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Fatigue is one of the most common symptoms in multiple sclerosis (MS), with a major impact on patients’ quality of life. Currently, treatment proceeds by trial and error with limited success, probably due to the presence of multiple different underlying mechanisms. Recent neuroscientific advances offer the potential to develop tools for differentiating these mechanisms in individual patients and ultimately provide a principled basis for treatment selection. However, development of these tools for differential diagnosis will require guidance by pathophysiological and cognitive theories that propose mechanisms which can be assessed in individual patients. This article provides an overview of contemporary pathophysiological theories of fatigue in MS and discusses how the mechanisms they propose may become measurable with emerging technologies and thus lay a foundation for future personalised treatments.

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms. This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in "Bayesian brain" concepts of perception from cognitive neuroscience, such as predictive coding. These concepts regard the brain as embodying a model of its environment (including the external world and the body) which predicts future sensory inputs and is updated by prediction errors, depending on how precise these error signals are. This article offers a concrete proposal how core concepts of MBCT-(i) the being mode (accepting whatever sensations arise, without judging or changing them), (ii) decentering (experiencing thoughts and percepts simply as events in the mind that arise and pass), and (iii) cognitive reactivity (changes in mood reactivate negative beliefs)-could be understood in terms of perceptual and metacognitive processes that draw on specific computational mechanisms of the "Bayesian brain." Importantly, the proposed theory can be tested experimentally, using a combination of behavioral paradigms, computational modelling, and neuroimaging. The novel theoretical perspective on MBCT described in this paper may offer opportunities for finessing the conceptual and practical aspects of MBCT.

Background: Clinician-assessed Expanded Disease Status Scale (EDSS) is gold standard in clinical investigations but normally unavailable in population-based, patient-centred MS-studies. Our objective was to develop a selfreported gait measure reflecting EDSS-categories. Methods: We developed the self-reported disability status scale (SRDSS) with three categories (≤3.5, 4-6.5, ≥7) based on three mobility-related questions. The SRDSS was determined for 173 persons with MS and validated against clinical EDSS to calculate sensitivity and specificity. Results: Accuracy was 88.4% (153 correctly classified) and weighted kappa 0.73 (0.62-0.84). Sensitivity/specificity-pairs were 94.5%/77.8%, 69.0%/94.7% and 100%/98.2% for SRDSS ≤3.5, 4-6.5 and ≥7, respectively. Conclusions: Self-reported SRDSS approximates EDSS-categories well and fosters comparability between clinical and population-based studies. questionnaire of the Swiss MS Registry (SMSR), only 11% knew their EDSS-score (unpublished data). This lack of knowledge amongst PwMS about the own EDSS poses substantial challenges for observational studies relying on patient self-reports such as the SMSR or the UK MS Registry, because this hinders interstudy comparability (Ford et al., 2012;Puhan et al., 2018;Steinemann et al., 2018).Although self-reported EDSS-proxy measures have been proposed, no instrument has emerged as a standard (Collins et al., 2016). Therefore, there is still a need for short, reliable, and robust instruments