Aberrant Long-Range Temporal Correlations in Depression Are Attenuated after Psychological Treatment

The spontaneous oscillatory activity in the human brain shows long-range temporal correlations (LRTC) that extend over time scales of seconds to minutes. Previous research has demonstrated aberrant LRTC in depressed patients; however, it is unknown whether the neuronal dynamics normalize after psychological treatment. In this study, we recorded EEG during eyes-closed rest in depressed patients (N = 71) and healthy controls (N = 25), and investigated the temporal dynamics in depressed patients at baseline, and after attending either a brief mindfulness training or a stress reduction training. Compared to the healthy controls, depressed patients showed stronger LRTC in theta oscillations (4–7 Hz) at baseline. Following the psychological interventions both groups of patients demonstrated reduced LRTC in the theta band. The reduction of theta LRTC differed marginally between the groups, and explorative analyses of separate groups revealed noteworthy topographic differences. A positive relationship between the changes in LRTC, and changes in depressive symptoms was observed in the mindfulness group. In summary, our data show that aberrant temporal dynamics of ongoing oscillations in depressive patients are attenuated after treatment, and thus may help uncover the mechanisms with which psychotherapeutic interventions affect the brain

Robust and Pareto Optimality of Insurance Contract

The optimal insurance problem represents a fast growing topic that explains the most efficient contract that an insurance player may get. The classical problem investigates the ideal contract under the assumption that the underlying risk distribution is known, i.e. by ignoring the parameter and model risks. Taking these sources of risk into account, the decision-maker aims to identify a robust optimal contract that is not sensitive to the chosen risk distribution. We focus on Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR)-based decisions, but further extensions for other risk measures are easily possible. The Worst-case scenario and Worst-case regret robust models are discussed in this paper, which have been already used in robust optimisation literature related to the investment portfolio problem. Closed-form solutions are obtained for the VaR Worst-case scenario case, while Linear Programming (LP) formulations are provided for all other cases. A caveat of robust optimisation is that the optimal solution may not be unique, and therefore, it may not be economically acceptable, i.e. Pareto optimal. This issue is numerically addressed and simple numerical methods are found for constructing insurance contracts that are Pareto and robust optimal. Our numerical illustrations show weak evidence in favour of our robust solutions for VaR-decisions, while our robust methods are clearly preferred for CVaR-based decisions

Deep quantile and deep composite triplet regression

A main difficulty in actuarial claim size modeling is that covariates may have different effects on the body of the conditional distribution and on its tail. To cope with this problem, we introduce a deep composite regression model whose splicing point is given in terms of a quantile of the conditional claim size distribution (rather than a constant). This allows us to simultaneously fit different regression models in the two different parts of the conditional distribution function. To facilitate M-estimation for such models, we introduce and characterize the class of strictly consistent scoring functions for the triplet consisting of a quantile, as well as the lower and upper expected shortfall beyond that quantile. In a second step, this elicitability result is applied to fit deep neural network regression models. We demonstrate the applicability of our approach and its superiority over classical approaches on a real data set from accident insurance.ISSN:0167-6687ISSN:1873-595

Gains in cognition through combined cognitive and physical training: The role of training dosage and severity of neurocognitive disorder

Physical as well as cognitive training interventions improve specific cognitive functions but effects barely generalize on global cognition. Combined physical and cognitive training may overcome this shortcoming as physical training may facilitate the neuroplastic potential which, in turn, may be guided by cognitive training. This study aimed at investigating the benefits of combined training on global cognition while assessing the effect of training dosage and exploring the role of several potential effect modifiers. In this multi-center study, 322 older adults with or without neurocognitive disorders were allocated to a computerized, game-based, combined physical and cognitive training group (n=237) or a passive control group (n=85). Training group participants were allocated to different training dosages ranging from 24 to 110 potential sessions. In a pre-posttest design, global cognition was assessed by averaging standardized performance in working memory, episodic memory and executive function tests. The intervention group increased in global cognition compared to the control group, p=.002, Cohen's d=0.31. Exploratory analysis revealed a trend for less benefits in participants with more severe neurocognitive disorder, p=.08 (cognitively healthy: d=0.54; mild cognitive impairment: d=0.19; dementia: d=0.04). In participants without dementia, we found a dose-response effect of the potential number and of the completed number of training sessions on global cognition, p=.008 and p=.04, respectively. The results indicate that combined physical and cognitive training improves global cognition in a dose-responsive manner but these benefits may be less pronounced in older adults with more severe neurocognitive disorder. The long-lasting impact of combined training on the incidence and trajectory of neurocognitive disorders in relation to its severity should be assessed in future long-term trials. © 2015 Bamidis, Fissler, Papageorgiou, Zilidou, Konstantinidis, Billis, Romanopoulou, Karagianni, Bearatis, Tsapanou, Tsilikopoulou, Grigoriadou, Ladas, Kyrillidou, Tsolaki, Frantzidis, Sidiropoulos, Siountas, Matsi, Papatriantafyllou, Margioti, Nika, Schlee, Elbert, Tsolaki, Vivas and Kolassa