Parameter inference from a non-stationary unknown process.

Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration to brain dynamics driven by ascending neuromodulation. Accordingly, there is a need for methods to analyze non-stationary processes, and yet, most time-series analysis methods that are used in practice on important problems across science and industry make the simplifying assumption of stationarity. One important problem in the analysis of non-stationary systems is the problem class that we refer to as parameter inference from a non-stationary unknown process (PINUP).

Comparative efficacy, cognitive effects and acceptability of electroconvulsive therapies for the treatment of depression: protocol for a systematic review and network meta-analysis.

Introduction: There have been important advances in the use of electroconvulsive therapy (ECT) to treat major depressive episodes. These include variations to the type of stimulus the brain regions stimulated, and the stimulus parameters (eg, stimulus duration/pulse width). Our aim is to investigate ECT types using a network meta-analysis (NMA) approach and report on comparative treatment efficacy, cognitive side effects and acceptability.