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Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. We demonstrate that task and rest periods lowered participants' mood, an effect we call "Mood Drift Over Time." This finding was replicated in 19 cohorts totaling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 minutes of rest, Cohen's d=0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

Impairing emotional outbursts, defined by extreme anger or distress in response to relatively ordinary frustrations and disappointments, impact all mental health care systems, emergency departments, schools, and juvenile justice programs. However, the prevalence, outcome, and impact of outbursts are difficult to quantify because they are transdiagnostic and not explicitly defined by current diagnostic nosology. Research variably addresses outbursts under the rubrics of tantrums, anger, irritability, aggression, rage attacks, or emotional and behavioral dysregulation. Consistent methods for identifying and assessing impairing emotional outbursts across development or systems of care are lacking.The American Academy of Child and Adolescent Psychiatry Presidential Task Force (2019-2021) conducted a narrative review addressing impairing emotional outbursts within the limitations of the existing literature and independent of diagnosis.Extrapolating from the existing literature, best estimates suggest that outbursts occur in 4%-10% of community children (preschoolers through adolescents). Impairing emotional outbursts may respond to successful treatment of the primary disorder, especially for some children with attention-deficit/hyperactivity disorder whose medications have been optimized. However, outbursts are generally multi-determined and often represent maladaptive or deficient coping strategies and responses.Evidence-based strategies are necessary to address factors that trigger, reinforce, or excuse the behaviors and to enhance problem-solving skills. Currently available interventions yield only modest effect sizes for treatment effect. More specific definitions and measures are needed to track and quantify outbursts and to design and assess the effectiveness of interventions. Better treatments are clearly needed.

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.

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.

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 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.

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.

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.

Background The COVID-19 pandemic has affected all our lives, not only through the infection itself but also through the measures taken to control the spread of the virus (e.g. lockdown). Aims Here, we investigated how the COVID-19 pandemic and unprecedented lockdown affected the mental health of young adults in England and Wales. Method We compared the mental health symptoms of up to 4773 twins in their mid-20s in 2018 prior to the COVID-19 pandemic (T1) and during four-wave longitudinal data collection during the pandemic in April, July and October 2020, and in March 2021 (T2–T5) using phenotypic and genetic longitudinal designs. Results The average changes in mental health were small to medium and mainly occurred from T1 to T2 (average Cohen d = 0.14). Despite the expectation of catastrophic effects of the pandemic on mental health, we did not observe trends in worsening mental health during the pandemic (T3–T5). Young people with pre-existing mental health problems were disproportionately affected at the beginning of the pandemic, but their increased problems largely subsided as the pandemic persisted. Twin analyses indicated that the aetiology of individual differences in mental health symptoms did not change during the lockdown (average heritability 33%); the average genetic correlation between T1 and T2–T5 was 0.95, indicating that genetic effects before the pandemic were substantially correlated with genetic effects up to a year later. Conclusions We conclude that on average the mental health of young adults in England and Wales has been remarkably resilient to the effects of the pandemic and associated lockdown.

The COVID-19 pandemic and its social and economic consequences have had adverse impacts on physical and mental health worldwide and exposed all segments of the population to protracted uncertainty and daily disruptions. The CoRonavIruS health and Impact Survey (CRISIS) was developed for use as an easy to implement and robust questionnaire covering key domains relevant to mental distress and resilience during the pandemic. Ongoing studies using CRISIS include international studies of COVID-related ill health conducted during different phases of the pandemic and follow-up studies of cohorts characterized before the COVID pandemic. In the current work, we demonstrate the feasibility, psychometric structure, and construct validity of this survey. We then show that pre-existing mood states, perceived COVID risk, and lifestyle changes are strongly associated with negative mood states during the pandemic in population samples of adults and in parents reporting on their children in the US and UK. These findings are highly reproducible and we find a high degree of consistency in the power of these factors to predict mental health during the pandemic.

The Affective Reactivity Index (ARI) is an irritability measure with good psychometric properties. However, there are no published studies in preschool children, an important population in which to differentiate normative from non-normative irritability. The goal of this study was to validate the ARI in preschoolers. Two samples were included: a school-based sample (N = 487, mean age = 57.80 ± 7.23 months, 52.8% male) and a clinical sample of children with Attention Deficit Hyperactivity Disorder (ADHD; N = 153, mean age = 60.5 ± 7.6 months, 83.7% males). Confirmatory factor analysis assessed ARI unidimensionality. ARI criterion validity was tested through comparison to other scales measuring irritability, related constructs, and other aspects of psychopathology. Test–retest reliability was assessed in the school-based sample. Analyses confirmed a single-factor structure and good internal consistency. The ARI showed stronger correlations with irritability measures than with measures of other constructs. In the clinical sample, ADHD children with comorbid disruptive behavior disorders had higher ARI scores than those without this comorbidity. In the school-based sample, test–retest reliability was moderate. This is the first study to demonstrate ARI validity and reliability in preschoolers. The scale performed well in both school-based and clinical samples. Having a concise and validated irritability measure for preschoolers may facilitate both clinical assessment and research on early irritability.

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health’s Research Domain Criteria (RDoC). Following RDoC’s integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that a) are developmentally stable over years during adolescence and b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ∼11k) and combined MRI data across modalities (task-fMRI from three tasks, resting-state fMRI, structural MRI, DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across two years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of ‘opportunistic stacking’ allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated=18.65% [17.29%-20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated=15.6% [11%-20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC’s integrative framework.Key PointsUsing opportunistic stacking and multimodal MRI, we developed brain-based predictive models for children’s cognitive abilities that were longitudinally stable, generalisable to different sites and robust against missing data.Our brain-based models were able to partially mediate the relationships of childhood cognitive abilities with the socio-demographic, psychological and genetic factors.Our approach should pave the way for future researchers to employ multimodal MRI as a tool for the brain-based indicator of cognitive abilities, according to the integrative RDoC framework.

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman’s H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Anhedonia reflects a reduced ability to engage in previously pleasurable activities and has been reported in children as young as 3 years of age. It manifests early and is a strong predictor of psychiatric disease onset and progression over the course of development and into adulthood. However, little is known about its mechanistic origins, particularly in childhood and adolescence. In this chapter, we provide a socio-cognitive model of the development of anhedonia. This model is substantiated by past literature presented in this chapter to account for how the individual trajectories of emotion knowledge, autobiographical memory, and self-concept representations contribute to the onset, persistence, and progression of anhedonia from early childhood through adolescence.

Research in adolescent depression has found aberrant intrinsic functional connectivity (iFC) among the ventral striatum (VS) and several brain regions implicated in reward processing. The present study probes this question by taking advantage of the availability of data from a large youth cohort, the IMAGEN Consortium.iFC data from 303 adolescents (48% of them female) were used to examine associations of VS connectivity at baseline (at age 14) with depressive disorders at baseline and at 2-year (N=250) and 4-year (N=219) follow-ups. Eleven regions of interest, key nodes of the reward system, were used to probe the reward network and calculate the connectivity strength of the VS within this network (VS connectivityHigher right VS connectivityThe connectivity strength of the VS within the reward network showed distinct patterns of association with depressive disorders and anhedonia from mid to late adolescence, suggesting that the role of this circuitry in depression changes with age. This study replicates, in an independent sample, the association between the VS and depression previously reported in younger adolescents. The findings suggest a role of VS connectivity

BackgroundAdolescent onset of depression is associated with long-lasting negative consequences. Identifying adolescents at risk for developing depression would enable the monitoring of risk factors and the development of early intervention strategies. Using machine learning to combine several risk factors from multiple modalities might allow prediction of depression onset at the individual level. MethodsA subsample of a multisite longitudinal study in adolescents, the IMAGEN study, was used to predict future (subthreshold) major depressive disorder onset in healthy adolescents. Based on 2-year and 5-year follow-up data, participants were grouped into the following: 1) those developing a diagnosis of major depressive disorder or subthreshold major depressive disorder and 2) healthy control subjects. Baseline measurements of 145 variables from different modalities (clinical, cognitive, environmental, and structural magnetic resonance imaging) at age 14 years were used as input to penalized logistic regression (with different levels of penalization) to predict depression onset in a training dataset (n = 407). The features contributing the highest to the prediction were validated in an independent hold-out sample (three independent IMAGEN sites; n = 137). ResultsThe area under the receiver operating characteristic curve for predicting depression onset ranged between 0.70 and 0.72 in the training dataset. Baseline severity of depressive symptoms, female sex, neuroticism, stressful life events, and surface area of the supramarginal gyrus contributed most to the predictive model and predicted onset of depression, with an area under the receiver operating characteristic curve between 0.68 and 0.72 in the independent validation sample. ConclusionsThis study showed that depression onset in adolescents can be predicted based on a combination multimodal data of clinical characteristics, life events, personality traits, and brain structure variables.

Objective Irritable mood, a common and impairing symptom in psychopathology, has been proposed to underlie the developmental link between oppositional problems in youth and depression in adulthood. We examined the neural correlates of adolescent irritability in IMAGEN, a sample of 2,024 14-year-old adolescents from 5 European countries. Method The Development and Well-Being Assessment (DAWBA) was used to assess attention-deficit/hyperactivity disorder, major depressive disorder, oppositional defiant disorder, and generalized anxiety disorder. Three items from the DAWBA, selected as close matches to the Affective Reactivity Index, were used to assess irritability. Structural magnetic resonance imaging was examined using whole-brain voxel-based morphometry analysis, and functional magnetic resonance imaging was examined during a stop signal task of inhibitory control. Imaging data were included in structural equation models to examine the direct and indirect associations between irritable mood and comorbid DSM diagnoses. Results Whole-brain voxelwise analysis showed that adolescent irritable mood was associated with less gray matter volume and less neural activation underlying inhibitory control in frontal and temporal cortical areas (cluster-correction at p Conclusion This study identifies adolescent irritability as an independent construct and points to a neurobiological correlate to irritability that is an important contributing feature to many psychopathological disorders.

Family accommodation (FA) refers to the participation of family members in obsessive-compulsive disorder (OCD) rituals. Most studies have focused on maternal accommodation; consequently, little is known about fathers’ accommodation of OCD. The current study aims to extend the existing literature by examining maternal versus paternal accommodation of OCD symptoms. The sample consisted of 209 children with OCD (Mean [M] age = 14.1 years) and their parents (NMothers = 209, NFathers = 209) who had completed the Family Accommodation Scale- Parent Report (FAS-PR). Paired t-test and chi-square analyses were used to compare FA of OCD symptoms between mothers and fathers. Linear regression was used to examine correlates of maternal and paternal FA and its impact on treatment outcomes. Mothers reported significantly higher levels of daily FA than fathers. Correlates of maternal and paternal accommodation included OCD symptom severity, emotional and behavioral difficulties, and parent psychopathology. Both maternal and paternal FA significantly predicted worse treatment outcomes. Both mothers and fathers accommodate child OCD symptoms with high frequency, and in similar ways. Although mothers accommodate to a greater extent than fathers, both maternal and paternal involvement in rituals are a significant predictor of the child’s treatment response. Results emphasise the need to consider the whole family system, including fathers, in understanding and treating OCD in children. Electronic supplementary material The online version of this article (10.1007/s10802-020-00678-9) contains supplementary material, which is available to authorized users.

Objective Despite the clinical importance of chronic and severe irritability, there is a paucity of controlled trials for its pharmacological treatment. Here, we examine the effects of adding citalopram (CTP) to methylphenidate (MPH) in the treatment of chronic severe irritability in youth using a double-blind randomized placebo-controlled design. Method After a lead-in phase of open treatment with stimulant, 53 youth meeting criteria for severe mood dysregulation (SMD) were randomly assigned to receive CTP or placebo (PBO) for 8 weeks. A total of 49 participants, 48 of them (98%) meeting disruptive mood dysregulation disorder (DMDD) criteria, were included in the intent-to-treat analysis. The primary outcome measure was the proportion of response based on improvements of irritability at the week 8 of the trial. Results At the end of the trial, a significantly higher proportion of response was seen in those participants randomly assigned to CTP+MPH compared to PBO+MPH (35% CTP+MPH versus 6% PBO+MPH; odds ratio = 11.70, 95% CI = 2.00−68.16, p = 0.006). However, there were no differences in functional impairment between groups at the end of the trial. No differences were found in any adverse effect between treatment groups, and no trial participant exhibited hypomanic or manic symptoms. Conclusion Adjunctive CTP might be efficacious in the treatment of chronic severe irritability in youth resistant to stimulant treatment alone. Clinical trial registration information A Controlled Trial of Serotonin Reuptake Inhibitors Added to Stimulant Medication in Youth With Severe Mood Dysregulation; https://clinicaltrials.gov ; NCT00794040 .

Recent longitudinal studies in youth have reported MRI correlates of prospective anxiety symptoms during adolescence, a vulnerable period for the onset of anxiety disorders. However, their predictive value has not been established. Individual prediction through machine-learning algorithms might help bridge the gap to clinical relevance. A voting classifier with Random Forest, Support Vector Machine and Logistic Regression algorithms was used to evaluate the predictive pertinence of gray matter volumes of interest and psychometric scores in the detection of prospective clinical anxiety. Participants with clinical anxiety at age 18-23 (N = 156) were investigated at age 14 along with healthy controls (N = 424). Shapley values were extracted for in-depth interpretation of feature importance. Prospective prediction of pooled anxiety disorders relied mostly on psychometric features and achieved moderate performance (area under the receiver operating curve = 0.68), while generalized anxiety disorder (GAD) prediction achieved similar performance. MRI regional volumes did not improve the prediction performance of prospective pooled anxiety disorders with respect to psychometric features alone, but they improved the prediction performance of GAD, with the caudate and pallidum volumes being among the most contributing features. To conclude, in non-anxious 14 year old adolescents, future clinical anxiety onset 4-8 years later could be individually predicted. Psychometric features such as neuroticism, hopelessness and emotional symptoms were the main contributors to pooled anxiety disorders prediction. Neuroanatomical data, such as caudate and pallidum volume, proved valuable for GAD and should be included in prospective clinical anxiety prediction in adolescents.

According to influential theories about mood, exposure to environments characterized by specific patterns of punishments and rewards could shape mood response to future stimuli. This raises the intriguing possibility that mood could be trained by exposure to controlled environments. The aim of the present study is to investigate experimental settings that increase resilience of mood to negative stimuli. We developed a new task where participants register their mood when rewards are added or subtracted from their score. We examined sixteen experimental environments with different mood-reward relationships. In an exploratory experiment, we identified environments that produce the greatest improvements in mood resilience, as measured by decreased sensitivity to loss. In a next step, we replicated our results around mood training and tested mood’s resilience over time in a novel sample. We found that training effects are detectable and remain significant after multiple task rounds (approximately two minutes). These findings are a first step in our effort to better understand the mechanisms behind mood training and its potential clinical utility.

Background Ketamine as an antidepressant improves anhedonia as early as 2 hours after infusion. These drug effects are thought to be exerted via actions on reward-related brain areas—yet these actions remain largely unknown. Our study investigates ketamine’s effects during the anticipation and receipt of an expected reward, after the psychotomimetic effects of ketamine have passed, when early antidepressant effects are reported. Methods We examined ketamine’s effects during the anticipation and receipt of expected rewards on predefined brain areas, namely, the dorsal and ventral striatum, ventral tegmental area, amygdala, and insula. We recruited 37 male and female participants with remitted depression who were free from symptoms and antidepressant treatments at the time of the scan. Participants were scanned 2 hours after drug administration in a double-blind crossover design (ketamine: 0.5 mg/kg and placebo) while performing a monetary reward task. Results A significant main effect of ketamine was observed across all regions of interest during the anticipation and feedback phases of win and no-win trials. The drug effects were particularly prominent in the nucleus accumbens and putamen, which showed increased activation on the receipt of smaller rewards compared with neutral. The levels of (2R,6R)-hydroxynorketamine 2 hours after infusion significantly correlated with the activation observed in the ventral tegmental area for that contrast. Conclusions These findings demonstrate that ketamine can produce detectable changes in reward-related brain areas 2 hours after infusion, which occur without symptom changes and support the idea that ketamine might improve reward-related symptoms via modulation of response to feedback.

To investigate whether, compared to pre-pandemic levels, depressive and anxiety symptoms in adolescents with depression increased during the pandemic.We used data from National Institute of Mental Health Characterization and Treatment of Depression (NIMH CAT-D) cohort, a longitudinal case-control study that started pre-pandemic. Most of the participants are from the states of Maryland and Virginia in the United States. We compared depressive symptoms (1,820 measurements; 519 measurements pre-pandemic and 1,302 during the pandemic) and anxiety symptoms (1,800 measurements; 508 measurements pre-pandemic and 1,292 ratings during the pandemic) of 166 adolescents (109 girls, 96 adolescents with depression) before and during the pandemic. Data were collected during yearly clinical visits, interim 4-month follow-up visits, inpatient stays, and weekly outpatient sessions, with additional data collection during the pandemic. Pre-pandemic, healthy volunteers (HVs) had a median of 1 depressive and anxiety rating (range, 1-3), and adolescents with depression had a median of 2 ratings (anxiety rating range, 1-25; depressive rating range, 1-26). During the pandemic, HVs had a median of 8 anxiety ratings and 9 depressive ratings (range, 1-13), and adolescents with depression had a median of 7 anxiety and depressive ratings (range, 1-29). We also analyzed adolescent- and parent-reported behaviors in the CoRonavIruS Health Impact Survey (CRISIS), totaling 920 self-reported measures for 164 adolescents (112 girls, 92 adolescents with depression). HVs had a median of 7 surveys (range, 1-8), and adolescents with depression had a median of 5 surveys (range, 1-8).Pre-pandemic, adolescents with depression had a mean depressive score of 11.16 (95% CI = 10.10, 12.22) and HVs had a mean depressive score of 1.76 (95% CI = 0.40, 3.13), a difference of 9.40 points (95% CI = 7.78, 11.01). During the pandemic, this difference decreased by 22.6% (2.05 points, 95% CI = 0.71, 3.40, p = .003) due to 0.89 points decrease in severity of scores in adolescents with depression (95% CI = 0.08, 1.70, p = .032) and 1.16 points increase in HVs' depressive symptoms (95% CI = 0.10, 2.23, p = .032). Compared to their pre-pandemic levels, adolescents with depression reported overall lower anxiety symptoms during the pandemic. Parent-on-child reports also were consistent with these results.Contrary to our hypothesis, we found that both depressive and anxiety symptoms were lower for adolescents with depression during the pandemic compared to before. In contrast, the depression scores for the HVs were higher during the pandemic relative to their pre-pandemic ratings; these scores remained much lower than those of adolescents with depression.Characterization and Treatment of Adolescent Depression; https://clinicaltrials.gov/; NCT03388606.

Objective: To examine the impact of COVID-19 restrictions among children with attention-deficit/hyperactivity disorder (ADHD). Methods: Parents of 213 Australian children (5–17 years) with ADHD completed a survey in May 2020 when COVID-19 restrictions were in place (i.e., requiring citizens to stay at home except for essential reasons). Results: Compared to pre-pandemic, children had less exercise (Odds Ratio (OR) = 0.4; 95% CI 0.3–0.6), less outdoor time (OR = 0.4; 95% 0.3–0.6), and less enjoyment in activities (OR = 6.5; 95% CI 4.0–10.4), while television (OR = 4.0; 95% CI 2.5–6.5), social media (OR = 2.4; 95% CI 1.3–4.5), gaming (OR = 2.0; 95% CI 1.3–3.0), sad/depressed mood (OR = 1.8; 95% CI 1.2–2.8), and loneliness (OR = 3.6; 95% CI 2.3–5.5) were increased. Child stress about COVID-19 restrictions was associated with poorer functioning across most domains. Most parents (64%) reported positive changes for their child including more family time. Conclusions: COVID-19 restrictions were associated with both negative and positive impacts among children with ADHD.

The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that affected the lifestyle and economy. Various studies have been focused on the identification of COVID-19 impact to mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the change of mood state of children and adolescents during the first lockdown. Therefore, to this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best performing model and (v) a post-hoc interpretation of the features’ impact on the best performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of COVID-19 pandemic on the mood state of children and adolescents.

Capturing individual differences in cognition is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability can be improved by integrating MRI signals across the whole brain and across modalities, including task-based functional MRI (tfMRI) of different tasks along with other non-task MRI modalities, such as structural MRI, resting-state functional connectivity. Using the Human Connectome Project (n=873, 473 females, after quality control), we directly compared predictive models comprising different sets of MRI modalities (e.g., seven tasks vs. non-task modalities). We applied two approaches to integrate multimodal MRI, stacked vs. flat models, and implemented 16 combinations of machine-learning algorithms. The stacked model integrating all modalities via stacking Elastic Net provided the best prediction (r=.57), relatively to other models tested, as well as excellent test-retest reliability (ICC=~.85) in capturing general cognitive abilities. Importantly, compared to the stacked model integrating across non-task modalities (r=.27), the stacked model integrating tfMRI across tasks led to significantly higher prediction (r=.56) while still providing excellent test-retest reliability (ICC=~.83). The stacked model integrating tfMRI across tasks was driven by frontal and parietal areas and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results contradict the recently popular notion that tfMRI is not reliable enough to capture individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.HighlightsNon-task MRI (sMRI, rs-fMRI) are often used for the brain-cognition relationship.Task-based fMRI has been deemed unreliable for capturing individual differences.We tested if drawing task-based fMRI information across regions/tasks improves prediction and reliability of the brain-cognition relationship.Our approach boosts prediction of task-based fMRI over non-task MRI.Our approach renders task-based fMRI reliable over time.Our approach shows the importance of the fronto-parietal areas in cognition.