Brain signal variability and executive functions across the life span

Neural variability is thought to facilitate survival through flexible adaptation to changing environmental demands. In humans, such capacity for flexible adaptation may manifest as fluid reasoning, inhibition of automatic responses, and mental set-switching-skills falling under the broad domain of executive functions that fluctuate over the life span. Neural variability can be quantified via the BOLD signal in resting-state fMRI. Variability of large-scale brain networks is posited to underpin complex cognitive activities requiring interactions between multiple brain regions. Few studies have examined the extent to which network-level brain signal variability across the life span maps onto high-level processes under the umbrella of executive functions. The present study leveraged a large publicly available neuroimaging dataset to investigate the relationship between signal variability and executive functions across the life span. Associations between brain signal variability and executive functions shifted as a function of age. Limbic-specific variability was consistently associated with greater performance across subcomponents of executive functions. Associations between executive function subcomponents and network-level variability of the default mode and central executive networks, as well as whole-brain variability, varied across the life span. Findings suggest that brain signal variability may help to explain to age-related differences in executive functions across the life span.

Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology

In the current paper, we review existing tools for solving variable selection problems in psychology. Modern regularization methods such as lasso regression have recently been introduced in the field and are incorporated into popular methodologies, such as network analysis. However, several recognized limitations of lasso regularization may limit its suitability for psychological research. In this paper, we compare the properties of lasso approaches used for variable selection to Bayesian variable selection approaches. In particular we highlight advantages of stochastic search variable selection (SSVS), that make it well suited for variable selection applications in psychology. We demonstrate these advantages and contrast SSVS with lasso type penalization in an application to predict depression symptoms in a large sample and an accompanying simulation study. We investigate the effects of sample size, effect size, and patterns of correlation among predictors on rates of correct and false inclusion and bias in the estimates. SSVS as investigated here is reasonably computationally efficient and powerful to detect moderate effects in small sample sizes (or small effects in moderate sample sizes), while protecting against false inclusion and without over-penalizing true effects. We recommend SSVS as a flexible framework that is well-suited for the field, discuss limitations, and suggest directions for future development.

Intersecting Relationships of Psychosocial and Structural Syndemic Problems Among People with HIV in South Africa: Using Network Analysis to Identify Influential Problems

In South Africa, little is known about interrelationships between syndemic problems among people with HIV (PWH). A better understanding of syndemic problems may yield important information regarding factors amenable to mitigation. We surveyed 194 PWH in Khayelitsha, outside of Cape Town, South Africa. We used network analysis to examine the frequency of 10 syndemic problems and their interrelationships. Syndemic problems among PWH in South Africa were common; 159 (82.8%) participants reported at least 2 co-occurring syndemic problems and 90 (46.9%) endorsed 4 or more. Network analysis revealed seven statistically significant associations. The most central problems were depression, substance use, and food insecurity. Three clusters of syndemics were identified: mood and violence; structural factors; and behavioral factors. Depression, substance use, and food insecurity commonly co-occur among PWH in sub-Saharan Africa and interfere with HIV outcomes. Network analysis can identify intervention targets to potentially improve HIV treatment outcomes.

Perceived neighborhood factors, health behaviors, and related outcomes in the Hispanic Community Health Study/Study of Latinos

Hispanic/Latino populations may experience significant neighborhood disadvantage, but limited research has explored whether these factors affect their health behaviors. Associations between perceived neighborhood factors at Visit 1 and health behaviors and related outcomes at Visit 2 in the Hispanic Community Health Study/Study of Latinos were evaluated. Multivariable logistic regression assessed cross-sectional and longitudinal relationships between perceived neighborhood social cohesion (NSC, 5 items), and neighborhood problems (NP, 7 items), with cancer screening, current smoking, excessive/binge drinking, hypertension, obesity, physical activity, and poor diet by gender and birthplace. NSC and NP scores were converted into quartiles. Mean age of participants was 42.5 years and 62.1% were women. Perceived NP, but not perceived NSC, differed by gender (p < 0.001). In unstratified models, no significant associations were observed between perceived NSC and any health behavior, whereas greater perceived NP was associated with less adherence to colon cancer screening (moderate level: aOR = 0.68, 95% CI = 0.51, 090) and more physical activity (very high level: aOR = 1.34, 95% CI = 1.06, 1.69) compared to low perceived NP. Women with moderate perceived NP, versus low NP, had a lower odds of colon cancer screening at Visit 1 (aOR = 0.62, 95% CI = 0.43, 0.91) and higher odds of mammogram adherence at Visit 2 (aOR = 2.86, 95% CI = 1.44, 5.68). Men with high perceived NP had a higher odds of excessive or binge drinking at Visit 2 (aOR = 1.99, 95% CI = 1.19, 3.31). We conclude that perceived NP were significantly related to health behaviors among HCHS/SOL individuals. Perceptions of neighborhood environment may be considered modifiable factors of structural neighborhood environment interventions.

Revisiting the factor structure and construct validity of the Cognitive Failures Questionnaire

The Cognitive Failures Questionnaire (CFQ; Broadbent et al., 1982) is an established and commonly used self-report measure of cognitive errors experienced in daily life, capturing perceived difficulties with forgetfulness, distractibility, and thinking blunders. Despite frequent use in clinical research and established associations with psychological and neuropsychological disorders, the psychometric properties and construct validity of the CFQ remain ambiguous. This study sought to critically assess the factor structure and external validity of the CFQ. A sample of 839 people (62% female) between 16 and 85 years of age (M = 44.12, SD = 19.54) was drawn from the Nathan Kline Institute-Rockland Sample. Previously published CFQ factor structures were compared via confirmatory factor analysis and the unique variance explained by each factor was assessed. Next, we related the CFQ latent variables to neuropsychological tasks and symptom measures of depression, anxiety domains, inattention, hyperactivity, and impulsivity. A single-factor model was best supported by the data, indicating that the CFQ represents a global measure of subjective cognitive difficulties rather than errors in specific domains. Scores on the CFQ did not predict poorer performance on objective neuropsychological tasks but were related to a range of psychological distress symptoms. Subscales derived from previously published factor structures may provide misleading impressions of the construct validity of the CFQ and are not recommended for use in future research or clinical contexts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Symptom-level networks of youth- and parent-reported depression and anxiety in a transdiagnostic clinical sample

Depression and anxiety disorders confer a significant public health concern for youth and their co-occurrence places youth at a higher risk for poorer psychosocial outcomes. In the present study, we use network analysis to investigate the role of and interactions among individual depression and anxiety symptoms in a treatment-seeking clinical sample.

METHODS

We estimate regularized partial correlation networks for youth- and parent reported symptoms in a transdiagnostic sample of youth (N = 417, ages 8-18). We examined features of the symptom-level networks such as network stability, centrality, bridge symptoms, and communities in both youth- and parent-reported networks.

RESULTS

Results indicate stable networks with disorder-specific clustering, such that symptoms were more interconnected within compared to between disorders. Symptoms related to self-comparison to peers and negative views of the future were most central in both networks. Symptoms of depression and anxiety were connected by worries for the future and hopelessness in the youth-reported network, whereas self-comparison to peers and low self-efficacy were bridge symptoms in the parent network. Distinct symptom clusters emerged in the parent- and youth-reported networks.

CONCLUSIONS

Our findings indicate that negative self-evaluation, negative views of the future, and repetitive negative thinking more generally are influential symptoms in the presentation and co-occurrence of depression and anxiety and as such may be promising targets in the treatment and prevention of depression and anxiety in youth.

shinyDLRs: A dashboard to facilitate derivation of diagnostic likelihood ratios

Despite increased recognition of the importance of evidence-based assessment in clinical psychology, utilization of gold-standard practices remains low, including during diagnostic assessments. One avenue to streamline evidence-based diagnostic assessment is to increase the use of diagnostic likelihood ratios (DLRs), derived from receiver operating characteristic curve analyses. DLRs allow for the adjustment of the likelihood that an individual has a disorder based on self-report data (e.g., questionnaires, psychosocial, family history). Although DLRs provide strong and readily implementable psychometric data to guide diagnostic decision-making, analyses necessary to derive DLRs are not commonplace in psychological curriculum and available resources require familiarity with specialized statistical methodologies and software. We developed a free, researcher-oriented dashboard, shinyDLRs (https://dlrs.shinyapps.io/shinyDLRs/), to facilitate the derivation of DLRs. shinyDLRs allows researchers to carry out multiple analyses while providing descriptive interpretations of statistics derived from receiver operating characteristic curves. We present the utility of this interface as applied to several freely available measures of mood and anxiety for the purposes of guiding diagnosis of psychopathology. The sample leveraged to accomplish this goal included 576 youth, 4-19 years of age, and a parent informant, both of whom completed several questionnaires and semi-structured interviews prior to participating in treatment at a university-based research clinic. Lastly, we provide recommendations for inclusion of DLRs in future research investigating the psychometric properties and diagnostic utility of assessments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Treating depression and improving adherence in HIV care with task-shared cognitive behavioural therapy in Khayelitsha, South Africa: a randomized controlled trial

INTRODUCTION

Major depressive disorder, highly prevalent among people with HIV (PWH) globally, including South Africa, is associated with suboptimal adherence to antiretroviral therapy. Globally, there are insufficient numbers of mental health providers and tested depression treatments. This study's aim was to test task-shared cognitive-behavioural therapy for adherence and depression (CBT-AD) in HIV, delivered by clinic nurses in South Africa.

METHODS

This was a two-arm randomized controlled effectiveness trial (recruitment: 14 July 2016 to 4 June 2019, last follow 9 June 2020). One-hundred-sixty-one participants with clinical depression and virally uncontrolled HIV were recruited from primary care clinics providing HIV care, in Khayelitsha, South Africa. Arm 1 was task-shared, nurse-delivered CBT-AD; and arm 2 was enhanced treatment as usual (ETAU). Primary outcomes (baseline to 4 months) were blinded Hamilton Depression Rating Scale (HAM-D) scores, and weekly adherence via real-time monitoring (Wisepill). Secondary outcomes were adherence and depression over 4-, 8- and 12-month follow-ups, proportion of participants with undetectable viremia and continuous CD4 cell counts at 12 months. Additional analyses involved viral load and CD4 over time.

RESULTS

At 4 months, the HAMD scores in the CBT-AD condition improved by an estimated 4.88 points more (CI: -7.86, -1.87, p = 0.0016), and for weekly adherence, 1.61 percentage points more per week (CI: 0.64, 2.58, p = 0.001) than ETAU. Over follow-ups, CBT-AD had an estimated 5.63 lower HAMD scores (CI: -7.90, -3.36, p < 0.001) and 23.56 percentage points higher adherence (CI: 10.51, 34.21, p < 0.001) than ETAU. At 12 months, adjusted models indicated that the odds of having an undetectable viremia was 2.51 greater at 12 months (CI: 1.01, 6.66, p = 0.047), and 3.54 greater over all of the follow-ups (aOR = 3.54, CI: 1.59, 20.50; p = 0.038) for those assigned CBT-AD. CD4 was not significantly different between groups at 12 months or over time.

CONCLUSIONS

Task-shared, nurse-delivered, CBT-AD is effective in improving clinical depression, ART adherence and viral load for virally unsuppressed PWH. The strategy of reducing depression to allow patients with self-care components of medical illness to benefit from adherence interventions is one to extend. Implementation science trials and analyses of cost-effectiveness are needed to translate findings into clinical practice.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02696824 https://clinicaltrials.gov/ct2/show/NCT02696824.

Testing measurement equivalence of neurocognitive assessments across language in the Hispanic Community Health Study/Study of Latinos

OBJECTIVE

Neuropsychological instruments are often developed in English and translated to other languages to facilitate the clinical evaluation of diverse populations or to utilize in research environments. However, the psychometric equivalence of these assessments across language must be demonstrated before populations can validly be compared.

METHOD

To test this equivalence, we applied measurement invariance procedures to a subsample (N = 1,708) of the Hispanic Community Health Survey/Study of Latinos (HCHS/SOL) across English and Spanish versions of a neurocognitive battery. Using cardinality matching, 854 English-speaking and 854 Spanish-speaking subsamples were matched on age, education, sex, immigration status (U.S. born, including territories, or foreign-born), and Hispanic/Latino heritage background. Neurocognitive measures included the Six-Item Screener (SIS), Brief-Spanish English Verbal Learning Test (B-SEVLT), Word Fluency (WF), and Digit Symbol Substitution (DSS). Confirmatory factor analysis was utilized to test item-level invariance of the SIS, B-SEVLT, and WF, as well as factor-level invariance of a higher-order neurocognitive functioning latent variable.

RESULTS

One item of both the SIS and WF were more difficult in Spanish than English, as was the DSS test. After accounting for partial invariance, Spanish-speakers performed worse on each of the subtests and the second-order neurocognitive functioning latent variable.

CONCLUSIONS

We found some evidence of bias at both item and factor levels, contributing to the poorer neurocognitive performance of Spanish test-takers. While these results explain the underperformance of Spanish-speakers to some extent, more work is needed to determine whether such bias is reflective of true cognitive differences or additional variables unaccounted for in this study. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Longitudinal Associations of Syndemic Conditions with Antiretroviral Therapy Adherence and HIV Viral Suppression Among HIV-Infected Patients in Primary Care

Psychosocial syndemic conditions have received more attention regarding their deleterious effects on HIV acquisition risk than for their potential impact on HIV treatment and viral suppression. To examine syndemic conditions' impact on the HIV care continuum, we analyzed data collected from people living with HIV (N = 14,261) receiving care through The Centers for AIDS Research Network of Integrated Clinical Systems at seven sites from 2007 to 2017 who provided patient-reported outcomes ∼4-6 months apart. Syndemic condition count (depression, anxiety, substance use, and hazardous drinking), sexual risk group, and time in care were modeled to predict antiretroviral therapy (ART) adherence and viral suppression (HIV RNA <400 copies/mL) using multilevel logistic regression. Comparing patients with each other, odds of ART adherence were 61.6% lower per between-patient syndemic condition [adjusted odds ratio (AOR) = 0.384; 95% confidence interval (CI), 0.362-0.408]; comparing patients with themselves, odds of ART adherence were 36.4% lower per within-patient syndemic condition (AOR = 0.636 95% CI, 0.606-0.667). Odds of viral suppression were 29.3% lower per between-patient syndemic condition (AOR = 0.707; 95% CI, 0.644-0.778) and 27.7% lower per within-patient syndemic condition (AOR = 0.723; 95% CI, 0.671-0.780). Controlling for the effects of adherence (AOR = 5.522; 95% CI, 4.67-6.53), each additional clinic visit was associated with 1.296 times higher odds of viral suppression (AOR = 1.296; 95% CI, 1.22-1.38), but syndemic conditions were not significant. Deploying effective interventions within clinics to identify and treat syndemic conditions and bolster ART adherence and continued engagement in care can help control the HIV epidemic, even within academic medical settings in the era of increasingly potent ART.

Syndemics and salivary inflammation in people living with HIV/AIDS

OBJECTIVE

People living with HIV/AIDS (PLWHA) are disproportionally exposed to a host of structural, community, and individual-level physical and psychosocial stressors also termed 'syndemic conditions.' The current study aimed to examine the association between experiencing syndemic conditions and physiological stress response and be associated with bodily inflammation, including Interlekin-6 (IL-6) and C-reactive protein (CRP) in PLWHA.

DESIGN

Participants (N = 103) were recruited from a public HIV clinic. They provided saliva samples of IL-6 and CRP and completed psychosocial measures.

MAIN OUTCOME MEASURES

Levels of circulating salivary IL-6 and CRP.

RESULTS

When predictors (birth country, recent housing instability, and incarceration history) were simultaneously entered into a regression model, only incarceration history was negatively associated with IL-6 [b = -.27, t(98) = -3.11, p = .002]. For CRP, the resulting regression model was not significant, [F(3, 98) = 2.23, p = .090].

CONCLUSION

Although we had expected higher levels of syndemics to be associated with higher levels of circulating inflammation, in our sample, length of incarceration was associated with lower levels of circulating IL-6. Findings are therefore suggestive of a stress response disruption resulting in a negative feedback loop as the long-term impact of chronic stress on inflammation.

Whole-Brain Functional Dynamics Track Depressive Symptom Severity

Depressive symptoms are reported by 20% of the population and are related to altered functional integrity of large-scale brain networks. The link between moment-to-moment brain function and depressive symptomatology, and the implications of these relationships for clinical and community populations alike, remain understudied. The present study examined relationships between functional brain dynamics and subclinical-to-mild depressive symptomatology in a large community sample of adults with and without psychiatric diagnoses. This study used data made available through the Enhanced Nathan Kline Institute-Rockland Sample; 445 participants between 18 and 65 years of age completed a 10-min resting-state functional MRI scan. Coactivation pattern analysis was used to examine the dimensional relationship between depressive symptoms and whole-brain states. Elevated levels of depressive symptoms were associated with increased frequency and dwell time of the default mode network, a brain network associated with self-referential thought, evaluative judgment, and social cognition. Furthermore, increased depressive symptom severity was associated with less frequent occurrences of a hybrid brain network implicated in cognitive control and goal-directed behavior, which may impair the inhibition of negative thinking patterns in depressed individuals. These findings demonstrate how temporally dynamic techniques offer novel insights into time-varying neural processes underlying subclinical and clinically meaningful depressive symptomatology.

Estimating HIV transmissions in a large U.S. clinic-based sample: effects of time and syndemic conditions

INTRODUCTION

Little is known about onward HIV transmissions from people living with HIV (PLWH) in care. Antiretroviral therapy (ART) has increased in potency, and treatment as prevention (TasP) is an important component of ending the epidemic. Syndemic theory has informed modelling of HIV risk but has yet to inform modelling of HIV transmissions.

METHODS

Data were from 61,198 primary HIV care visits for 14,261 PLWH receiving care through the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) at seven United States (U.S.) sites from 2007 to 2017. Patient-reported outcomes and measures (PROs) of syndemic conditions - depressive symptoms, anxiety symptoms, drug use (opiates, amphetamines, crack/cocaine) and alcohol use - were collected approximately four to six months apart along with sexual behaviours (mean = 4.3 observations). Counts of syndemic conditions, HIV sexual risk group and time in care were modelled to predict estimated HIV transmissions resulting from sexual behaviour and viral suppression status (HIV RNA < 400/mL) using hierarchical linear modelling.

RESULTS

Patients averaged 0.38 estimated HIV transmissions/100 patients/year for all visits with syndemic conditions measured (down from 0.83, first visit). The final multivariate model showed that per 100 patients, each care visit predicted 0.05 fewer estimated transmissions annually (95% confidence interval (CI): 0.03 to 0.06; p < 0.0005). Cisgender women, cisgender heterosexual men and cisgender men of undisclosed sexual orientation had, respectively, 0.47 (95% CI: 0.35 to 0.59; p < 0.0005), 0.34 (95% CI: 0.20 to 0.49; p < 0.0005) and 0.22 (95% CI: 0.09 to 0.35; p < 0.005) fewer estimated HIV transmissions/100 patients/year than cisgender men who have sex with men (MSM). Each within-patient syndemic condition predicted 0.18 estimated transmissions/100 patients/year (95% CI: 0.12 to 0.24; p < 0.0005). Each between-syndemic condition predicted 0.23 estimated HIV transmissions/100 patients/year (95% CI: 0.17 to 0.28; p < 0.0005).

CONCLUSIONS

Estimated HIV transmissions among PLWH receiving care in well-resourced U.S. clinical settings varied by HIV sexual risk group and decreased with time in care, highlighting the importance of TasP efforts. Syndemic conditions remained a significant predictor of estimated HIV transmissions notwithstanding the effects of HIV sexual risk group and time in care.

Insomnia, Health, and Health-related Quality of Life in an Urban Clinic Sample of People Living with HIV/AIDS

OBJECTIVE/BACKGROUND

Insomnia is a prevalent and interfering comorbidity of HIV infection. Nearly 70% of people living with HIV/AIDS (PLWHA) experience symptoms of insomnia and associated distress. The current study examined the mechanisms of insomnia in HIV health status and health-related quality of life and explored behavioral pathways to explain relationships.

PARTICIPANTS

Participants (N = 103) were active patients in an HIV clinic located within a nonprofit, tertiary care hospital in a large, urban city in the Southeast United States.

METHODS

Participants completed a clinical sleep interview and self-report assessments for adherence to antiretroviral medication, depression (PHQ-9), quality of life (ACTG-QOL), and relevant covariates. Viral load and CD4 were obtained via medical chart review.

RESULTS

Insomnia affected 67% of the clinic sample. Insomnia symptoms were directly associated with poorer health-related quality of life (p<.001). Greater insomnia symptoms were also significantly associated with greater depressive symptoms [b =.495, S.E. =.061], poorer medication adherence [b = -.912, S.E. =.292], and worse health status measured by absolute CD4 count [b = -.011, S.E. =.005].

CONCLUSIONS

In this sample of PLWHA, insomnia was associated with poorer health-related quality of life and worse health status. Future research and practice should consider insomnia treatment for this population, as it could improve overall health and well-being.

Modeling daily and weekly moderate and vigorous physical activity using zero-inflated mixture Poisson distribution

Recently developed accelerometer devices have been used in large epidemiological studies for continuous and objective monitoring of physical activities. Typically, physical movements are summarized as minutes in light, moderate, and vigorous physical activities in each wearing day. Because of preponderance of zeros, zero-inflated distributions have been used for modeling the daily moderate or higher levels of physical activity. Yet, these models do not fully account for variations in daily physical activity and cannot be extended to model weekly physical activity explicitly, while the weekly physical activity is considered as an indicator for a subject's average level of physical activity. To overcome these limitations, we propose to use a zero-inflated Poisson mixture distribution that can model daily and weekly physical activity in same family of mixture distributions. Under this method, the likelihood of an inactive day and the amount of exercise in an active day are simultaneously modeled by a joint random effects model to incorporate heterogeneity across participants. If needed, the method has the flexibility to include an additional random effect to address extra variations in daily physical activity. Maximum likelihood estimation can be obtained through Gaussian quadrature technique, which is implemented conveniently in an R package GLMMadaptive. Method performances are examined using simulation studies. The method is applied to data from the Hispanic Community Health Study/Study of Latinos to examine the relationship between physical activity and BMI groups and within a participant the difference in physical activity between weekends and weekdays.

Elucidating mechanisms of quality-of-life disparities in Hispanic women with breast cancer: An examination of disease stage, coping, and affect

OBJECTIVES

Compared to non-Hispanic white (NHW) women, Hispanic women with breast cancer (BCa) are more likely to be diagnosed at later stages of disease and experience reduced quality of life (QOL) following diagnosis. We hypothesized that the demands of later-stage disease results in a perceived inability to cope and greater distress for Hispanic women, resulting in decreased QOL.

METHODS

Hispanic (51%) and NHW (49%) women (N = 198) with newly diagnosed stage 0-3 BCa in Miami were enrolled in two trials between 2006 and 2019. In this cross-sectional analysis, a multiple-group structural equation modeling approach was applied to baseline measures of coping confidence (Measure of Current Status Scale), negative and positive affect (Affect Balance Scale), QOL (Functional Assessment of Cancer Therapy - Breast), and disease stage.

RESULTS

In our model, later-stage disease was not associated with worse QOL for Hispanic or NHW women. However, there were differences between Hispanic and NHW women on the path from disease stage to (1) coping confidence, (2) positive affect, and (3) negative affect, such that later disease stage was associated with lower coping confidence (b[SE] = -1.75[0.59], p = 0.002), less positive affect (b[SE] = -0.21[0.10], p = 0.026), and greater negative affect (b[SE] = 0.15[0.08], p = 0.052) among Hispanic, but not NHW, women. In addition, an indirect effect was found from greater stage to poorer QOL via less positive affect among Hispanic women only (b[SE] = -0.49[0.24], p = 0.041).

CONCLUSIONS

This data supports our theory that Hispanic women experience worse emotional distress at later-stage disease than do NHW women, in turn impacting QOL.

Child eyewitness researchers often bin age: Prevalence of the practice and recommendations for analyzing developmental trends

OBJECTIVE

Effective practices for eliciting and analyzing children's eyewitness reports rely on accurate conclusions about age differences in how children retain information and respond to memory probes. Binning, which is the practice of categorizing continuous variables into discrete groups, can lower studies' power to detect age differences and, in some situations, produce significant but spurious effects. In this article, we (a) describe a systematic review that estimated the frequency of binning age in child eyewitness studies, (b) analyze real and simulated data to illustrate how binning can distort conclusions about age and covariate effects, and (c) demonstrate best practices for analyzing and reporting age trends.

HYPOTHESES

We expected that researchers would frequently bin age and that we would replicate the negative consequences of binning in the demonstration data sets.

METHOD

For the systematic review, we retrieved 58 articles describing child eyewitness studies and determined whether researchers binned age for one randomly selected analysis per article. We then compared alternative ways of analyzing actual and simulated data sets.

RESULTS

Researchers binned age for 64% of the analyses (88% of analyses involving experimental manipulations vs. 35% of the nonexperimental analyses, φ = .55, p < .01). A significant age trend in the real data example was nonsignificant when age was treated as categorical, and in the simulated data sets we demonstrate how this practice may lead to detecting a spurious effect.

CONCLUSIONS

Treating age as a continuous variable maximizes power to detect real differences without inflating the frequency of spurious results, thereby ensuring that policies regarding child eyewitnesses reflect developmental changes in children's needs and abilities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A Network Analysis of Hoarding Symptoms, Saving and Acquiring Motives, and Comorbidity

Hoarding disorder is marked by strong attachments to everyday objects, extreme difficulties discarding, and impairing levels of clutter. We examined the associations between hoarding symptoms and associated clinical features using network analysis in a large sample of individuals with established hoarding disorder (n=217) and matched healthy controls (n=130). Network nodes included the three core features of hoarding (difficulties discarding, clutter, and acquiring), along with comorbid symptoms, impairment, and saving and acquiring motives. Models showed hoarding and comorbid symptoms as separate syndromes. Healthy and patient networks differed significantly in both global network strength and structure. For the hoarding patient network, the comorbidity and hoarding clusters were connected by acquiring and anxiety, which served as bridge symptoms. Clutter was the only hoarding node associated with impairment. Hoarding beliefs were not central to the model, and only difficulties discarding was associated with saving and acquiring motives, including emotional attachment and wastefulness beliefs. Our findings indicate that the network approach to mental disorders provides a new and complementary way to improve our understanding of the etiological model of hoarding, and may present novel hypotheses to examine in treatment development research.

Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain

Frequently, researchers in psychology are faced with the challenge of narrowing down a large set of predictors to a smaller subset. There are a variety of ways to do this, but commonly it is done by choosing predictors with the strongest bivariate correlations with the outcome. However, when predictors are correlated, bivariate relationships may not translate into multivariate relationships. Further, any attempts to control for multiple testing are likely to result in extremely low power. Here we introduce a Bayesian variable-selection procedure frequently used in other disciplines, stochastic search variable selection (SSVS). We apply this technique to choosing the best set of predictors of the perceived unpleasantness of an experimental pain stimulus from among a large group of sociocultural, psychological, and neurobiological (functional MRI) individual-difference measures. Using SSVS provides information about which variables predict the outcome, controlling for uncertainty in the other variables of the model. This approach yields new, useful information to guide the choice of relevant predictors. We have provided Web-based open-source software for performing SSVS and visualizing the results.

Examining a Syndemics Network Among Young Latino Men Who Have Sex with Men

BACKGROUND

Although studies consistently find that syndemic indicators are additively associated with increased HIV/STI risk behavior (e.g., condomless anal sex; CAS) among men who have sex with men (MSM), information is lacking about how syndemic indicators are associated with each other. Young Latino MSM are one of the most at-risk groups for acquiring HIV in the U.S. Understanding the associations of syndemic indicators with each other and with CAS may improve understanding of how to enhance sexual and behavioral health in this population.

METHOD

Network analysis using the graphical LASSO (glasso) algorithm was employed to explore associations between CAS and syndemic indicators among 139 young Latino MSM. Structural and psychosocial syndemic indicators were assessed via self-report. CAS was defined as the number of partners in the past 3 months with whom one engaged in CAS.

RESULTS

Results of the network analysis suggested the variables with the highest centrality were unstable housing, prison history, childhood sexual abuse, and CAS. Specific significant associations included links between CAS and alcohol use (b = 0.40), childhood sexual abuse and unstable housing (b = - 0.75), alcohol use and childhood sexual abuse (b = 0.40), and substance use and intimate partner violence (b = 0.43).

CONCLUSION

This pattern of interconnectedness demonstrates the potential for network analysis to examine nuanced interrelationships of syndemic indicators. The specific associations in this sample raise the question whether a primary focus of interventions should address the more central syndemic indicators for this population, such as alcohol use and unstable housing, and whether this would, via downstream effects, affect other aspects of behavioral health in this population.

Longitudinal Effects of Syndemics on HIV-Positive Sexual Minority Men's Sexual Health Behaviors

This study examined the longitudinal effects of co-occurring psychosocial concerns, or syndemics, on HIV-positive sexual minority men's likelihood of engaging in serodiscordant condomless anal sex (CAS), a health behavior with implications for personal and public health. Participants included 390 HIV-positive sexual minority men from two prior secondary prevention trials. Over the course of the 1-year data collection period (up to 5 observations per participant), participants completed self-report measures of CAS, as well as six syndemic factors: post-traumatic stress disorder, childhood sexual abuse, depression, anxiety, alcohol abuse, and polysubstance/stimulant use. We employed multilevel modeling to examine the longitudinal additive effect of syndemics on serodiscordant CAS (binary) over the 1-year period. The number of syndemic conditions was a significant predictor of CAS, with each additional syndemic associated with 1.41 greater odds of CAS (p = .0004; 95% CI [1.16, 1.70]). Both the between-person (p = .0121, 95% CI [1.07, 1.69]) and within-person (p = .01, 95% CI [1.11, 2.10]) effects of syndemics were significant predictors, showing that an increase in the number of syndemic conditions across person and time both increased odds of CAS. Interventions addressing HIV-positive sexual minority men's sexual health behaviors should address the potential impact of co-occurring psychosocial concerns that affect these behaviors. This will benefit this population's personal sexual health and reduce transmission of HIV and STIs among sexual minority men.

The situation or the person? Individual and task-evoked differences in BOLD activity

Investigations of between-person variability are enjoying a recent resurgence in functional magnetic resonance imaging (fMRI) research. Several recent studies have found persistent between-person differences in blood-oxygenated-level dependent (BOLD) activation patterns and resting-state functional connectivity. Conflicting findings have been reported regarding the extent to which (a) between-person or (b) within-person cognitive state differences explain differences in BOLD activation patterns. These discrepancies may arise due to statistical analysis choices, parcellation resolution, and limited sampling of task-fMRI datasets. We attempt to address these issues in a large-scale analysis of several task-fMRI paradigms. Using a novel application of multivariate distance matrix regression, we examine between-person and task-condition variability estimates across varying levels of "resolution", from a coarse region-of-interest level to the vertex-level, and across different distance metrics. These analyses revealed that under most circumstances, differences in task conditions explained a greater amount of variance in activation map differences than between-person differences. However, this finding was reversed when comparing activation maps at a "high-resolution" vertex level. More generally, we observed that when moving from "low" to "high" resolutions, the variance explained by between-person differences increased while variance explained by task conditions decreased. We further analyzed the relationships among subject-level activation maps across all task-conditions using an unsupervised clustering approach and identified a superordinate task structure. This structure went beyond conventional task labels and highlighted those experimental manipulations across task conditions that produce contrasting versus similar whole-brain activation patterns. Overall, these analyses suggest that the question of the subject- versus task-effects on BOLD activation patterns is nontrivial, and depends on the comparison "resolution," choice of distance metric, and the coding of task-conditions.

Longitudinal Effects of Syndemics on ART Non-adherence Among Sexual Minority Men

This study examined longitudinally the additive effect of syndemics, or co-occurring psychosocial problems, on antiretroviral treatment (ART) non-adherence among 390 HIV-positive sexual minority men. Participants completed measures of ART adherence (reduced to a non-adherence score using exploratory factor analysis) and six syndemic conditions. We employed multilevel modeling with the number of syndemics as a longitudinal predictor of non-adherence, and logistic regression with baseline syndemics predicting follow up viral load. Number of syndemics was a significant longitudinal predictor of non-adherence, with each additional syndemic associated with a 0.13 increase in non-adherence (p = 0.004). Each additional syndemic was also associated with 1.27 greater odds of detectable viral load (p = 0.002). Among HIV-positive sexual minority men in this sample, more syndemics were associated with lower ART adherence and greater odds of detectable viral load, suggesting the need for behavioral intervention to facilitate care for this population.

A multilevel examination of sleep, depression, and quality of life in people living with HIV/AIDS

Sleep problems are prevalent in people living with HIV/AIDS; however, few studies examine how poor sleep affects mental health and quality of life longitudinally. A sample of people living with HIV/AIDS from a randomized trial (N = 240; mean age = 47.18; standard deviation = 8.3; 71.4% male; 61.2% White) completed measures of depression (Montgomery-Åsberg Depression Rating Scale), health-related quality of life (AIDS Clinical Trial Group Quality of Life Measure), and life satisfaction (Quality of Life Inventory) at baseline and 4, 8, and 12 months. Controlling for time, condition, and relevant interactions, sleep problems significantly predicted worse outcomes over time (ps < 0.001). Findings have implications for the importance of identifying and treating sleep problems in people living with HIV/AIDS to improve mental health and quality-of-life outcomes.

Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators

Psychometric models for item-level data are broadly useful in psychology. A recurring issue for estimating item factor analysis (IFA) models is low-item endorsement (item sparseness), due to limited sample sizes or extreme items such as rare symptoms or behaviors. In this paper, I demonstrate that under conditions characterized by sparseness, currently available estimation methods, including maximum likelihood (ML), are likely to fail to converge or lead to extreme estimates and low empirical power. Bayesian estimation incorporating prior information is a promising alternative to ML estimation for IFA models with item sparseness. In this article, I use a simulation study to demonstrate that Bayesian estimation incorporating general prior information improves parameter estimate stability, overall variability in estimates, and power for IFA models with sparse, categorical indicators. Importantly, the priors proposed here can be generally applied to many research contexts in psychology, and they do not impact results compared to ML when indicators are not sparse. I then apply this method to examine the relationship between suicide ideation and insomnia in a sample of first-year college students. This provides an important alternative for researchers who may need to model items with sparse endorsement.

Comparing within-person effects from multivariate longitudinal models

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model. Whereas previous research has focused on detecting whether within-person effects exist over development, the present work can be used to understand the nature of these relationships. The authors compare each modeling approach using an example investigating the concurrent development of mother-child closeness and mother-child conflict. The findings demonstrate that fundamentally different conclusions about developmental processes may be reached depending on which model is used, and a framework is demonstrated for making sense of seemingly contradictory findings. (PsycINFO Database Record

Judgments of self-identified gay and heterosexual male speakers: Which phonemes are most salient in determining sexual orientation?

While numerous studies have demonstrated that a male speaker's sexual orientation can be identified from relatively long passages of speech, few studies have evaluated whether listeners can determine sexual orientation when presented with word-length stimuli. If listeners are able to distinguish between self-identified gay and heterosexual male speakers of American English, it is unclear whether they form their judgments based on a phoneme, such as a vowel or consonant, or multiple phonemes, such as a vowel and a consonant. In this study, we first found that listeners can distinguish between self-identified gay and heterosexual speakers of American English upon hearing word-length stimuli. We extended these results in a separate experiment to demonstrate that listeners primarily rely on vowels, and to some extent consonants, when forming their judgments. Listeners were able to differentiate between the two groups of speakers for each of the vowels and three of the seven consonants presented. In a follow-up experiment we found evidence that listeners' judgments improved if they were presented with multiple phonemes, such as a vowel and /s/. These results provide important information about how different phonemes can provide discriminant information about a male speaker's sexual orientation.

Advantages of Integrative Data Analysis for Developmental Research

Amid recent progress in cognitive development research, high-quality data resources are accumulating, and data sharing and secondary data analysis is becoming an increasingly valuable tool. Integrative data analysis (IDA) is an exciting analytical framework that can enhance secondary data analysis in powerful ways. IDA pools item level data across multiple studies to make inferences possible both within and across studies and can be used to test questions not possible in individual contributing studies. Some of the potential benefits of IDA include the ability to study longer developmental periods, examine how the measurement of key constructs changes over time, increase subject heterogeneity, and improve statistical power and capability to study rare behaviors. Our goal in this paper is to provide a brief overview of the benefits and challenges of IDA in developmental research and to identify additional resources that provide more detailed discussions of this topic.

Interpretational Confounding or Confounded Interpretations of Causal Indicators?

In measurement theory causal indicators are controversial and little-understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning intended by a researcher. This article questions the validity of evidence used to claim that causal indicators are inherently susceptible to interpretational confounding. Further, a simulation study demonstrates that causal indicator coefficients are stable across correctly-specified models. Determining the suitability of causal indicators has implications for the way we conceptualize measurement and build and evaluate measurement models.

The separation of between-person and within-person components of individual change over time: a latent curve model with structured residuals

OBJECTIVE

Although recent statistical and computational developments allow for the empirical testing of psychological theories in ways not previously possible, one particularly vexing challenge remains: how to optimally model the prospective, reciprocal relations between 2 constructs as they developmentally unfold over time. Several analytic methods currently exist that attempt to model these types of relations, and each approach is successful to varying degrees. However, none provide the unambiguous separation over time of between-person and within-person components of stability and change, components that are often hypothesized to exist in the psychological sciences. Our goal in this article is to propose and demonstrate a novel extension of the multivariate latent curve model to allow for the disaggregation of these effects.

METHOD

We begin with a review of the standard latent curve models and describe how these primarily capture between-person differences in change. We then extend this model to allow for regression structures among the time-specific residuals to capture within-person differences in change.

RESULTS

We demonstrate this model using an artificial data set generated to mimic the developmental relation between alcohol use and depressive symptomatology spanning 5 repeated measures.

CONCLUSIONS

We obtain a specificity of results from the proposed analytic strategy that is not available from other existing methodologies. We conclude with potential limitations of our approach and directions for future research.