’ and ‘multiple sclerosis.’ They also present the pharmacological profile of siponimod, as well as clinical efficacy and safety, with emphasis on the recently published results of a Phase III trial. Phase II data in relapsing MS are also summarized. Expert opinion: Siponimod may reduce the activity of the disease and has a modest effect on the gradual disability accrual. If approved, it may become one of the few available therapy options for secondary progressive MS.

Meta-analysis of summary results from published neuroimaging studies independently testing a common hypothesis is performed using coordinate based meta-analysis (CBMA), which tests for consistent activation (in the case of functional MRI studies) of the same anatomical regions. Using just the reported coordinates it is also possible to meta-analyse coactivated regions to reveal a network-like structure of coordinate clusters (network nodes) distributed at the coactivated locations and a measure of the coactivation strength (network edges), which is determined by the presence/absence of reported activation. Here a new coordinate-based method to estimate a network of coactivations is detailed, which utilises the Z score accompanying each reported. Coordinate based meta-analysis of networks (CBMAN) assumes that if the activation pattern reported by independent studies is truly consistent, then the relative magnitude of these Z scores might also be consistent. It is hypothesised that this is detectable as Z score covariance between coactivated regions provided the within study variances are small. Advantages of using the Z scores instead of coordinates to measure coactivation strength are that censoring by the significance thresholds can be considered, and that using a continuous measure rather than a dichotomous one can increase statistical power. CBMAN uses maximum likelihood estimation to fit multivariate normal distributions to the standardised Z scores, and the covariances are considered as edges of a network of coactivated clusters (nodes). Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA. Software to perform CBMAN is freely available.