It has been claimed and demonstrated that many (and possibly most) of the conclusions drawn from biomedi-cal research are probably false 1. A central cause for this important problem is that researchers must publish in order to succeed, and publishing is a highly competitive enterprise, with certain kinds of findings more likely to be published than others. Research that produces novel results, statistically significant results (that is, typically p < 0.05) and seemingly 'clean' results is more likely to be published 2,3. As a consequence, researchers have strong incentives to engage in research practices that make their findings publishable quickly, even if those practices reduce the likelihood that the findings reflect a true (that is, non-null) effect 4. Such practices include using flexible study designs and flexible statistical analyses and running small studies with low statistical power 1,5. A simulation of genetic association studies showed that a typical dataset would generate at least one false positive result almost 97% of the time 6 , and two efforts to replicate promising findings in biomedicine reveal replication rates of 25% or less 7,8. Given that these publishing biases are pervasive across scientific practice, it is possible that false positives heavily contaminate the neuroscience literature as well, and this problem may affect at least as much, if not even more so, the most prominent journals 9,10. Here, we focus on one major aspect of the problem: low statistical power. The relationship between study power and the veracity of the resulting finding is under-appreciated. Low statistical power (because of low sample size of studies, small effects or both) negatively affects the likelihood that a nominally statistically significant finding actually reflects a true effect. We discuss the problems that arise when low-powered research designs are pervasive. In general, these problems can be divided into two categories. The first concerns problems that are mathematically expected to arise even if the research conducted is otherwise perfect: in other words, when there are no biases that tend to create statistically significant (that is, 'positive') results that are spurious. The second category concerns problems that reflect biases that tend to co-occur with studies of low power or that become worse in small, underpowered studies. We next empirically show that statistical power is typically low in the field of neuroscience by using evidence from a range of subfields within the neuroscience literature. We illustrate that low statistical power is an endemic problem in neuroscience and discuss the implications of this for interpreting the results of individual studies. Low power in the absence of other biases Three main problems contribute to producing unreliable findings in studies with low power, even when all other research practices are ideal. They are: the low probability of finding true effects; the low positive predictive value (PPV; see BOX 1 for definitions of key statistical terms) when an eff...

In an increasing number of states and countries, cannabis now stands poised to join alcohol and tobacco as a legal drug. Quantifying the relative adverse and beneficial effects of cannabis and its constituent cannabinoids should therefore be prioritized. Whereas newspaper headlines have focused on links between cannabis and psychosis, less attention has been paid to the much more common problem of cannabis addiction. Certain cognitive changes have also been attributed to cannabis use, although their causality and longevity are fiercely debated. Identifying why some individuals are more vulnerable than others to the adverse effects of cannabis is now of paramount importance to public health. Here, we review the current state of knowledge about such vulnerability factors, the variations in types of cannabis, and the relationship between these and cognition and addiction.

RationaleAnecdotally, both acute and chronic cannabis use have been associated with apathy, amotivation, and other reward processing deficits. To date, empirical support for these effects is limited, and no previous studies have assessed both acute effects of Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), as well as associations with cannabis dependence.ObjectivesThe objectives of this study were (1) to examine acute effects of cannabis with CBD (Cann + CBD) and without CBD (Cann-CBD) on effort-related decision-making and (2) to examine associations between cannabis dependence, effort-related decision-making and reward learning.MethodsIn study 1, 17 participants each received three acute vaporized treatments, namely Cann-CBD (8 mg THC), Cann + CBD (8 mg THC + 10 mg CBD) and matched placebo, followed by a 50 % dose top-up 1.5 h later, and completed the Effort Expenditure for Rewards Task (EEfRT). In study 2, 20 cannabis-dependent participants were compared with 20 non-dependent, drug-using control participants on the EEfRT and the Probabilistic Reward Task (PRT) in a non-intoxicated state.ResultsCann-CBD reduced the likelihood of high-effort choices relative to placebo (p = 0.042) and increased sensitivity to expected value compared to both placebo (p = 0.014) and Cann + CBD (p = 0.006). The cannabis-dependent and control groups did not differ on the EEfRT. However, the cannabis-dependent group exhibited a weaker response bias than the control group on the PRT (p = 0.007).ConclusionsCannabis acutely induced a transient amotivational state and CBD influenced the effects of THC on expected value. In contrast, cannabis dependence was associated with preserved motivation alongside impaired reward learning, although confounding factors, including depression, cannot be disregarded. This is the first well powered, fully controlled study to objectively demonstrate the acute amotivational effects of THC.Electronic supplementary materialThe online version of this article (doi:10.1007/s00213-016-4383-x) contains supplementary material, which is available to authorized users.

Background:People with Down syndrome (DS) are an ultra-high risk population for Alzheimer’s disease (AD). Understanding the factors associated with age of onset and survival in this population could highlight factors associated with modulation of the amyloid cascade.Objective:This study aimed to establish the typical age at diagnosis and survival associated with AD in DS and the risk factors associated with these.Methods:Data was obtained from the Aging with Down Syndrome and Intellectual Disabilities (ADSID) research database, consisting of data extracted from clinical records of patients seen by Community Intellectual Disability Services (CIDS) in England. Survival times when considering different risk factors were calculated.Results:The mean age of diagnosis was 55.80 years, SD 6.29. Median survival time after diagnosis was 3.78 years, and median age at death was approximately 60 years. Survival time was associated with age of diagnosis, severity of intellectual disability, living status, anti-dementia medication status, and history of epilepsy. Age at diagnosis and treatment status remained predictive of survival time following adjustment.Conclusion:This study provides the best estimate of survival in dementia within the DS population to date, and is in keeping with previous estimates from smaller studies in the DS population. This study provides important estimates and insights into possible predictors of survival and age of diagnosis of AD in adults with DS, which will inform selection of participants for treatment trials in the future.

The recent liberalisation of cannabis regulation has increased public and scientific debate about its potential benefits and risks. A key focus has been the extent to which cannabidiol (CBD) might influence the acute effects of delta-9-tetrahydrocannabinol (THC), but this has never been reviewed systematically. In this systematic review of how CBD influences the acute effects of THC we identified 16 studies involving 466 participants. Ten studies were judged at low risk of bias. The findings were mixed, although CBD was found to reduce the effects of THC in several studies. Some studies found that CBD reduced intense experiences of anxiety or psychosis-like effects of THC and blunted some of the impairments on emotion and reward processing. However, CBD did not consistently influence the effects of THC across all studies and outcomes. There was considerable heterogeneity in dose, route of administration and THC:CBD ratio across studies and no clear dose-response profile emerged. Although findings were mixed, this review suggests that CBD may interact with some acute effects of THC.