Switching state-space modeling of neural signal dynamics.

Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity.

Disease modifying therapy management of multiple sclerosis after stem cell therapies: A retrospective case series.

Background: Stem cell therapies (SCT) have not received formal regulatory approval for the treatment of people with multiple sclerosis (PwMS), but PwMS may seek various options on their own accord. The current literature largely focuses on the efficacy and safety of SCT in PwMS in clinical trials, in particular autologous hematopoietic stem cell transplantation (aHSCT), in carefully selected participants. There is little reported on the MS disease modifying therapy (DMT) management of PwMS who choose to undergo SCT outside of these trials.

Electronic pill bottles to monitor and promote medication adherence for people with multiple sclerosis: A randomized, virtual clinical trial.

Objective: We perform a randomized trial to test the impact of electronic pill bottles with audiovisual reminders on oral disease modifying therapy (DMT) adherence in people with MS (PwMS).

Methods: Adults with multiple sclerosis (MS) taking an oral DMT were randomized 1:1 for 90 days to remote smartphone app- and pill bottle-based (a) adherence monitoring, or (b) adherence monitoring with audiovisual medication reminders. Optimal adherence was defined as the proportion of doses taken ±3 h of the scheduled time.