Search

Psychological therapies are among the most effective treatments for a range of common mental health problems – however, we still know relatively little about how exactly they improve symptoms. Here, we demonstrate the power of combing theory with computational methods to parse effects of different components of cognitive-behavioural therapies on to underlying mechanisms. Specifically, we present data from a series of randomized-controlled experiments testing the effects of components of behavioural and cognitive therapies on different cognitive processes, using well-validated behavioural measures and associated computational models (total N=807). We found that a goal-setting intervention, based on behavioural activation therapy, reliably and selectively reduced sensitivity to effort when deciding how to act to gain reward. By contrast, we found that a cognitive restructuring intervention, based on cognitive therapy, reliably and selectively reduced the tendency to attribute negative everyday events to self-related causes. Importantly, the effects of each intervention were specific to these respective measures. Our approach provides a basis for understanding how different elements of common psychotherapy programs work, which may enable theoretically-informed treatment targeting in the future.

Promising initial data indicate that the glutamate N-methyl-D-aspartate (NMDA) receptor antagonist ketamine may be beneficial in post-traumatic stress disorder (PTSD). Here, we explore the neural correlates of ketamine-related changes in PTSD symptoms, using a rich battery of functional imaging data (two emotion-processing tasks and one task-free scan), collected from a subset of participants of a randomized clinical trial of repeated-dose intravenous ketamine vs midazolam (total N=21). In a pre-registered analysis, we tested whether changes in an a priori set of imaging measures from a target neural circuit were predictive of improvement in PTSD symptoms, using leave-one-out cross-validated elastic-net regression models (regions of interest in the target circuit consisted of the dorsal and rostral anterior cingulate cortex, ventromedial prefrontal cortex, anterior hippocampus, anterior insula, and amygdala). Improvements in PTSD severity were associated with increased functional connectivity between the ventromedial prefrontal cortex (vmPFC) and amygdala during emotional face-viewing (change score retained in model with minimum predictive error in left-out subjects, standardized regression coefficient [β]=2.90). This effect was stronger in participants who received ketamine compared to midazolam (interaction β=0.86), and persisted following inclusion of concomitant change in depressive symptoms in the analysis model (β=0.69). Improvement following ketamine was also predicted by decreased dorsal anterior cingulate activity during emotional conflict regulation, and increased task-free connectivity between the vmPFC and anterior insula (βs=-2.82, 0.60). Exploratory follow-up analysis via dynamic causal modelling revealed that whilst improvement in PTSD symptoms following either drug was associated with decreased excitatory modulation of amygdala→vmPFC connectivity during emotional face-viewing, increased top-down inhibition of the amygdala by the vmPFC was only observed in participants who improved under ketamine. Individuals with low prefrontal inhibition of amygdala responses to faces at baseline also showed greater improvements following ketamine treatment. These preliminary findings suggest that, specifically under ketamine, improvements in PTSD symptoms are accompanied by normalization of hypofrontal control over amygdala responses to social signals of threat.

BACKGROUND Anxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored. OBJECTIVE We aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up. METHODS The individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning–based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status. RESULTS Individual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group. CONCLUSIONS Patients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers—passive-sensing of shifts in category-based social media app usage during the lockdown—can identify individuals at risk for psychiatric sequelae.

BackgroundProblems in learning that sights, sounds, or situations that were once associated with danger have become safe (extinction learning) may explain why some individuals suffer prolonged psychological distress following traumatic experiences. Although simple learning models have been unable to provide a convincing account of why this learning fails, it has recently been proposed that this may be explained by individual differences in beliefs about the causal structure of the environment.MethodsHere, we tested two competing hypotheses as to how differences in causal inference might be related to trauma-related psychopathology, using extinction learning data collected from clinically well-characterised individuals with varying degrees of post-traumatic stress (N = 56). Model parameters describing individual differences in causal inference were related to multiple post-traumatic stress disorder (PTSD) and depression symptom dimensions via network analysis.ResultsIndividuals with more severe PTSD were more likely to assign observations from conditioning and extinction stages to a single underlying cause. Specifically, greater re-experiencing symptom severity was associated with a lower likelihood of inferring that multiple causes were active in the environment.ConclusionsWe interpret these results as providing evidence of a primary deficit in discriminative learning in participants with more severe PTSD. Specifically, a tendency to attribute a greater diversity of stimulus configurations to the same underlying cause resulted in greater uncertainty about stimulus-outcome associations, impeding learning both that certain stimuli were safe, and that certain stimuli were no longer dangerous. In the future, better understanding of the role of causal inference in trauma-related psychopathology may help refine cognitive therapies for these disorders.

IntroductionWe conducted an ecological study to determine if state-level healthcare access is associated with trajectories of daily reported COVID-19 cases in the United States. Our focus is on trajectories of daily reported COVID-19 cases, rather than cumulative cases, as trajectories help us identify trends in how the pandemic naturally develops over time, and study the shapes of the curve in different states.MethodsWe analyzed data on daily reported confirmed and probable COVID-19 cases from January 21 to June 16, 2020 in 50 states, adjusted for the population size of each state. Cluster analysis for time-series data was used to split the states into clusters that have distinct trajectories of daily cases. Differences in socio-demographic characteristics and healthcare access between clusters were tested. Adjusted models were used to determine if healthcare access is associated with reporting a high trajectory of COVID-19 cases.ResultsTwo clusters of states were identified. One cluster had a high trajectory of population- adjusted COVID-19 cases, and comprised of 19 states, including New York and New Jersey. The other cluster of states (n=31) had a low trajectory of population-adjusted COVID-19 cases.There were significantly more Black residents (p=0.027) and more nursing facility residents (p=0.001) in states reporting high trajectory of COVID-19 cases. States reporting a high trajectory of COVID-19 cases also had fewer uninsured persons (p=0.005), fewer persons who reported having to forgo medical care due to cost (p=0.016), more registered physicians (p=0.002) and more nurses (p=0.03), higher health spending per capita (p=0.01), fewer residents in Health Professional Shortage Areas per 100,000 population (p=0.027), and higher adoption of Medicaid Expansion (p=0.05).In adjusted models, a higher proportion of uninsured persons (OR: 0.51 [0.25-0.85]; p=0.032), higher proportion of patients who had to forgo medical care due to cost (OR: 0.55 [0.28-0.95]; p=0.048), and no adoption of Medicaid expansion (OR: 0.05 [0 – 0.59]; p=0.04), were associated with reporting a low trajectory of COVID-19 cases.ConclusionOur findings from adjusted models suggest that healthcare access can partially explain variations in COVID-19 case trajectories by state.

ObjectiveTo identify factors associated with local variation in the time course of COVID-19 case burden in England.MethodsWe analyzed laboratory-confirmed COVID-19 case data for 150 upper tier local authorities, from the period from January 30 to May 6, 2020, as reported by Public Health England. Using methods suitable for time-series data, we identified clusters of local authorities with distinct trajectories of daily cases, after adjusting for population size. We then tested for differences in sociodemographic, economic, and health disparity factors between these clusters.ResultsTwo clusters of local authorities were identified: a higher case trajectory that rose faster over time to reach higher peak infection levels, and a lower case trajectory cluster that emerged more slowly, and had a lower peak. The higher case trajectory cluster (79 local authorities) had higher population density (pConclusionAreas belonging to the trajectory with significantly higher COVID-19 case burden were more deprived, and had higher proportions of ethnic minority residents. A higher proportion of Black residents in regions belonging to the high trajectory cluster was not fully explained by differences in population density, deprivation, and other overall health disparities between the clusters.What is already known on this subject?Emerging evidence suggests that the burden of COVID-19 infection is falling unequally across England, with provisional data suggesting higher overall infection and mortality rates for Black, Asian, and mixed race/ethnicity individuals.What does this study add?We found that regions with greater socioeconomic deprivation and poorer population health measures showed a faster rise in COVID-19 cases, and reached higher peak case levels. Areas with a higher proportion of Black residents were more likely to show this kind of time course, even after adjusting for multiple co-occurring factors, including population density. This finding merits further investigation in terms of the intersecting vulnerability factors Black and other minority ethnic individuals face in England (e.g. proportion of people working in service and caring roles, and the role of structural discrimination), and has implications for the ongoing allocation of public health resources, in order to better mitigate such inequalities.

Insight into motivational processes may be gained by examining measures of willingness to exert effort for rewards, which have been linked to neuropsychiatric symptoms of anhedonia and apathy. However, while much work has focused on the development of models of motivation based on classic tasks of externally-generated levels of effort for reward, there has been less focus on the question of self-generated motivation or volition. We developed a task that aims to capture separate measures of self-generated and externally-generated motivation, with two task variants for physical and cognitive effort, and sought to test and validate this measure in two populations of healthy volunteers (N = 27 and N = 28). Similar to previous reports, a sigmoid function represented a better overall fit to the effort-reward data than a linear or Weibull model. Individual sigmoid function shapes were governed by two free parameters: bias (the amount of reward needed for effort initiation) and reward insensitivity (the amount of increase in reward needed to accelerate effort expenditure). For both physical and cognitive effort, bias was higher in the self-generated condition, indicating reduced self-generated volitional effort initiation, compared to externally-generated effort initiation, across effort domains. Bias against initial effort initiation in the self-generated condition was related to a specific dimensional measure of anticipatory anhedonia. For physical effort only, reward insensitivity was also higher in the self-generated condition compared to the externally-generated motivation condition, indicating lower self-generated effort acceleration. This work provides a novel objective measure of self-generated motivation that may provide insights into mechanisms of anhedonia and related symptoms.

Resilience, or the phenomenon of successful adaptation following significant trauma exposure, is a complex, multidimensional, and dynamic process. To date, research on neural mechanisms involved in human resilience has comprised of two major research streams – involving individuals with childhood and adulthood trauma exposure, respectively. Although there are systematic differences in how both trauma and resilience have been defined across these two bodies of research, some striking regions of convergence emerge when considering the literature as a whole. Here, we review functional imaging studies from across these two research streams, alongside discussion of some of the methodological difficulties involved in quantifying both trauma and resilience in human participants. Due to the broad scope of this literature, we restrict the scope of our narrative to several key domains where studies from across these two bodies of work implicate common neural circuitry. These areas of convergence include brain networks implicated in emotion regulation, responses to rewards, and cognitive control. Further, we briefly review functional imaging evidence related to proposed mechanisms underlying resilient outcomes: namely active coping, cognitive reappraisal and successful fear extinction. Finally, we also touch upon several ongoing issues in neuroimaging study design and analysis that will need to be addressed in order to enable us to harness insight from such studies to improve treatments for – or, ideally, guard against the development of – debilitating post-traumatic stress syndromes.

Sensation-seeking (SS) is a personality trait that refers to individual differences in motivation for intense and unusual sensory experiences. It describes a facet of human behaviour that has direct relevance for several psychopathologies associated with high social cost. Here, we first review ways of measuring SS behaviour in both humans and animals. We then present convergent evidence that implicates dopaminergic neurotransmission (particularly via D2-type receptors) in individual differences in SS trait. Both high tonic dopamine levels and hyper-reactive midbrain dopaminergic responses to signals of forthcoming reward are evident in higher sensations-seekers. We propose that differences in the efficacy of striatal dopaminergic transmission may result in differential expression of approach-avoidance reactions to same intensity stimuli. This constitutes a quantitative trait of intensity preference for sensory stimulation that may underlie core features of the SS personality. We review the evidence that high trait SS is a vulnerability factor for psychopathologies related to changes in brain dopamine function, in particular substance and gambling addictions. Conversely, we consider the possibility that increased tolerance of high intensity stimulation may represent a protective mechanism against the development of trauma-related psychopathologies (e.g. post-traumatic stress disorder) in high sensation-seeking individuals. Further understanding of the brain mechanisms underlying SS trait might not only to shed light on the aetiology of these disorders, but also aid in developing individualised therapies and prevention strategies for psychopathologies.

Capacity limitations in working memory (WM) necessitate the need to effectively control its contents. Here, we examined the effect of cabergoline, a dopamine D2 receptor agonist, on WM using a continuous report paradigm that allowed us to assess the fidelity with which items are stored. We assessed recall performance under three different gating conditions: remembering only one item, being cued to remember one target among distractors, and having to remember all items. Cabergoline had differential effects on recall performance according to whether distractors had to be ignored and whether mnemonic resources could be deployed exclusively to the target. Compared with placebo, cabergoline improved mnemonic performance when there were no distractors but significantly reduced performance when distractors were presented in a precue condition. No significant difference in performance was observed under cabergoline when all items had to be remembered. By applying a stochastic model of response selection, we established that the causes of drug-induced changes in performance were due to changes in the precision with which items were stored in WM. However, there was no change in the extent to which distractors were mistaken for targets. Thus, D2 agonism causes changes in the fidelity of mnemonic representations without altering interference between memoranda.

Response rates to available treatments for psychological and chronic pain disorders are poor, and there is a considerable burden of suffering and disability for patients, who often cycle through several rounds of ineffective treatment. As individuals presenting to the clinic with symptoms of these disorders are likely to be heterogeneous, there is considerable interest in the possibility that different constellations of signs could be used to identify subgroups of patients that might preferentially benefit from particular kinds of treatment. To this end, there has been a recent focus on the application of machine learning methods to attempt to identify sets of predictor variables (demographic, genetic, etc.) that could be used to target individuals towards treatments that are more likely to work for them in the first instance. Importantly, the training of such models generally relies on datasets where groups of individual predictor variables are labelled with a binary outcome category − usually ‘responder’ or ‘non-responder’ (to a particular treatment). However, as previously highlighted in other areas of medicine, there is a basic statistical problem in classifying individuals as ‘responding’ to a particular treatment on the basis of data from conventional randomized controlled trials. Specifically, insufficient information on the partition of variance components in individual symptom changes mean that it is inappropriate to consider data from the active treatment arm alone in this way. This may be particularly problematic in the case of psychiatric and chronic pain symptom data, where both within-subject variability and measurement error are likely to be high. Here, we outline some possible solutions to this problem in terms of dataset design and machine learning methodology, and conclude that it is important to carefully consider the kind of inferences that particular training data are able to afford, especially in arenas where the potential clinical benefit is so large.

Trait sensation-seeking, defined as a need for varied, complex, and intense sensations, represents a relatively underexplored hedonic drive in human behavioral neuroscience research. It is related to increased risk for a range of behaviors including substance use, gambling, and risky sexual practice. Individual differences in self-reported sensation-seeking have been linked to brain dopamine function, particularly at D2-like receptors, but so far no causal evidence exists for a role of dopamine in sensation-seeking behavior in humans. Here, we investigated the effects of the selective D2/D3 agonist cabergoline on performance of a probabilistic risky choice task in healthy humans using a sensitive within-subject, placebo-controlled design. Cabergoline significantly influenced the way participants combined different explicit signals regarding probability and loss when choosing between response options associated with uncertain outcomes. Importantly, these effects were strongly dependent on baseline sensation-seeking score. Overall, cabergoline increased sensitivity of choice to information about probability of winning; while decreasing discrimination according to magnitude of potential losses associated with different options. The largest effects of the drug were observed in participants with lower sensation-seeking scores. These findings provide evidence that risk-taking behavior in humans can be directly manipulated by a dopaminergic drug, but that the effectiveness of such a manipulation depends on baseline differences in sensation-seeking trait. This emphasizes the importance of considering individual differences when investigating manipulation of risky decision-making, and may have relevance for the development of pharmacotherapies for disorders involving excessive risk-taking in humans, such as pathological gambling.

Impulsivity is a vulnerability marker for drug addiction in which other behavioural traits such as anxiety and novelty seeking (‘sensation seeking’) are also widely present. However, inter-relationships between impulsivity, novelty seeking and anxiety traits are poorly understood. The objective of this paper was to investigate the contribution of novelty seeking and anxiety traits to the expression of behavioural impulsivity in rats. Rats were screened on the five-choice serial reaction time task (5-CSRTT) for spontaneously high impulsivity (SHI) and low impulsivity (SLI) and subsequently tested for novelty reactivity and preference, assessed by open-field locomotor activity (OF), novelty place preference (NPP), and novel object recognition (OR). Anxiety was assessed on the elevated plus maze (EPM) both prior to and following the administration of the anxiolytic drug diazepam, and by blood corticosterone levels following forced novelty exposure. Finally, the effects of diazepam on impulsivity and visual attention were assessed in SHI and SLI rats. SHI rats were significantly faster to enter an open arm on the EPM and exhibited preference for novelty in the OR and NPP tests, unlike SLI rats. However, there was no dimensional relationship between impulsivity and either novelty-seeking behaviour, anxiety levels, OF activity or novelty-induced changes in blood corticosterone levels. By contrast, diazepam (0.3–3 mg/kg), whilst not significantly increasing or decreasing impulsivity in SHI and SLI rats, did reduce the contrast in impulsivity between these two groups of animals. This investigation indicates that behavioural impulsivity in rats on the 5-CSRTT, which predicts vulnerability for cocaine addiction, is distinct from anxiety, novelty reactivity and novelty-induced stress responses, and thus has relevance for the aetiology of drug addiction.