COVID-19 pandemic is a global calamity posing an unprecedented opportunity to study resilience. We developed a brief resilience survey probing self-reliance, emotion-regulation, ...interpersonal-relationship patterns and neighborhood-environment, and applied it online during the acute COVID-19 outbreak (April 6-15, 2020), on a crowdsourcing research website ( www.covid19resilience.org ) advertised through social media. We evaluated level of stress (worries) regarding COVID-19: (1) contracting, (2) dying from, (3) currently having, (4) family member contracting, (5) unknowingly infecting others with (6) experiencing significant financial burden following. Anxiety (GAD7) and depression (PHQ2) were measured. Totally, 3042 participants (n = 1964 females, age range 18-79, mean age = 39) completed the resilience and COVID-19-related stress survey and 1350 of them (mean age = 41, SD = 13; n = 997 females) completed GAD7 and PHQ2. Participants significantly endorsed more distress about family contracting COVID-19 (48.5%) and unknowingly infecting others (36%), than getting COVID-19 themselves (19.9%), p < 0.0005 covarying for demographics and proxy COVID-19 exposures like getting tested and knowing infected individuals. Patterns of COVID-19 related worries, rates of anxiety (GAD7 > 10, 22.2%) and depression (PHQ2 > 2, 16.1%) did not differ between healthcare providers and non-healthcare providers. Higher resilience scores were associated with lower COVID-19 related worries (main effect F
= 134.9; p < 0.00001, covarying for confounders). Increase in 1 SD on resilience score was associated with reduced rate of anxiety (65%) and depression (69%), across healthcare and non-healthcare professionals. Findings provide empirical evidence on mental health associated with COVID-19 outbreak in a large convenience sample, setting a stage for longitudinal studies evaluating mental health trajectories following COVID-19 pandemic.
The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures ...(typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman Schmid & Leiman, 1957 and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.
Neurobiological abnormalities associated with psychiatric disorders do not map well to existing diagnostic categories. High co-morbidity suggests dimensional circuit-level abnormalities that cross ...diagnoses. Here we seek to identify brain-based dimensions of psychopathology using sparse canonical correlation analysis in a sample of 663 youths. This analysis reveals correlated patterns of functional connectivity and psychiatric symptoms. We find that four dimensions of psychopathology - mood, psychosis, fear, and externalizing behavior - are associated (r = 0.68-0.71) with distinct patterns of connectivity. Loss of network segregation between the default mode network and executive networks emerges as a common feature across all dimensions. Connectivity linked to mood and psychosis becomes more prominent with development, and sex differences are present for connectivity related to mood and fear. Critically, findings largely replicate in an independent dataset (n = 336). These results delineate connectivity-guided dimensions of psychopathology that cross clinical diagnostic categories, which could serve as a foundation for developing network-based biomarkers in psychiatry.
Summary
The coronavirus disease 2019 (COVID‐19) pandemic resulted in significant increases in insomnia, with up to 60% of people reporting increased insomnia. However, it is unclear whether exposure ...to risk factors for the virus or worries about COVID‐19 are more strongly associated with insomnia. Using a three‐part survey over the course of the first 6 months of the pandemic, we evaluated associations between COVID‐19 exposures, COVID‐19 worries, and insomnia. We hypothesised that COVID‐19‐related worries and exposure to risk of COVID‐19 would predict increases in insomnia. Participants (N = 3,560) completed a survey at three time‐points indicating their exposures to COVID‐19 risk factors, COVID‐19‐related worries, and insomnia. COVID‐19 worry variables were consistently associated with greater insomnia severity, whereas COVID‐19 exposure variables were not. COVID‐19 worries decreased significantly over time, and there were significant interactions between change in COVID‐19 worries and change in insomnia severity over time. Individuals who experienced increases in COVID‐19 worries also experienced increases in insomnia severity. Changes in worry during the COVID‐19 pandemic were associated with changes in insomnia; worries about COVID‐19 were a more consistent predictor of insomnia than COVID‐19 exposures. Evidence‐based treatments targeting virus‐related worries may improve insomnia during this and future calamities.
Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report ...disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs FPN, dorsal attention network (DAN), and cingulo-opercular network (CON), (ii) higher within-network connectivity in two intrinsic networks DMN and salience network (SAN), and (iii) higher within-network connectivity in two sensory networks sensorimotor network (SMN) and visual network (VIS). Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.
The human brain is organized into large-scale functional modules that have been shown to evolve in childhood and adolescence. However, it remains unknown whether the underlying white matter ...architecture is similarly refined during development, potentially allowing for improvements in executive function. In a sample of 882 participants (ages 8–22) who underwent diffusion imaging as part of the Philadelphia Neurodevelopmental Cohort, we demonstrate that structural network modules become more segregated with age, with weaker connections between modules and stronger connections within modules. Evolving modular topology facilitates global network efficiency and is driven by age-related strengthening of hub edges present both within and between modules. Critically, both modular segregation and network efficiency are associated with enhanced executive performance and mediate the improvement of executive functioning with age. Together, results delineate a process of structural network maturation that supports executive function in youth.
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•Structural brain modules become more segregated during youth•Targeted strengthening of hub edges simultaneously promotes network efficiency•Enhanced modular segregation mediates improvements in executive function in youth
Baum et al. apply network analytic techniques to demonstrate that human white matter networks become increasingly modular during adolescent development. Furthermore, targeted strengthening of hub connections facilitates global network integration. This process of network evolution mediates improvements in executive functioning during youth.
There is an ongoing revolution in psychology and psychiatry that will likely change how we conceptualize, study and treat psychological problems. Many theorists now support viewing psychopathology ...as consisting of continuous dimensions rather than discrete diagnostic categories. Indeed, recent papers have proposed comprehensive taxonomies of psychopathology dimensions to replace the DSM and ICD taxonomies of categories. The proposed dimensional taxonomies, which portray psychopathology as hierarchically organized correlated dimensions, are now well supported at phenotypic levels. Multiple studies show that both a general factor of psychopathology at the top of the hierarchy and specific factors at lower levels predict different functional outcomes. Our analyses of data on a large representative sample of child and adolescent twins suggested the causal hypothesis that phenotypic correlations among dimensions of psychopathology are the result of many familial influences being pleiotropic. That is, most genetic variants and shared environmental factors are hypothesized to non‐specifically influence risk for multiple rather than individual dimensions of psychopathology. In contrast, person‐specific experiences tend to be related to individual dimensions. This hierarchical causal hypothesis has been supported by both large‐scale family and molecular genetic studies. Current research focuses on three issues. First, the field has not settled on a preferred statistical model for studying the hierarchy of causes and phenotypes. Second, in spite of encouraging progress, the neurobiological correlates of the hierarchy of dimensions of psychopathology are only partially described. Third, although there are potentially important clinical implications of the hierarchical model, insufficient research has been conducted to date to recommend evidence‐based clinical practices.
Objective: The Penn Computerized Neurocognitive Battery (CNB) was designed to measure performance accuracy and speed on specific neurobehavioral domains using tests that were previously validated ...with functional neuroimaging. The goal of the present study was to evaluate the neuropsychological theory used to construct the CNB by confirming the factor structure of the tests composing it. Method: In a large community sample (N = 9,138; age range 8-21), we performed a correlated-traits confirmatory factor analysis (CFA) and multiple exploratory factor analyses (EFAs) on the 12 CNB measures of Efficiency (which combine Accuracy and Speed). We then performed EFAs of the Accuracy and Speed measures separately. Finally, we performed a confirmatory bifactor analysis of the Efficiency scores. All analyses were performed with Mplus using maximum likelihood estimation. Results: Results strongly support the a priori theory used to construct the CNB, showing that tests designed to measure executive, episodic memory, complex cognition, and social cognition aggregate their loadings within these domains. When Accuracy and Speed were analyzed separately, Accuracy produced 3 reliable factors: executive and complex cognition, episodic memory, and social cognition, while speed produced 2 factors: tests that require fast responses and those where each item requires deliberation. The statistical "Fit" of almost all models described above was acceptable (usually excellent). Conclusions: Based on the analysis from these large-scale data, the CNB offers an effective means for measuring the integrity of intended neurocognitive domains in about 1 hour of testing and is thus suitable for large-scale clinical and genomic studies.
IMPORTANCE: Substantial shifts in perception and policy regarding cannabis have recently occurred, with use of cannabis increasing while its perceived harm decreases. One possible risk of increased ...cannabis use is poorer cognitive functioning, especially in youth. OBJECTIVE: To provide the first quantitative synthesis of the literature examining cannabis and cognitive functioning in adolescents and young adults (with a mean age of 26 years and younger). DATA SOURCES: PubMed, PsycInfo, Academic Search Premier, Scopus, and bibliographies of relevant reviews were searched for peer-reviewed, English-language studies from the date the databases began through May 2017. STUDY SELECTION: Consensus criteria were used to determine study inclusion through abstract and manuscript review. DATA EXTRACTION AND SYNTHESIS: This study followed Meta-analysis of Observational Studies in Epidemiology guidelines. Effect size estimates were calculated using multivariate mixed-effects models for cognitive functioning outcomes classified into 10 domains. MAIN OUTCOMES AND MEASURES: Results from neurocognitive tests administered in cross-sectional studies were primary outcomes, and we examined the influence of a priori explanatory variables on variability in effect size. RESULTS: Sixty-nine studies of 2152 cannabis users (mean SD age, 20.6 2.8 years; 1472 68.4% male) and 6575 comparison participants with minimal cannabis exposure were included (mean SD age, 20.8 3.4; 3669 55.8% male). Results indicated a small overall effect size (presented as mean d) for reduced cognitive functioning associated with frequent or heavy cannabis use (d, −0.25; 95% CI, −0.32 to −0.17; P < .001). The magnitude of effect sizes did not vary by sample age or age at cannabis use onset. However, studies requiring an abstinence period longer than 72 hours (15 studies; n = 928) had an overall effect size (d, −0.08; 95% CI, −0.22 to 0.07) that was not significantly different from 0 and smaller than studies with less stringent abstinence criteria (54 studies; n = 7799; d, −0.30; 95% CI, −0.37 to −0.22; P = .01). CONCLUSIONS AND RELEVANCE: Associations between cannabis use and cognitive functioning in cross-sectional studies of adolescents and young adults are small and may be of questionable clinical importance for most individuals. Furthermore, abstinence of longer than 72 hours diminishes cognitive deficits associated with cannabis use. Although other outcomes (eg, psychosis) were not examined in the included studies, results indicate that previous studies of cannabis in youth may have overstated the magnitude and persistence of cognitive deficits associated with use. Reported deficits may reflect residual effects from acute use or withdrawal. Future studies should examine individual differences in susceptibility to cannabis-associated cognitive dysfunction.
•COVID-19 has major ramifications for both global economy and mental health.•We studied the link between finances and mental health over a month course.•Two independent studies were conducted in the ...US and Israel.•Financial loss was associated significantly more with depression than with anxiety.•Worsening of income loss was associated with exacerbation of depressive symptoms.
COVID-19 pandemic has major ramifications for global health and economy, with growing concerns about economic recession and implications for mental health. Here we investigated the associations between pandemic-related income loss with financial strain and mental health trajectories over a 1-month course, in two independent cohorts.
Two independent studies were conducted in the U.S and in Israel at the beginning of the outbreak (March-April 2020, T1; study I: N = 2904, study II: N = 1267) and at a 1-month follow-up (T2; study I: N = 1318, study II: N = 241). Mixed-effects models were applied to assess associations among COVID-19-related income loss, financial strain, and pandemic-related worries about health, with anxiety and depression, controlling for multiple covariates including pre-COVID-19 income.
In both studies, income loss and financial strain were associated with greater depressive symptoms at T1, above and beyond T1 anxiety, worries about health, and pre-COVID-19 income. Worsening of income loss was associated with exacerbation of depression at T2 in both studies. Worsening of subjective financial strain was associated with exacerbation of depression at T2 in one study (US).
Income loss and financial strain were uniquely associated with depressive symptoms and their exacerbation over time, above and beyond pandemic-related anxiety. In times when a myriad of stressors are affecting mental health worldwide, our findings reveal specific links between the economic impact of COVID-19 and psychiatric outcomes.
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