The effects of life experiences and polygenic risk for depression on the development of positive and negative cognitive biases across adolescence: The CogBIAS hypothesis.

The Cognitive Bias (CogBIAS) hypothesis proposes that cognitive biases develop as a function of environmental influences (which determine the valence of biases) and the genetic susceptibility to those influences (which determines the potency of biases). The current study employed a longitudinal, polygenic-by-environment approach to examine the CogBIAS hypothesis. To this end, measures of life experiences and polygenic scores for depression were used to assess the development of memory and interpretation biases in a three-wave sample of adolescents (12-16 years) (N = 337). Using mixed effects modeling, three patterns were revealed. First, positive life experiences (PLEs) were found to diminish negative and enhance positive forms of memory and social interpretation biases. Second, and against expectation, negative life experiences and depression polygenic scores were not associated with any cognitive outcomes, upon adjusting for psychopathology. Finally, and most importantly, the interaction between high polygenic risk and greater PLEs was associated with a stronger positive interpretation bias for social situations. These results provide the first line of polygenic evidence in support of the CogBIAS hypothesis, but also extend this hypothesis by highlighting positive genetic and nuanced environmental influences on the development of cognitive biases across adolescence.

The network structure of psychopathological and resilient responses to the pandemic: A multicountry general population study of depression and anxiety.

Commonly identified patterns of psychological distress in response to adverse events are characterized by resilience (i.e., little to no distress), delayed (i.e., distress that increases over time), recovery (i.e., distress followed by a gradual decrease over time), and sustained (i.e., distress remaining stable over time). This study aimed to examine these response patterns during the COVID-19 pandemic. Anxiety and depressive symptom data collected across four European countries over the first year of the pandemic were analyzed (N = 3,594). Participants were first categorized into groups based on the four described patterns. Network connectivity and symptom clustering were then estimated for each group and compared. Two thirds (63.6%) of the sample displayed a resilience pattern. The sustained distress network (16.3%) showed higher connectivity than the recovery network (10.0%) group, p = .031; however, the resilient network showed higher connectivity than the delayed network (10.1%) group, p = .016. Regarding symptom clustering, more clusters emerged in the recovery network (i.e., three) than the sustained network (i.e., two). These results replicate findings that resilience was the most common mental health pattern over the first pandemic year. Moreover, they suggest that high network connectivity may be indicative of a stable mental health response over time, whereas fewer clusters may be indicative of a sustained distress pattern. Although exploratory, the network perspective provides a useful tool for examining the complexity of psychological responses to adverse events and, if replicated, could be useful in identifying indicators of protection against or vulnerability to future psychological distress.

The three-dimensional community structure of attention-deficit hyperactivity disorder (ADHD) traits captured by the Adult ADHD Self-Report Scale: An exploratory graph analysis.

OBJECTIVE: To employ a novel analytic method-namely, exploratory graph analysis (EGA)-to subclinical attention-deficit hyperactivity disorder (ADHD) trait scores in order to reveal their dimensional structure, as well as compare EGA's performance with traditional factor-analytic techniques in doing so. METHOD: 1149 respondents from a survey panel completed the ASRS, a common ADHD scale made up of 18 distinct trait measures. EGA and factor analysis were applied to identify traits which associate with each other. RESULTS: EGA revealed 3 distinct communities, and ruled out a 2-community structure. This was in contrast to the 2-factor structure suggested by the factor analysis, and the conventional division of ADHD into two subdimensions (hyperactivity and inattention). CONCLUSION: A dimensional structure of three clusters (hyperactivity, impulsivity and inattention) may better reflect the traits underlying ADHD. EGA has benefits in terms of both analytic approach and interpretability of findings.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder.

OBJECTIVES: Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania. METHODS: Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms. RESULTS: Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility. CONCLUSIONS: Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

A network approach to understanding social distancing behaviour during the first UK lockdown of the COVID-19 pandemic.

OBJECTIVE: Given the highly infectious nature of COVID-19, social distancing practices are key in stemming the spread of the virus. We aimed to assess the complex interplay among psychological factors, socio-demographic characteristics and social distancing behaviours within the framework of the widely used Capability, Opportunity, Motivation-Behaviour (COM-B) model. DESIGN: The present research employed network psychometrics on data collected during the first UK lockdown in April 2020 as part of the COVID-19 Psychological Research Consortium (C19PRC) Study. Using a network approach, we examined the predictions of psychological and demographic variables onto social distancing practices at two levels of analysis: macro and micro. RESULTS: Our findings revealed several factors that influenced social distancing behaviour during the first UK lockdown. The COM-B model was successful in predicting particular aspects of social-distancing via the influence of psychological capability and motivation at the macro-and micro-levels, respectively. Notably, demographic variables, such as education, income, and age, were directly and uniquely predictive of certain social distancing behaviours. CONCLUSION: Our findings reveal psychological factors that are key predictors of social distancing behaviour and also illustrate how demographic variables directly influence such behaviour. Our research has implications for the design of empirically-driven interventions to promote adherence to social distancing practices in this and future pandemics.

How does the COVID-19 pandemic impact on population mental health? A network analysis of COVID influences on depression, anxiety and traumatic stress in the UK population.

BACKGROUND: The coronavirus disease 2019 (COVID-19) emergency has led to numerous attempts to assess the impact of the pandemic on population mental health. The findings indicate an increase in depression and anxiety but have been limited by the lack of specificity about which aspects of the pandemic (e.g. viral exposure or economic threats) have led to adverse mental health outcomes. METHODS: Network analyses were conducted on data from wave 1 (N = 2025, recruited 23 March-28 March 2020) and wave 2 (N = 1406, recontacts 22 April-1 May 2020) of the COVID-19 Psychological Research Consortium Study, an online longitudinal survey of a representative sample of the UK adult population. Our models included depression (PHQ-9), generalized anxiety (GAD-7) and trauma symptoms (ITQ); and measures of COVID-specific anxiety, exposure to the virus in self and close others, as well as economic loss due to the pandemic. RESULTS: A mixed graphical model at wave 1 identified a potential pathway from economic adversity to anxiety symptoms via COVID-specific anxiety. There was no association between viral exposure and symptoms. Ising network models using clinical cut-offs for symptom scores at each wave yielded similar findings, with the exception of a modest effect of viral exposure on trauma symptoms at wave 1 only. Anxiety and depression symptoms formed separate clusters at wave 1 but not wave 2. CONCLUSIONS: The psychological impact of the pandemic evolved in the early phase of lockdown. COVID-related anxiety may represent the mechanism through which economic consequences of the pandemic are associated with psychiatric symptoms.