Follow-up services for improving long-term outcomes in intensive care unit (ICU) survivors

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: Our main objective is to assess the effectiveness of follow-up services for ICU survivors that aim to identify and address unmet health needs related to the ICU period. We aim to assess the effectiveness in relation to health-related quality of life, mortality, depression and anxiety, post-traumatic stress disorder, physical function, cognitive function, ability to return to work or education and adverse events. Our secondary objectives are, in general, to examine both the various ways that follow-up services are provided and any major influencing factors. Specifically, we aim to explore: the effectiveness of service organisation (physician versus nurse led, face to face versus remote, timing of follow-up service); possible differences in services related to country (developed versus developing country); and whether participants had delirium within the ICU setting

Later-life crisis: Towards a holistic model

Crisis episodes have been most commonly associated with midlife, and correspondingly research on crisis after midlife is marked by its absence. Here, we report findings from a retrospective interview-based study of 21 adults about crises occurring between the ages of 60 and 69, in the first attempt to explore the holistic structure, process and experiential contents of later-life crisis. Basing our analysis on existing models of late-adult development, four key areas of later-life crisis were explored as follows: (1) life events and relationships, (2) self and identity, (3) motivation and goals and (4) cognition and affect. We were able to define a provisional common holistic process to later-life crisis episodes, shared by all participants, which included multiple loss-inducing stressful life events that provide a cumulative challenge to coping resources, a struggle with ego integrity, increased mortality awareness and the re-scaling of goals, activities and roles in ways that pertain to re-engagement, continuity or disengagement. Findings are discussed in relation to theories of adult development in later life, while strategies are proposed for future research on this understudied area

The star-formation history of the universe - an infrared perspective

A simple and versatile parameterized approach to the star formation history allows a quantitative investigation of the constraints from far infrared and submillimetre counts and background intensity measurements. The models include four spectral components: infrared cirrus (emission from interstellar dust), an M82-like starburst, an Arp220-like starburst and an AGN dust torus. The 60 $\mu$m luminosity function is determined for each chosen rate of evolution using the PSCz redshift data for 15000 galaxies. The proportions of each spectral type as a function of 60 $\mu$m luminosity are chosen for consistency with IRAS and SCUBA colour-luminosity relations, and with the fraction of AGN as a function of luminosity found in 12 $\mu$m samples. The luminosity function for each component at any wavelength can then be calculated from the assumed spectral energy distributions. With assumptions about the optical seds corresponding to each component and, for the AGN component, the optical and near infrared counts can be accurately modelled. A good fit to the observed counts at 0.44, 2.2, 15, 60, 90, 175 and 850 $\mu$m can be found with pure luminosity evolution in all 3 cosmological models investigated: $\Omega_o$ = 1, $\Omega_o$ = 0.3 ($\Lambda$ = 0), and $\Omega_o$ = 0.3, $\Lambda$ = 0.7. All 3 models also give an acceptable fit to the integrated background spectrum. Selected predictions of the models, for example redshift distributions for each component at selected wavelengths and fluxes, are shown. The total mass-density of stars generated is consistent with that observed, in all 3 cosmological models.Comment: 20 pages, 25 figures. Accepted for publication in ApJ. Full details of models can be found at http://astro.ic.ac.uk/~mrr/countmodel

Measuring cognitive effort without difficulty

An important finding in the cognitive effort literature has been that sensitivity to the costs of effort varies between individuals, suggesting that some people find effort more aversive than others. It has been suggested this may explain individual differences in other aspects of cognition; in particular that greater effort sensitivity may underlie some of the symptoms of conditions such as depression and schizophrenia. In this paper, we highlight a major problem with existing measures of cognitive effort that hampers this line of research, specifically the confounding of effort and difficulty. This means that behaviour thought to reveal effort costs could equally be explained by cognitive capacity, which influences the frequency of success and thereby the chance of obtaining reward. To address this shortcoming, we introduce a new test, the Number Switching Task (NST), specially designed such that difficulty will be unaffected by the effort manipulation and can easily be standardised across participants. In a large, online sample, we show that these criteria are met successfully and reproduce classic effort discounting results with the NST. We also demonstrate the use of Bayesian modelling with this task, producing behavioural parameters which can be associated with other measures, and report a preliminary association with the Need for Cognition scale

Recommendations for Bayesian hierarchical model specifications for case-control studies in mental health

Hierarchical model fitting has become commonplace for case-control studies of cognition and behaviour in mental health. However, these techniques require us to formalise assumptions about the data-generating process at the group level, which may not be known. Specifically, researchers typically must choose whether to assume all subjects are drawn from a common population, or to model them as deriving from separate populations. These assumptions have profound implications for computational psychiatry, as they affect the resulting inference (latent parameter recovery) and may conflate or mask true group-level differences. To test these assumptions we ran systematic simulations on synthetic multi-group behavioural data from a commonly used multi-armed bandit task (reinforcement learning task). We then examined recovery of group differences in latent parameter space under the two commonly used generative modelling assumptions: (1) modelling groups under a common shared group-level prior (assuming all participants are generated from a common distribution, and are likely to share common characteristics); (2) modelling separate groups based on symptomatology or diagnostic labels, resulting in separate group-level priors. We evaluated the robustness of these approaches to variations in data quality and prior specifications on a variety of metrics. We found that fitting groups separately (assumptions 2), provided the most accurate and robust inference across all conditions. Our results suggest that when dealing with data from multiple clinical groups, researchers should analyse patient and control groups separately as it provides the most accurate and robust recovery of the parameters of interest.Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstrac

Threat vigilance and intrinsic amygdala connectivity

A well-documented amygdala-dorsomedial prefrontal circuit is theorized to promote attention to threat (“threat vigilance”). Prior research has implicated a relationship between individual differences in trait anxiety/vigilance, engagement of this circuitry, and anxiogenic features of the environment (e.g., through threat-of-shock and movie-watching). In the present study, we predicted that—for those scoring high in self-reported anxiety and a behavioral measure of threat vigilance—this circuitry is chronically engaged, even in the absence of anxiogenic stimuli. Our analyses of resting-state fMRI data (N = 639) did not, however, provide evidence for such a relationship. Nevertheless, in our planned exploratory analyses, we saw a relationship between threat vigilance behavior (but not self-reported anxiety) and intrinsic amygdala-periaqueductal gray connectivity. Here, we suggest this subcortical circuitry may be chronically engaged in hypervigilant individuals, but that amygdala-prefrontal circuitry may only be engaged in response to anxiogenic stimuli

Anxiety Shapes Amygdala-Prefrontal Dynamics During Movie Watching

Background: A well-characterized amygdala–dorsomedial prefrontal circuit is thought to be crucial for threat vigilance during anxiety. However, engagement of this circuitry within relatively naturalistic paradigms remains unresolved. // Methods: Using an open functional magnetic resonance imaging dataset (Cambridge Centre for Ageing Neuroscience; n = 630), we sought to investigate whether anxiety correlates with dynamic connectivity between the amygdala and dorsomedial prefrontal cortex during movie watching. // Results: Using an intersubject representational similarity approach, we saw no effect of anxiety when comparing pairwise similarities of dynamic connectivity across the entire movie. However, preregistered analyses demonstrated a relationship between anxiety, amygdala-prefrontal dynamics, and anxiogenic features of the movie (canonical suspense ratings). Our results indicated that amygdala-prefrontal circuitry was modulated by suspense in low-anxiety individuals but was less sensitive to suspense in high-anxiety individuals. We suggest that this could also be related to slowed habituation or amplified anticipation. Moreover, a measure of threat-relevant attentional bias (accuracy/reaction time to fearful faces) demonstrated an association with connectivity and suspense. // Conclusions: Overall, this study demonstrated the presence of anxiety-relevant differences in connectivity during movie watching, varying with anxiogenic features of the movie. Mechanistically, exactly how and when these differences arise remains an opportunity for future research

Viral infections and asthma: An inflammatory interface?

© 2014 ERS. Asthma is a chronic inflammatory disease of the airways in which the majority of patients respond to treatment with corticosteroids and β2-adrenoceptor agonists. Acute exacerbations of asthma substantially contribute to disease morbidity, mortality and healthcare costs, and are not restricted to patients who are not compliant with their treatment regimens. Given that respiratory viral infections are the principal cause of asthma exacerbations, this review article will explore the relationship between viral infections and asthma, and will put forward hypotheses as to why virus-induced exacerbations occur. Potential mechanisms that may explain why current therapeutics do not fully inhibit virus-induced exacerbations, for example, β2-adrenergic desensitisation and corticosteroid insensitivity, are explored, as well as which aspects of virus-induced inflammation are likely to be attenuated by current therapy

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety