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Zr-89 oxine labeled cell doses infused to rhesus macaques.

The effect of cognitive effort on mood is unclear. Expending cognitive effort is generally avoided but expenditure of cognitive effort is the primary feature of some popular recreational activities, such as sudoku or video games. However, one common confound is that previous studies mostly looked into mood changes before and after cognitive effort manipulation. Therefore, it is unknown what im- mediate impact cognitive effort has on momentary mood. To investigate this, in two studies, we used a letter sorting paradigm to test whether momentary mood ratings change with different levels of experimentally manipulated task difficulty. In study 1 (N = 105), we found that increased difficulty, and consequently cogni- tive effort, leads to more errors and lower mood ratings. In study 2 (N = 210), we replicated our results from study 1 and further demonstrated that cognitive effort influences mood independently from immediate reward. In conclusion, controlling for reward conditions, we found that cognitive effort reduces people’s moment to moment mood.

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25–40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.

The Adolescent Brain Cognitive Development dataset (ABCD) is a popular source of data for research on children and adolescents, aged 9-11 at baseline. Depression is frequently first diagnosed in adolescents, is prevalent and carries heavy burden, thus ABCD provides an attractive opportunity to research depression in adolescents. The only continuous measure of depression provided in ABCD is the parent-report Child Behavior Checklist’s DSM-5-Oriented Affective Problems scale (CBCL-Aff). It is important for depression research in the ABCD dataset that the CBCL-Aff is a valid measure of depression in this age group. We, therefore, tested the sensitivity and specificity of the CBCL-Aff for depression in the ABCD data. CBCL-Aff agreed with parent-report of children’s symptoms but disagreed with child self-report. To resolve this disagreement, we further confirmed our results in two independent datasets, aged 9-12: the Healthy Brain Network (HBN) and the Brazilian High Risk Cohort Study (BHRC). Both datasets provided clinician-report depression diagnoses, which we used as a gold standard. CBCL-Aff successfully predicted clinician-report diagnoses, supporting its validity as a continuous measure of depression. As parents and children disagreed on the child’s symptoms, we discuss implications of using a parent-report only measure of child depression.

Reporting of effect sizes is standard practice in psychology and psychiatry research. However, interpretation of these effect sizes can be meaningless or misleading – in particular, the evaluation of specific effect sizes as “small”, “medium” and “large” can be inaccurate depending on the research context. A real-world example of this is research into the mental health of children and young people during the Covid-19 pandemic. Evidence suggests that clinicians and services are struggling with increased demand, yet population studies looking at the difference in mental health before and during the pandemic report effect sizes that are deemed “small”. In this short review we utilise simulations to demonstrate that a relatively small shift in mean scores on mental health measures can indicate a large shift in the number of cases of anxiety and depression when scaled up to an entire population. This shows that “small” effect sizes can in some contexts be large and impactful.

Supplemental material, sj-docx-1-jad-10.1177_10870547231168334 for Longitudinal Associations Between COVID-19 Stress and the Mental Health of Children With ADHD by Ainsley Summerton, Susannah T. Bellows, Elizabeth M. Westrupp, Mark A. Stokes, David Coghill, Mark A. Bellgrove, Delyse Hutchinson, Stephen P. Becker, Glenn Melvin, Jon Quach, Daryl Efron, Argyris Stringaris, Christel M. Middeldorp, Tobias Banaschewski and Emma Sciberras in Journal of Attention Disorders

We will be investigating the impact of social anxiety disorder CBT/CT treatments on depression through a systematic review and meta-analysis of social anxiety disorders RCT's, pulling the depression scores taken as a secondary measure.

Social anxiety disorder (SAD) is a debilitating condition that typically develops in adolescence. SAD frequently co-occurs with other psychological conditions, including depression and suicidal ideation. Understanding how the symptoms of SAD may be related to later suicidal ideation and depressive symptoms in adolescents is critical to developing effective intervention strategies that can potentially save lives. Social anxiety is associated with thwarted belongingness and perceived burdensomeness (Arditte et al., 2016) and may confer vulnerability to suicidality (Joiner, 2007). In line with this view, Leigh, Chiu, and Ballard (2022) reported meta-analytic evidence of a concurrent, positive association between social anxiety symptoms and suicidal ideation in young people. However, the findings from the two prospective studies are inconsistent (Gallagher et al., 2014; Zhu et al., 2021). This limits our confidence in inferring a causal relationship between social anxiety and suicidal ideation from existing findings. Furthermore, these prospective studies did not investigate potential mediators in this association. Identifying mediating mechanism through which the predictor influences the outcome can guide the development of targeted interventions aimed at disrupting the mediator and preventing negative outcomes. Depressive symptoms can be a potential mediator of the association between social anxiety and subsequent suicidality. Social anxiety symptoms have been shown to predict depressive symptoms at 12-month follow-up in an adolescent sample (Belmans, Bastin, Raes, & Bijttebier, 2019; N = 1290). In addition, Hill et al. (2018; N = 80) found evidence that depressive symptoms predict subsequent suicidal ideation in a group of young people. However, the mediating role of depressive symptoms in this association has not been explored in a prospective study. The presence of social phobia can exacerbate depression. Individuals with a dual diagnosis of social phobia and depression at baseline are more likely to have persistent depression at 12-month follow-up in an adult sample (Gaynes et al., 1999; N = 85) and at 34-50-month follow-up in an adolescent sample (Stein et al., 2001; N = 2548). However, these findings may not apply to people who do not have a clinical diagnosis of social phobia or depression. Studying community adolescents is important as it can inform the development of targeted prevention programs. in addition, previous studies did not control for anxiety symptoms, therefore it is unclear if the findings reported are specifically attributable to social anxiety. The current research aims to investigate the potential role of social anxiety symptoms in exacerbating suicidal ideation and other depressive symptoms in adolescents. We will examine suicidal ideation and other depressive symptoms separately. While depression may be a risk factor for suicidal ideation, not all individuals who experience depression have suicidal thoughts or behaviours. The proposed study will use open-access data from prospective cohort studies on adolescents, one from the UK and another one from the US. We will conduct cross-study validation, which involves replicating the results across two datasets. By using this approach, we can provide more robust evidence to inform clinical practice. Based on meta-analytic evidence, we hypothesise a positive correlation between baseline social anxiety symptoms and subsequent suicidal ideation, with a statistically significant effect. Considering the links between depressive symptoms, social anxiety, and suicidal ideation, we further hypothesise that depressive symptoms (excluding suicidal ideation) will have a significant indirect effect in the association between baseline social anxiety and subsequent suicidal ideation, even after controlling for anxiety symptoms. We also hypothesise that intermediate depressive symptoms will have a significant indirect effect in the association between baseline social anxiety symptoms and subsequent depressive symptoms, even after accounting for the effect of anxiety symptoms. The findings of this study could have important implications for understanding the mechanisms underlying the development and maintenance of suicidal ideation and depressive symptoms in socially anxious youth.

Summary of the Literature and Rationale for Study The NIH (https://www.nimh.nih.gov/health/statistics/major-depression) estimates that in 2019, 3.8 million (15.7%) adolescents aged 12 to 17 in the United States had at least one major depressive episode. This makes adolescent depression (major depressive disorder, MDD) a topic of great importance. There is a growing trend towards big data in psychiatry, in particular in depression research [Bzdok et al, 2020]. This is facilitated by the collection of massive datasets, such as the Adolescent Brain Cognitive Development study (ABCD) [Casey et al, 2018]. The ABCD study is an ongoing collection of longitudinal and multi-modal brain data collected from children/adolescents, described in more detail below in the data section. The ABCD study is an unprecedented attempt to collect multimodal longitudinal data from adolescents, and at this moment it is the largest database of such nature [Karcher & Barch, 2021]. Thus, this data is attractive to use in research on depression onset and development in adolescents. Depression severity in ABCD is measured with the Child Behavior Checklist (CBCL) [​​Achenbach & Edelbrock, 1991]. It is a component of the Achenbach System of Empirically Based Assessment (ASEBA). The ASEBA is used to detect behavioral and emotional problems in children and adolescents. The CBCL is completed by parents and is a continuous measure of depression severity that is based on presence or absence of rated depression-related symptoms. CBCL measurements have been used in predictive depression research based on ABCD data [Ho et al., 2021]. The number of publications based on the ABCD data are growing every year (https://abcdstudy.org/publications/), and so do the papers that address depression. There have been warnings against the use of unreliable measurements in psychology research [Flake & Fried, 2020]. In particular, the authors highlight that the validity of the study conclusions reflects the validity of measurements used. Therefore, it is important to validate CBCL-dep score (which we refer to as simply CBCL throughout this registration) as a measure of depression in ABCD data. In our study, we attempt to validate the CBCL scores as a measure of adolescent depression in ABCD data. We use CBCL scores to predict the “true” diagnosis, as measured by K-SADS. [Townsend et al., 2020]. The K-SADS screen is a combination of dimensional and categorical approaches and is used to diagnose psychopathology in children and adolescents according to DSM-5 criteria. We then plot the Receiver Operator Characteristic (ROC) and calculate the Area Under the Curve (AUCROC). We describe more methodological details in the Analysis Plan section. We propose two parts to our study: PREDICTION In this part, we check if CBCL scores would be a good predictor of K-SADS scores. Methodological details are listed in sections below. SPECIFICITY In this part, we aim to ensure the specificity of the prediction, i.e. whether CBCL could predict depression specifically vs. general psychopathology. Methodological details are listed in sections below.

The reliability of measurements is crucial in psychology research. The validity of the study conclusions reflects the validity of measurements used [Flake and Fried, 2020]. Using valid measurements is especially important when working with large datasets. Such datasets are increasingly popular in neuropsychiatry [Thompson et al., 2020, Turner, 2014] and allow researchers to answer questions with higher confidence [Thompson et al., 2020]. One example of a massive dataset primarily aimed at neuropsychiatric research is the Adolescent Brain Cognitive Develoment study (ABCD) [Casey et al., 2018]. There is an increasing volume of depression research that uses ABCD data (https://abcdstudy.org/publications/). Adolescent depression is a popular direction of research because of its high prevalence in society (https: //www.nimh.nih.gov/health/statistics/major-depression). This brings relevance to the measures used for depression estimation in the ABCD. In our previous pre-registered analysis (https://osf.io/8s7bv), we have looked at the only continuous measure of depression available in the ABCD - the Child Behavior Checklist (CBCL-Dep, further referred to as CBCL) [Achenbach and Edelbrock, 1991], a parent report measure, and compared it against the commonly accepted gold standard - the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) [Townsend et al., 2020]. ABCD contains both parent and child report KSADS data, but it does not contain information on consesnsus diagnosis. We used the area Under the Curve (AUCROC) to evaluate the predictive performance of the CBCL on the child report KSADS. The results were quite poor: AUCROC for differentiating adolescents with depression from adolescents without depression was 0.63, which corresponds to ”poor” performance [Ferdinand, 2008]. CBCL was not a good predictor of KSADS depression scores, as measured on ABCD data. For the specificity hypothesis, the results were even poorer. CBCL could differentiate adolescents with depression from adolescents without depression but with another form of psychopathology with AUCROC equal to 0.48, and adolescents with depression but without another form of psychopathology from adolescents without depression but with another form of psychopathology with AUCROC equal to 0.46. These results are below chance. As a next step, we would like to extend our conclusions beyond the ABCD data. We intend to look into two datasets that contain psychiatric evaluations of adolescents matching the age of the baseline wave of the ABCD data: the Healthy Brain Network (HBN) [Alexander et al., 2017] and the Brazilian High Risk Cohort Study for the Development of Childhood Psychiatric Disorders (BHRC) [Salum et al., 2015]. Since these datasets did not have enough subjects with depression in the 9-11 years old range, we extended our consideration to 9-13 years old for both datasets. Both of those datasets use CBCL as well as a clinician diagnosis (HBN: KSADS; BHRC: the Development And Well-Being Assessment (DAWBA)) [Goodman et al., 2000]).

The reliability of measurements is crucial in psychology research. The validity of the study conclusions reflects the validity of measurements used [Flake and Fried, 2020]. Using valid measurements is especially important when working with large datasets. Such datasets are increasingly popular in neuropsychiatry [Thompson et al., 2020, Turner, 2014] and allow researchers to answer questions with higher confidence [Thompson et al., 2020]. One example of a massive dataset primarily aimed at neuropsychiatric research is the Adolescent Brain Cognitive Development study (ABCD) [Casey et al., 2018]. There is an increasing volume of depression research that uses ABCD data (https://abcdstudy.org/publications/). Adolescent depression is a popular direction of research because of its high prevalence in society (https: //www.nimh.nih.gov/health/statistics/major-depression). This brings relevance to the measures used for depression estimation in the ABCD. In our previous pre-registered analysis (https://osf.io/8s7bv), we have looked at the only continuous measure of depression available in the ABCD - the Child Behavior Checklist (CBCL-Dep, further referred to as CBCL) [Achenbach and Edelbrock, 1991], a parent report measure, and compared it against the commonly accepted gold standard - the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) [Townsend et al., 2020]. ABCD contains both parent and child report K-SADS data, but it does not contain information on consensus diagnosis. We used the area Under the Curve (AUCROC) to evaluate the predictive performance of the CBCL on the child report KSADS. The results were quite poor: AUCROC for differentiating adolescents with depression from adolescents without depression was 0.63, which corresponds to ”poor” performance [Ferdinand, 2008]. CBCL was not a good predictor of K-SADS depression scores, as measured on ABCD data. For the specificity hypothesis, the results were even poorer. CBCL could differentiate adolescents with depression from adolescents without depression but with another form of psychopathology with AUCROC equal to 0.48, and adolescents with depression but without another form of psychopathology from adolescents without depression but with another form of psychopathology with AUCROC equal to 0.46. These results are below chance. As a next step, we would like to extend our conclusions beyond the ABCD data. We intend to look into two datasets that contain psychiatric evaluations of adolescents matching the age of the baseline wave of the ABCD data: the Healthy Brain Network (HBN) [Alexander et al., 2017] and the Brazilian High Risk Cohort Study for the Development of Childhood Psychiatric Disorders (BHRC) [Salum et al., 2015]. Since these datasets did not have enough subjects with depression in the 9-11 years old range, we extended our consideration to 9-13 years old for both datasets. Both of those datasets use CBCL as well as a clinician diagnosis (HBN: K-SADS; BHRC: the Development And Well-Being Assessment (DAWBA)) [Goodman et al., 2000]).

The reliability of measurements is crucial in psychology research. The validity of the study conclusions reflects the validity of measurements used [Flake and Fried, 2020]. Using valid measurements is especially important when working with large datasets. Such datasets are increasingly popular in neuropsychiatry [Thompson et al., 2020, Turner, 2014] and allow researchers to answer questions with higher confidence [Thompson et al., 2020]. One example of a massive dataset primarily aimed at neuropsychiatric research is the Adolescent Brain Cognitive Develoment study (ABCD) [Casey et al., 2018]. There is an increasing volume of depression research that uses ABCD data (https://abcdstudy.org/publications/). Adolescent depression is a popular direction of research because of its high prevalence in society (https://www. nimh.nih.gov/health/statistics/major-depression). This bring relevance to the measures used for depression estimation in the ABCD. In our previous pre-registered analysis (https://osf.io/8s7bv), we have looked at the only continuous measure of depression available in the ABCD - the Child Behavior Checklist (CBCL) [Achenbach and Edelbrock, 1991], a parent report measure, and compared it against the commonly accepted gold standard - the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) [Townsend et al., 2020]. ABCD contains both parent and child report KSADS data, but it does not contain information on consesnsus diagnosis. We used the area Under the Curve (AUCROC) to evaluate the predictive performance of the CBCL on the child report KSADS. The results were quite poor: AUCROC for differentiating adolescents with depression from adolescents without depression was 0.63, which corresponds to ”poor” performance [Ferdinand, 2008]. CBCL was not a good predictor of K-SADS depression scores, as measured on ABCD data. For the specificity hypothesis, the results were even poorer. CBCL could differentiate adolescents with depression from adolescents without depression but with another form of psychopathology with AUCROC equal to 0.48, and adolescents with depression but without another form of psychopathology from adolescents without depression but with another form of psychopathology with AUCROC equal to 0.46. These results are below chance. As a next step, we would like to extend our conclusions beyond the ABCD data. We intend to look into two datasets that contain psychiatric evaluations of adolescents matching the age of the baseline wave of the ABCD data: the Healthy Brain Network (HBN) [Alexander et al., 2017] and the Brazilian High Risk Cohort Study for the Development of Childhood Psychiatric Disorders (BHRC) [Salum et al., 2015]. Since these datasets did not have enough subjects with depression in the 9-11 years old range, we extended our consideration to 9-13 years old for both datasets. Both of those datasets use CBCL as well as a clinician diagnosis (HBN: KSADS; BHRC: the Development And Well-Being Assessment (DAWBA) [Goodman et al., 2000]).

The use of SSRIs for the treatment of depression and anxiety in young people is increasing. However, the effects of SSRIs in adolescence, a time when there are substantial changes in neural, cognitive, and social functioning, are not well understood. Here, we review evidence from clinical trials about the benefits and risks of SSRIs in young people and consider their mechanisms of action, as shown through human experimental work and animal models. We emphasise key outstanding questions about the effects of SSRIs in youth, identified through gaps in the literature and in consultation with young people with lived experience. It is crucial to characterise the mechanisms underpinning risks and benefits of SSRIs in this age group to progress the field, and to narrow the chasm between the widespread use of SSRIs in youth and the science on which this use is based.

The test-retest reliability of fMRI functional connectivity is a key factor in the identification of reproducible biomarkers for psychiatric illness. Low reliability limits the observable effect size of brain-behavior associations. Despite this important connection to clinical applications of fMRI, few studies have explored reliability in populations with psychiatric illnesses or across age groups. We investigate the test-retest reliability of functional connectivity in a longitudinal cohort of adolescents with and without major depressive disorder (MDD). Measuring reliability is complex and several metrics exist that can offer unique perspectives: for example, univariate metrics capture reliability of a single connection at a time while multivariate metrics reflect stability of the entire connectome. We compare a widely used univariate metric, intraclass correlation coefficient (ICC), and two multivariate metrics, fingerprinting and discriminability. Depressed adolescents were more reliable than healthy adolescents at the univariate level (0.34 > 0.24; Wilcoxon rank-sum:p< .001), and both groups had poor average ICCs (HV= 0.53; FIMDD= 0.45; Poisson(1) testp< .001) and discriminability were above chance (DiscrHV= 0.75;DiscrMDD= 0.76; 500-fold permutation testp< .01). Reliability was not associated with symptoms or medication, suggesting that there is not a strong relationship between depression and reliability. These findings support the shift towards multivariate analysis for improved power and reliability.

This project aims to address the effect of environmental contingencies on emotional response and decision-making behavior under threatening scenarios. In particular, the threatening scenarios are simulated via selected horror movie clips. By role-playing as the characters depicted in the clips, participants are asked to rate their emotional responses to the scenes and choose a decision (fight or flight) that they consider to maximize the survival utility for the characters.

Having a family history of depression makes a child three times more likely to have a major depressive episode. It is also associated with an earlier age of onset of MDD, a longer length of illness, and the presence of comorbid diagnoses in cross-sectional samples. However, few studies have examined the impact of a family history of mental illness in a longitudinal adolescent sample. In this study, we followed both healthy and depressed teenagers, ages 11-17, for multiple years. Parents of participants completed a family history interview, providing information about the mental health diagnoses and symptoms of both the immediate and extended family. By using this information in conjunction with clinical information about the child’s symptoms, we hope to determine whether family history is a clinically useful predictor in the development of mood disorders in adolescents.

This is a pre-registration on whether the psychological therapy outcomes for young people (aged 16 -24 years) differ from those of older people (25 - 65 years) in the NHS Englad Data of the Increased Access for Psychological Therapy (IAPT) .

The assumption that mood remains constant when unaffected by task stimuli is an implicit but integral part of neurocognitive research, but it remains largely unexamined. The purpose of this study is to test the hypothesis that periods of rest and simple tasks lower participants’ mood, an effect we call “passage-of-time dysphoria.” This would empirically demonstrate the long-intuited phenomenon that mood is sensitive to the passage of time. This hypothesis and related ones have been confirmed in (1) a set of “online participants” collected at the National Institute of Mental Health and (2) an exploratory sample of 5,000 “mobile app” participants who played a gambling game on their smartphones. This exploratory sample was randomly selected from a dataset collected by our co-authors at University College London (UCL). We are preregistering the same analyses that were performed on the exploratory sample of mobile app participants for a held-out, confirmatory sample of 21,896 of the mobile app participants collected by UCL.

Irritability is a common, chronic, and impairing clinical phenotype in youth (Leibenluft, 2011; Stringaris et al., 2018). Severe irritability is codified in the DSM-5 diagnosis of disruptive mood dysregulation disorder (DMDD; American Psychiatric Association, 2013). Alterations in reward processing, including frustrative nonreward (i.e., omission of expected reward), are a proposed mechanism of pediatric irritability (Brotman et al., 2017; Leibenluft, 2017). However, only a handful of studies have directly examined reward processing in irritability (Adleman et al., 2011; Deveney, 2019; Deveney et al., 2013; Dougherty et al., 2018). Further, no published studies have examined reward prediction error (RPE; i.e., the difference between an expected outcome and a received outcome). Negative RPE (i.e., an outcome that is worse than expected) has particular relevance to frustrative nonreward (Kircanski et al., 2019). In this study, youth with DMDD and healthy volunteer (HV) youth completed a Monetary Incentive Delay (MID) task while undergoing functional magnetic resonance imaging (fMRI). In our primary analyses, we use this task to compare neural and behavioral aspects of RPE in DMDD vs. HV. In supplementary exploratory analyses, we examine neural correlates of anticipation and receipt of reward and loss in DMDD vs. HV, neural correlates of RPE along two core dimensions in DMDD (phasic and tonic irritability), and other neural and behavioral effects.

The COVID-19 pandemic has devastated the lives of many, both for those infected by the virus as well as those spared from the infection. The COVID-19 pandemic has changed the everyday lives of all, through full or partial forced isolation (lockdown), closure of schools, public spaces and associated economic consequences. It is already well documented that COVID-19 pandemic may have substantial and long-lasting consequences for the mental health of many 1–4. This pandemic might worsen mental health on average in the population, but young people are likely to be affected disproportionately 5–8. Young people’s mental health has been found to be worse following the pandemic-related lockdown compared to the adult population even after the first restrictions were lifting, suggesting that there could be long-lasting consequences of pandemic 9. Young adulthood (the period between early twenties and early thirties) is a time marked by instability and fragility in several aspects of life and a critical age for the development of psychopathologies 10–12. Even before COVID-19, Public Health England reported that 14.3M days of work are lost every year to stress, depression and anxiety. The ~12M young adults living in the UK make up over a third of the current workforce and nearly half of the workforce for the next two decades. The instability and financial hardship generated by this crisis are likely to exacerbate the struggles of young adults, with cascading effects on the health, wellbeing and economic stability of the nation. The majority of the research to date focuses on average psychological changes during the COVID-19 crisis and the unprecedented global experience of lockdown, but this crisis is likely to affect individuals differently 13. Individual differences are likely to be large and include both negative changes, e.g., increased anxiety and depression, and positive changes, e.g., increased wellbeing 14. It is important to study individual differences as well as average differences (means) in response to the pandemic and extraordinary experience of lockdown. A fundamental aspect of individual differences often ignored when studying the response to COVID-19 crises is genetic variation. Yet, it has been demonstrated over decades of twin studies that inherited DNA differences contribute substantially to most psychological traits 15,16. Even more importantly, research has shown that sensitivity to environmental changes have a heritable component 17. It is thus reasonable to assume that the response to the COVID-19 crisis is also partially driven by genetic variation between individuals. Our focus here is on individual differences in mental health outcomes before and during the ongoing crisis and the genetic and environmental origins of these differences as assessed by the classical twin method that compares the resemblance of identical and non-identical twins. We will also investigate factors influencing changes in mental health using genome-wide polygenic scores (GPS), aggregate scores capturing genetic predisposition of risk and protective factors based on previous genome-wide association study (GWAS) 18. Lockdown measures could be especially detrimental for young adults with existing psychological and psychiatric vulnerabilities 8,14,19. Here we can capitalise on the data collected at TEDS (the Twins Early Development Study 20 over two decades to identify young adults already experiencing mental health problems prior to the COVID-19 pandemic and assess the change in their mental health symptoms while taking into account the genetic factors. One month after COVID-19 lockdown (20-30 April 2020), we assessed a dozen psychological measures for more than 3000 UK-representative young adult twins in their mid-twenties, participating since infancy in the longitudinal Twins Early Development Study (TEDS; Rimfeld et al., 2019). They were assessed on the same traits in a three-wave longitudinal study collected in April, July and October 2020, although the focus of the present study in on mental health outcomes. In addition, we collected data on measures developed specifically to quantify the impact of COVID-19 pandemic (the CoRonavIruS Health Impact Survey (CRISIS; 21). These data will allow us to study mental health before and during the pandemic using genetically sensitive designs. We will also test the extent to which self-reported data on COVID worries, life changes and mood states during pandemic (CRISIS measures) explain change in mental health measured over the first seven months of the pandemic. Our first study (Rimfeld et al. under review) compared a wide range of psychological measures (30 different traits) from T1 (2018) to T2 (April 2020) and found modest and inconsistent mean changes in these traits after one month of the COVID-19 lockdown. The largest negative effects of the pandemic were reduced volunteering and achievement motivation and increased hyperactivity-inattention. However, there were as many positive changes in response to the pandemic, most notably, reduced verbal peer victimisation. It is, however, possible that one month of lockdown was an insufficient timeframe to examine the negative consequences of this unprecedented restriction. Here, we use longitudinal data collected over four waves (2018 and three post-pandemic waves of assessment one, four and seven months after lockdown began in March 2020). The focus of the present study is on the aetiology of individual differences in mental health changes in young adults, as a function of the COVID-19 pandemic. We will: 1) describe changes in means from 2018 and during the pandemic, but our focus is on individual differences and their genetic and environmental origins at each wave of assessment as well as change in individual differences (we will also report these changes in extreme, especially vulnerable groups); 2) analyse change and continuity using longitudinal modelling (latent growth curve modelling) and the twin design (Cholesky decomposition of latent growth curve modelling); 3) assess the extent to which changes in mental health can be predicted by self-reported COVID worries, mood states, life changes and COVID impact (family, symptoms, living conditions) (derived from CRISIS 21 ) and polygenic scores (derived from both psychiatric and cognitive GWA summary statistics).

This is a protocol detailing the planned analyses for a number of studies within a single project. The studies are related by drawing on the same sample or subsets of the overall sample, and sharing one of two broad aims (detailed below). The purpose of the overall project is to investigate a patient-centred metric of change during routinely delivered psychological therapy for depression or anxiety disorders among adults in Improving Access to Psychological Therapies (IAPT) services. To do this, the project aims to calculate minimum clinically important differences (MCIDs) for symptom change before and once ending therapy; that is, the smallest amount of change pre-post treatment needed on a measure of symptoms for it to be deemed important by a patient.

Although other research focuses on average psychological changes during the COVID-19 crisis and the unprecedented global experience of lockdown, our focus is on individual differences in psychological traits before and after the crisis and the genetic and environmental origins of these differences as assessed by the classical twin method that compares the resemblance of identical and non-identical twins. One month after COVID-19 lockdown (20-30 April 2020), we assessed a dozen psychological measures for more than 3000 UK-representative young adult twins in their mid-twenties, participants since infancy in the longitudinal Twins Early Development Study (TEDS; Rimfeld et al., 2019). They were assessed on the same traits in 2018 (T1), which made it possible to compare results before and after lockdown (T2) for a wide range of traits such as anxiety and depression, relationships, physical activity, online behaviour, volunteering, self-harm and drug use, traits especially important during this emerging adulthood developmental phase. We will describe changes in means from T1 to T2, but our focus is on individual differences and their genetic and environmental origins at T1 and T2 and in changes from T1 to T2. We operationalise change by regressing T1 from T2 so that T2 scores are independent of scores at T1, which we refer to as ‘change scores’. We will use the twin method to estimate genetic and environmental influences on variance at T1, at T2, and on T2 change scores.

In our original manuscript, we demonstrated that rest periods lowered participants' mood, an effect we call “passage-of-time dysphoria." This finding was replicated in 15 cohorts totaling 27,882 adult and adolescent participants. The dysphoria was (1) relatively large (13.8% after 7.3 minutes, Cohen's d=0.574), (2) variable across and within individuals but consistent across cohorts, and (3) present during simple visuomotor and gambling tasks. Rest also impacted behaviour: participants were less likely to gamble at the beginning of a task if it was preceded by rest. The dysphoria was inversely related to depression risk and a computationally estimated reward sensitivity parameter. Reviewers raised 3 issues which we will address with this series of follow-up experiments. The first is that the POTD may be related to boredom, the second that it may be related to mind-wandering, and third, that we did not provide any evidence that the effect occurs in the real world. We will address boredom as a potential cause by assessing state boredom before and after a rest period. We will address mind-wandering by assessing self-report mental context during the rest period. We will demonstrate the real world applicability of POTD by having participants complete a task in which they are instructed to occupy themselves however they would like for seven minutes and then they are signaled that they need to return to the task by a sound and their mood is assessed. Specifically, the instructions for the real world task are: “After you rate your mood, you may put the task aside and do something else for 7 minutes. When that time is over, an alarm will sound. As soon as you hear the alarm, please come back to answer some questions. Press SPACE to start your break.”

This will be the third study on studying how momentary mood is influenced in the letters game with effort and reward components. The letter task is a task where participants will be first presented with letters with random orders and then asked to sort them according to the alphabet. The participants will receive reward information after they respond and then are asked to rate their current happiness ratings. We have used this task but with variation in other two studies. The first study uses a version of the task where participants are allowed to attempt multiple times till they either reach a predetermined limit or provide the correct answer. The second version of the task provided participants with two options that had varying degrees of difficulty and potential reward. Those two previous studies using the letter task all include a multiple attempt component, in which case the effect of effort and failure is unable to be discerned. This particular study specifically aims to demonstrate that the null effect of cognitive effect remains without the influence of with-in trial failure.

Objective: The Affective Reactivity Index (ARI) is widely used to assess young people’s irritability symptoms, but youth and caregivers often diverge in their assessments. Such informant discrepancy might stem from poor reliability. However, evidence from genetic, imaging and treatment studies suggests that irritability may not be a unitary construct. Hence informants might be sensitive to different aspects of irritability. We use an out-of-sample replication approach and a longitudinal design to test these hypotheses. Method: Across two independent samples (N1=765, 8-21 years; N2=1910, 6-21 years), we investigate the reliability and measurement invariance of the ARI, examine sociodemographic and clinical predictors of discrepant reporting, and probe the utility of a bifactor model for cross-informant integration. Results: Despite good internal consistency and 6-week-retest-reliability of parent and youth forms, we confirm substantial informant discrepancy in ARI ratings, which is stable over six weeks (n1=177). Measurement invariance across informants was weak, indicating that parents and youth may interpret ARI items differently. Informant-discrepancy increased with irritability severity, but which informant reported higher child-irritability depended on the child’s diagnostic status. In both datasets, a bifactor model parsing informant-specific from shared irritability-related variance fit the data well. Conclusion: Parent and youth ARI reports and their discrepancy are reliable. However, parent and youth ratings may reflect different interpretations of the scale items; hence they should not be averaged. Our findings suggest that irritability is not a unitary construct. Future work should investigate and model different aspects of irritability, which might be more accessible to specific informants.