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 and target bifactor rotations) and confirmatory bifactor modeling, c) differentiate between bifactor and second-order models, 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 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. KeywordsBifactor model; structural equation model; target rotations; Schmid-Leiman; Multidimensionality Chen, West, and Sousa (2006, p. 189) write, "Researchers interested in assessing a construct often hypothesize that several highly related domains comprise the general construct of interest." As a consequence, factor analytic evaluations of such measures often reveal some evidence of a general factor running through the items (e.g., a relatively large first eigenvalue) but also some evidence of multidimensionality (e.g., an interpretable multidimensional solution that arises due to parcels of items that tap similar content domains). These common findings invariably spark the age-old debate among researchers whether a given construct is unitary or Address correspondence to: Steven P. Reise, Ph.D., Department of Psychology, Franz Hall, UCLA, Los Angeles, CA 90095. NIH Public AccessAuthor Manuscript J Pers Assess. Author manuscript; available in PMC 2011 November 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript multi-faceted. Does scale score variation primarily reflect variation on a single construct (and, thus, scale scores are unambiguously interpretable) or reflect multiple non-ignorable so...

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 1,3054 = 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 nonhealthcare 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.

To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress–related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

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. A crucial step in determining whether the CNB has attained its objective is to assess its factor structure. 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 = 9138; age range 8-21), we performed a correlated-traits confirmatory factor analysis (CFA) and multiple exploratory factor analyses (EFA’s) on the twelve CNB measures of Efficiency (which combine Accuracy and Speed). To further explore the measures contributing to Efficiency, we then performed EFA’s 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 three reliable factors: executive and complex cognition, episodic memory and social cognition, while speed produced two factors: tests that require fast responses and those where each item requires deliberation. The interpretability and statistical “Fit” of almost all models described above was acceptable (usually excellent). Conclusions Based on the well powered analysis from these large scale data, the CNB offers an effective means for measuring the integrity of intended neurocognitive domains in about one hour of testing and is thus suitable for large-scale clinical and genomic studies.

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 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.

Background An integrative multidisciplinary approach is required to elucidate the multiple factors that shape neurodevelopmental trajectories of mental disorders. The Philadelphia Neurodevelopmental Cohort (PNC), funded by the National Institute of Mental Health Grand Opportunity (GO) mechanism of the American Recovery and Reinvestment Act, was designed to characterize clinical and neurobehavioral phenotypes of genotyped youths. Data generated, which are recently available through the NIMH Database of Genotypes and Phenotypes (dbGaP), have garnered considerable interest. We provide an overview of PNC recruitment and clinical assessment methods to allow informed use and interpretation of the PNC resource by the scientific community. We also evaluate the structure of the assessment tools and their criterion validity. Methods Participants were recruited from a large pool of youths (n=13,958) previously identified and genotyped at The Children's Hospital of Philadelphia. A comprehensive computerized tool for structured evaluation of psychopathology domains (GOASSESS) was constructed. We administered GOASSESS to all participants and used factor analysis to evaluate its structure. Results A total of 9,498 youths (ages 8-21; mean age=14.2; European-American=55.8%; African-American=32.9%; Other=11.4%) were enrolled. Factor analysis revealed a strong general psychopathology factor, and specific ‘anxious-misery’, ‘fear’ and ‘behavior’ factors. The ‘behavior’ factor had a small negative correlation (−0.21) with overall accuracy of neurocognitive performance, particularly in tests of executive and complex reasoning. Being female had a high association with the ‘anxious-misery’ and low association with the ‘behavior’ factors. The psychosis spectrum was also best characterized by a general factor and three specific factors: ideas about ‘special abilities/persecution,’ ‘unusual thoughts/perceptions,’ and ‘negative/disorganized’ symptoms. Conclusions The PNC assessment mechanism yielded psychopathology data with strong factorial validity in a large diverse community cohort of genotyped youths. Factor scores should be useful for dimensional integration with other modalities (neuroimaging, genomics). Thus, PNC public domain resources can advance understanding of complex inter-relationships among genes, cognition, brain and behavior involved in neurodevelopment of common mental disorders.

The protracted development of structural and functional brain connectivity within distributed association networks coincides with improvements in higher-order cognitive processes such as executive function. However, it remains unclear how white-matter architecture develops during youth to directly support coordinated neural activity. Here, we characterize the development of structure–function coupling using diffusion-weighted imaging andn-back functional MRI data in a sample of 727 individuals (ages 8 to 23 y). We found that spatial variability in structure–function coupling aligned with cortical hierarchies of functional specialization and evolutionary expansion. Furthermore, hierarchy-dependent age effects on structure–function coupling localized to transmodal cortex in both cross-sectional data and a subset of participants with longitudinal data (n= 294). Moreover, structure–function coupling in rostrolateral prefrontal cortex was associated with executive performance and partially mediated age-related improvements in executive function. Together, these findings delineate a critical dimension of adolescent brain development, whereby the coupling between structural and functional connectivity remodels to support functional specialization and cognition.