No difference in postprandial mesenteric blood flow between healthy younger and elderly individuals.

We recently used phase-contrast magnetic resonance imaging (PC-MRI) to demonstrate an attenuated postprandial blood flow response in the superior mesenteric artery (SMA) in patients with Parkinson's disease compared to age- and sex-matched healthy controls. Since both groups showed substantial inter-individual variations, we extended the cohort of controls with a group of young individuals to investigate possible age-related effects. Seventeen healthy young subjects aged < 30 years and 17 elderly subjects aged > 50 years underwent serial PC-MRI to measure the postprandial blood flow response in the SMA after ingestion of a standardized liquid test meal (∼400 kcal). Postprandial blood flow dynamics in SMA did not differ between young and elderly subjects. A noticeable inter-individual variation in postprandial intestinal blood flow increase was found, and approximately 30% of the variation could be explained by the preprandial blood flow. Regardless of age, some subjects showed a remarkable transient SMA blood flow increase immediately after meal intake. This study provides tentative evidence that postprandial blood flow dynamics in SMA in healthy young and elderly subjects do not substantially differ, indicating that age is without impact on vascular response in SMA as an indicator for regulation of mesenteric perfusion in response to food intake.

Performing well but not appreciating it - A trait feature of anorexia nervosa.

Background: Despite advances in the etiology of anorexia nervosa (AN), a large subgroup of individuals does not profit optimally from treatment. Perfectionism has been found to be a risk factor predicting the onset, severity, and duration of AN episodes. To date, perfectionism has been studied predominantly by the use of self-report questionnaires, a useful approach that may, however, be impacted by demand characteristics, or other distortions of introspective or metacognitive access. Methods: Here we circumvent these problems via a behavioral paradigm in which participants perform a modified Go/NoGo task, whilst self-evaluating their performance. We compared a group of 33 adolescent females during their first episode of AN (age = 16.0) with 29 female controls (age = 16.2), and 23 adolescent girls recovered from AN (age = 18.3) with 23 female controls (age = 18.5). The controls were closely matched by intelligence quotient and age to the two clinical groups. Results: First-episode AN and control participants performed equally well on the task (reaction time and errors of commission), whereas the recovered group displayed significantly faster reaction times but incurred the same error rate. Despite performing at least as good as and predominantly better than control groups, both clinical groups evaluated their performances more negatively than controls. Conclusion: We offer a novel behavioral method for measuring perfectionism independent of self-report, and we provide tentative evidence that this behavioral manifestation of perfectionism is evident during first-episode AN and persists even after recovery.

Does report modality modulate psychophysical sensitivity? The jury remains out.

Scientific studies of perception use motoric reports as the principal means of communicating subjective experience. In such experiments, a widely held and implicit assumption is that the motor action conveys but does not tamper with perceptual experience. We tested nine observers on a luminance detection task in a cross-over repeated measures design. In separate conditions, observers reported their detection via movements of either their hands or eyes. We found only anecdotal evidence for any modality-dependent effect on psychophysical sensitivity. We also reanalyzed an existing dataset from which deployed a similar detection paradigm involving hand and eye reports. In the four paradigm variants tested, we again only found anecdotal evidence for the effect of report modality on psychophysical sensitivity. Both studies reported here provide only anecdotal evidence; thus, whether we can replicate report-dependent perceptual effects still needs to be resolved. We argue why this remains an important question for consciousness research and why it deserves more rigorous and high-powered replication attempts.

Characterising the covariance pattern between lifestyle factors and structural brain measures: a multivariable replication study of two independent ageing cohorts.

Modifiable lifestyle factors have been shown to promote healthy brain ageing. However, studies have typically focused on a single factor at a time. Given that lifestyle factors do not occur in isolation, multivariable analyses provide a more realistic model of the lifestyle-brain relationship. Here, canonical correlation analyses (CCA) examined the relationship between nine lifestyle factors and seven MRI-derived indices of brain structure. The resulting covariance pattern was further explored with Bayesian regressions. CCA analyses were first conducted on a Danish cohort of older adults (n = 251) and then replicated in a British cohort (n = 668). In both cohorts, the latent factors of lifestyle and brain structure were positively correlated (UK: r = .37, p < 0.001; Denmark: r = .27, p < 0.001). In the cross-validation study, the correlation between lifestyle-brain latent factors was r = .10, p = 0.008. However, the pattern of associations differed between datasets. These findings suggest that baseline characterisation and tailoring towards the study sample may be beneficial for achieving targeted lifestyle interventions.

The Danish High-Risk and Resilience Study-VIA 15 - A Study Protocol for the Third Clinical Assessment of a Cohort of 522 Children Born to Parents Diagnosed With Schizophrenia or Bipolar Disorder and Population-Based Controls.

Background: Children born to parents with severe mental illness have gained more attention during the last decades because of increasing evidence documenting that these children constitute a population with an increased risk of developing mental illness and other negative life outcomes. Because of high-quality research with cohorts of offspring with familial risk and increased knowledge about gene-environment interactions, early interventions and preventive strategies are now being developed all over the world. Adolescence is a period characterized by massive changes, both in terms of physical, neurologic, psychological, social, and behavioral aspects. It is also the period of life with the highest risk of experiencing onset of a mental disorder. Therefore, investigating the impact of various risk and resilience factors in adolescence is important. Methods: The Danish High-Risk and Resilience Study started data collection in 2012, where 522 7-year-old children were enrolled in the first wave of the study, the VIA 7 study. The cohort was identified through Danish registers based on diagnoses of the parents. A total of 202 children had a parent diagnosed with schizophrenia, 120 children had a parent diagnosed with bipolar disorder, and 200 children had parents without these diagnoses. At age 11 years, all children were assessed for the second time in the VIA 11 study, with a follow-up retention rate of 89%. A comprehensive assessment battery covering domains of psychopathology, neurocognition, social cognition and behavior, motor development and physical health, genetic analyses, attachment, stress, parental functioning, and home environment was carried out at each wave. Magnetic resonance imaging scans of the brain and electroencephalograms were included from age 11 years. This study protocol describes the third wave of assessment, the VIA 15 study, participants being 15 years of age and the full, 3-day-long assessment battery this time including also risk behavior, magnetoencephalography, sleep, and a white noise paradigm. Data collection started on May 1, 2021. Discussion: We will discuss the importance of longitudinal studies and cross-sectional data collection and how studies like this may inform us about unmet needs and windows of opportunity for future preventive interventions, early illness identification, and treatment in the future.

Choice history effects in mice and humans improve reward harvesting efficiency.

Choice history effects describe how future choices depend on the history of past choices. In experimental tasks this is typically framed as a bias because it often diminishes the experienced reward rates. However, in natural habitats, choices made in the past constrain choices that can be made in the future. For foraging animals, the probability of earning a reward in a given patch depends on the degree to which the animals have exploited the patch in the past. One problem with many experimental tasks that show choice history effects is that such tasks artificially decouple choice history from its consequences on reward availability over time. To circumvent this, we use a variable interval (VI) reward schedule that reinstates a more natural contingency between past choices and future reward availability. By examining the behavior of optimal agents in the VI task we discover that choice history effects observed in animals serve to maximize reward harvesting efficiency. We further distil the function of choice history effects by manipulating first- and second-order statistics of the environment. We find that choice history effects primarily reflect the growth rate of the reward probability of the unchosen option, whereas reward history effects primarily reflect environmental volatility. Based on observed choice history effects in animals, we develop a reinforcement learning model that explicitly incorporates choice history over multiple time scales into the decision process, and we assess its predictive adequacy in accounting for the associated behavior. We show that this new variant, known as the double trace model, has a higher performance in predicting choice data, and shows near optimal reward harvesting efficiency in simulated environments. These results suggests that choice history effects may be adaptive for natural contingencies between consumption and reward availability. This concept lends credence to a normative account of choice history effects that extends beyond its description as a bias.

Ergodicity-breaking reveals time optimal decision making in humans.

Ergodicity describes an equivalence between the expectation value and the time average of observables. Applied to human behaviour, ergodic theories of decision-making reveal how individuals should tolerate risk in different environments. To optimize wealth over time, agents should adapt their utility function according to the dynamical setting they face. Linear utility is optimal for additive dynamics, whereas logarithmic utility is optimal for multiplicative dynamics. Whether humans approximate time optimal behavior across different dynamics is unknown. Here we compare the effects of additive versus multiplicative gamble dynamics on risky choice. We show that utility functions are modulated by gamble dynamics in ways not explained by prevailing decision theories. Instead, as predicted by time optimality, risk aversion increases under multiplicative dynamics, distributing close to the values that maximize the time average growth of in-game wealth. We suggest that our findings motivate a need for explicitly grounding theories of decision-making on ergodic considerations.

Reward signalling in brainstem nuclei under fluctuating blood glucose.

Phasic dopamine release from mid-brain dopaminergic neurons is thought to signal errors of reward prediction (RPE). If reward maximisation is to maintain homeostasis, then the value of primary rewards should be coupled to the homeostatic errors they remediate. This leads to the prediction that RPE signals should be configured as a function of homeostatic state and thus diminish with the attenuation of homeostatic error. To test this hypothesis, we collected a large volume of functional MRI data from five human volunteers on four separate days. After fasting for 12 hours, subjects consumed preloads that differed in glucose concentration. Participants then underwent a Pavlovian cue-conditioning paradigm in which the colour of a fixation-cross was stochastically associated with the delivery of water or glucose via a gustometer. This design afforded computation of RPE separately for better- and worse-than expected outcomes during ascending and descending trajectories of serum glucose fluctuations. In the parabrachial nuclei, regional activity coding positive RPEs scaled positively with serum glucose for both ascending and descending glucose levels. The ventral tegmental area and substantia nigra became more sensitive to negative RPEs when glucose levels were ascending. Together, the results suggest that RPE signals in key brainstem structures are modulated by homeostatic trajectories of naturally occurring glycaemic flux, revealing a tight interplay between homeostatic state and the neural encoding of primary reward in the human brain.

A Bayesian reanalysis of the effects of hydroxychloroquine and azithromycin on viral carriage in patients with COVID-19.

Gautret and colleagues reported the results of a non-randomised case series which examined the effects of hydroxychloroquine and azithromycin on viral load in the upper respiratory tract of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients. The authors reported that hydroxychloroquine (HCQ) had significant virus reducing effects, and that dual treatment of both HCQ and azithromycin further enhanced virus reduction. In light of criticisms regarding how patients were excluded from analyses, we reanalysed the original data to interrogate the main claims of the paper. We applied Bayesian statistics to assess the robustness of the original paper's claims by testing four variants of the data: 1) The original data; 2) Data including patients who deteriorated; 3) Data including patients who deteriorated with exclusion of untested patients in the comparison group; 4) Data that includes patients who deteriorated with the assumption that untested patients were negative. To ask if HCQ monotherapy was effective, we performed an A/B test for a model which assumes a positive effect, compared to a model of no effect. We found that the statistical evidence was highly sensitive to these data variants. Statistical evidence for the positive effect model ranged from strong for the original data (BF+0 ~11), to moderate when including patients who deteriorated (BF+0 ~4.35), to anecdotal when excluding untested patients (BF+0 ~2), and to anecdotal negative evidence if untested patients were assumed positive (BF+0 ~0.6). The fact that the patient inclusions and exclusions are not well justified nor adequately reported raises substantial uncertainty about the interpretation of the evidence obtained from the original paper.

Effective immunity and second waves: a dynamic causal modelling study.

This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a dynamic causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.

Dopamine agonist treatment increases sensitivity to gamble outcomes in the hippocampus in de novo Parkinson's disease.

BACKGROUND: Parkinson's disease is associated with severe nigro-striatal dopamine depletion, leading to motor dysfunction and altered reward processing. We previously showed that drug-naïve patients with Parkinson's disease had a consistent attenuation of reward signalling in the mesolimbic and mesocortical system. Here, we address the neurobiological effects of dopaminergic therapy on reward sensitivity in the mesolimbic circuitry, and how this may contribute to neuropsychiatric symptoms. OBJECTIVES: We tested the hypothesis that (1) dopaminergic treatment would restore the attenuated, mesolimbic and mesocortical responses to reward; and (2) restoration of reward responsivity by dopaminergic treatment would predict motor performance and the emergence of impulse control symptoms. METHODS: In 11 drug-naïve Parkinson patients, we prospectively assessed treatment-induced changes in reward processing before, and eight weeks after initiation of monotherapy with dopamine agonists. They were compared to 10 non-medicated healthy controls who were also measured longitudinally. We used whole-brain functional magnetic resonance imaging at 3 Tesla to assess the reward responsivity of the brain to monetary gains and losses, while participants performed a simple consequential gambling task. RESULTS: In patients, dopaminergic treatment improved clinical motor symptoms without significantly changing task performance. Dopamine agonist therapy induced a stronger reward responsivity in the right hippocampus with higher doses being less effective. None of the patients developed impulse control disorders in the follow-up period of four years. CONCLUSIONS: Short-term treatment with first-ever dopaminergic medication partially restores deficient reward-related processing in the hippocampus in de novo Parkinson's disease.

Asymptotic estimates of SARS-CoV-2 infection counts and their sensitivity to stochastic perturbation.

Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data from different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data point before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a susceptible-exposed-infected-recovered model, where the parameters are stochastically perturbed to simulate the difficulty in detecting patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.

Second waves, social distancing, and the spread of COVID-19 across the USA.

We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of viral spread among regions. Our focus is on a second wave of new cases that may result from loss of immunity-and the exchange of people between regions-and how mortality rates can be ameliorated under different strategic responses. In particular, we consider hard or soft social distancing strategies predicated on national (Federal) or regional (State) estimates of the prevalence of infection in the population. The modelling is demonstrated using timeseries of new cases and deaths from the United States to estimate the parameters of a factorial (compartmental) epidemiological model of each State and, crucially, coupling between States. Using Bayesian model reduction, we identify the effective connectivity between States that best explains the initial phases of the outbreak in the United States. Using the ensuing posterior parameter estimates, we then evaluate the likely outcomes of different policies in terms of mortality, working days lost due to lockdown and demands upon critical care. The provisional results of this modelling suggest that social distancing and loss of immunity are the two key factors that underwrite a return to endemic equilibrium.

Neurocomputational theories of homeostatic control.

Homeostasis is a problem for all living agents. It entails predictively regulating internal states within the bounds compatible with survival in order to maximise fitness. This can be achieved physiologically, through complex hierarchies of autonomic regulation, but it must also be achieved via behavioural control, both reactive and proactive. Here we briefly review some of the major theories of homeostatic control and their historical cognates, addressing how they tackle the optimisation of both physiological and behavioural homeostasis. We start with optimal control approaches, setting up key concepts, exploring their strengths and limitations. We then concentrate on contemporary neurocomputational approaches to homeostatic control. We primarily focus on a branch of reinforcement learning known as homeostatic reinforcement learning (HRL). A central premise of HRL is that reward optimisation is directly coupled to homeostatic control. A central construct in this framework is the drive function which maps from homeostatic state to motivational drive, where reductions in drive are operationally defined as reward values. We explain HRL's main advantages, empirical applications, and conceptual insights. Notably, we show how simple constraints on the drive function can yield a normative account of predictive control, as well as account for phenomena such as satiety, risk aversion, and interactions between competing homeostatic needs. We illustrate how HRL agents can learn to avoid hazardous states without any need to experience them, and how HRL can be applied in clinical domains. Finally, we outline several challenges to HRL, and how survival constraints and active inference models could circumvent these problems.

Extending models of "How Foraging Works": Uncertainty, controllability, and survivability.

We argue that How Foraging Works sketches a good foundational model, but it needs expanding to incorporate hierarchical and multiscale conceptions of uncertainty and to incorporate inference of environmental controllability. Most pressingly, its algorithmic implementation needs to be better justified in terms of its functional forms and, ultimately, to be more heavily constrained by survival optimality.

Patient profiling for success after weight loss surgery (GO Bypass study): An interdisciplinary study protocol.

Despite substantial research efforts, the mechanisms proposed to explain weight loss after gastric bypass (RYGB) and sleeve gastrectomy (SL) do not explain the large individual variation seen after these treatments. A complex set of factors are involved in the onset and development of obesity and these may also be relevant for the understanding of why success with treatments vary considerably between individuals. This calls for explanatory models that take into account not only biological determinants but also behavioral, affective and contextual factors. In this prospective study, we recruited 47 women and 8 men, aged 25-56 years old, with a BMI of 45.8 ±â€¯7.1 kg/m2 from the waiting list for RYGB and SL at Køge hospital, Denmark. Pre-surgery and 1.5, 6 and 18 months after surgery we assessed various endpoints spanning multiple domains. Endpoints were selected on basis of previous studies and include: physiological measures: anthropometrics, vital signs, biochemical measures and appetite hormones, genetics, gut microbiota, appetite sensation, food and taste preferences, neural sensitivity, sensory perception and movement behaviors; psychological measures: general psychiatric symptom-load, depression, eating disorders, ADHD, personality disorder, impulsivity, emotion regulation, attachment pattern, general self-efficacy, alexithymia, internalization of weight bias, addiction, quality of life and trauma; and sociological and anthropological measures: sociodemographic measures, eating behavior, weight control practices and psycho-social factors.Joining these many endpoints and methodologies from different scientific disciplines and creating a multi-dimensional predictive model has not previously been attempted. Data on the primary endpoint are expected to be published in 2018. TRIAL REGISTRATION: Clinicaltrials. gov ID NCT02070081.

Altered auditory processing and effective connectivity in 22q11.2 deletion syndrome.

22q11.2 deletion syndrome (22q11.2DS) is one of the most common copy number variants and confers a markedly increased risk for schizophrenia. As such, 22q11.2DS is a homogeneous genetic liability model which enables studies to delineate functional abnormalities that may precede disease onset. Mismatch negativity (MMN), a brain marker of change detection, is reduced in people with schizophrenia compared to healthy controls. Using dynamic causal modelling (DCM), previous studies showed that top-down effective connectivity linking the frontal and temporal cortex is reduced in schizophrenia relative to healthy controls in MMN tasks. In the search for early risk-markers for schizophrenia we investigated the neural basis of change detection in a group with 22q11.2DS. We recorded high-density EEG from 19 young non-psychotic 22q11.2 deletion carriers, as well as from 27 healthy non-carriers with comparable age distribution and sex ratio, while they listened to a sequence of sounds arranged in a roving oddball paradigm. Despite finding no significant reduction in the MMN responses, whole-scalp spatiotemporal analysis of responses to the tones revealed a greater fronto-temporal N1 component in the 22q11.2 deletion carriers. DCM showed reduced intrinsic connection within right primary auditory cortex as well as in the top-down, connection from the right inferior frontal gyrus to right superior temporal gyrus for 22q11.2 deletion carriers although not surviving correction for multiple comparison. We discuss these findings in terms of reduced adaptation and a general increased sensitivity to tones in 22q11.2DS.

Fairness, fast and slow: A review of dual process models of fairness.

Fairness, the notion that people deserve or have rights to certain resources or kinds of treatment, is a fundamental dimension of moral cognition. Drawing on recent evidence from economics, psychology, and neuroscience, we ask whether self-interest is always intuitive, requiring self-control to override with reasoning-based fairness concerns, or whether fairness itself can be intuitive. While we find strong support for rejecting the notion that self-interest is always intuitive, the literature has reached conflicting conclusions about the neurocognitive systems underpinning fairness. We propose that this disagreement can largely be resolved in light of an extended Social Heuristics Hypothesis. Divergent findings may be attributed to the interpretation of behavioral effects of ego depletion or neurostimulation, reverse inference from brain activity to the underlying psychological process, and insensitivity to social context and inter-individual differences. To better dissect the neurobiological basis of fairness, we outline how future research should embrace cross-disciplinary methods that combine psychological manipulations with neuroimaging, and that can probe inter-individual, and cultural heterogeneities.

Simultaneous representation of a spectrum of dynamically changing value estimates during decision making.

Decisions are based on value expectations derived from experience. We show that dorsal anterior cingulate cortex and three other brain regions hold multiple representations of choice value based on different timescales of experience organized in terms of systematic gradients across the cortex. Some parts of each area represent value estimates based on recent reward experience while others represent value estimates based on experience over the longer term. The value estimates within these areas interact with one another according to their temporal scaling. Some aspects of the representations change dynamically as the environment changes. The spectrum of value estimates may act as a flexible selection mechanism for combining experience-derived value information with other aspects of value to allow flexible and adaptive decisions in changing environments.

Imaging the Creative Unconscious: Reflexive Neural Responses to Objects in the Visual and Parahippocampal Region Predicts State and Trait Creativity.

What does it take to have a creative mind? Theories of creative cognition assert that the quantity of automatic associations places fundamental constraints on the probability of reaching creative solutions. Due to the difficulties inherent in isolating automated associative responses from cognitive control, the neural basis underlying this faculty remains unknown. Here we acquired fMRI data in an incidental-viewing paradigm in which subjects performed an attention-demanding task whilst viewing task-irrelevant objects. By assigning a standard creativity task on the same objects out of the scanner, as well as a battery of psychometric creativity tests, we could assess whether stimulus-bound neural activity was predictive of state or trait variability in creativity. We found that stimulus-bound responses in superior occipital regions were linearly predictive of trial-by-trial variability in creative performance (state-creativity), and that in more creative individuals (trait-creativity) this response was more strongly expressed in entorhinal cortex. Additionally, the mean response to the onset of objects in parahippocampal gyrus was predictive of trait differences in creativity. This work suggests that, creative individuals are endowed with occipital and medial temporal reflexes that generate a greater fluency in associative representations, making them more accessible for ideation even when no ideation is explicitly called for.