Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

The impact of psychological strengths on Veteran populations' mental health trajectories during the COVID-19 pandemic.

PURPOSE: Mental health trajectories during the COVID-19 pandemic have been examined in Veterans with tenuous social connections, i.e., those with recent homelessness (RHV) or a psychotic disorder (PSY), and in control Veterans (CTL). We test potential moderating effects on these trajectories by psychological factors that may help individuals weather the socio-emotional challenges associated with the pandemic (i.e., 'psychological strengths'). METHODS: We assessed 81 PSY, 76 RHV, and 74 CTL over 5 periods between 05/2020 and 07/2021. Mental health outcomes (i.e., symptoms of depression, anxiety, contamination concerns, loneliness) were assessed at each period, and psychological strengths (i.e., a composite score based on tolerance of uncertainty, performance beliefs, coping style, resilience, perceived stress) were assessed at the initial assessment. Generalized models tested fixed and time-varying effects of a composite psychological strengths score on clinical trajectories across samples and within each group. RESULTS: Psychological strengths had a significant effect on trajectories for each outcome (ps < 0.05), serving to ameliorate changes in mental health symptoms. The timing of this effect varied across outcomes, with early effects for depression and anxiety, later effects for loneliness, and sustained effects for contamination concerns. A significant time-varying effect of psychological strengths on depressive symptoms was evident in RHV and CTL, anxious symptoms in RHV, contamination concerns in PSY and CTL, and loneliness in CTL (ps < 0.05). CONCLUSION: Across vulnerable and non-vulnerable Veterans, presence of psychological strengths buffered against exacerbations in clinical symptoms. The timing of the effect varied across outcomes and by group.

Longitudinal trajectories of cortical development in 22q11.2 copy number variants and typically developing controls.

Probing naturally-occurring, reciprocal genomic copy number variations (CNVs) may help us understand mechanisms that underlie deviations from typical brain development. Cross-sectional studies have identified prominent reductions in cortical surface area (SA) and increased cortical thickness (CT) in 22q11.2 deletion carriers (22qDel), with the opposite pattern in duplication carriers (22qDup), but the longitudinal trajectories of these anomalies-and their relationship to clinical symptomatology-are unknown. Here, we examined neuroanatomic changes within a longitudinal cohort of 261 22q11.2 CNV carriers and demographically-matched typically developing (TD) controls (84 22qDel, 34 22qDup, and 143 TD; mean age 18.35, ±10.67 years; 50.47% female). A total of 431 magnetic resonance imaging scans (164 22qDel, 59 22qDup, and 208 TD control scans; mean interscan interval = 20.27 months) were examined. Longitudinal FreeSurfer analysis pipelines were used to parcellate the cortex and calculate average CT and SA for each region. First, general additive mixed models (GAMMs) were used to identify regions with between-group differences in developmental trajectories. Secondly, we investigated whether these trajectories were associated with clinical outcomes. Developmental trajectories of CT were more protracted in 22qDel relative to TD and 22qDup. 22qDup failed to show normative age-related SA decreases. 22qDel individuals with psychosis spectrum symptoms showed two distinct periods of altered CT trajectories relative to 22qDel without psychotic symptoms. In contrast, 22q11.2 CNV carriers with autism spectrum diagnoses showed early alterations in SA trajectories. Collectively, these results provide new insights into altered neurodevelopment in 22q11.2 CNV carriers, which may shed light on neural mechanisms underlying distinct clinical outcomes.

Motivational and cognitive factors linked to community integration in homeless veterans: study 1 - individuals with psychotic disorders.

BACKGROUND: Little is known about the determinants of community integration (i.e. recovery) for individuals with a history of homelessness, yet such information is essential to develop targeted interventions. METHODS: We recruited homeless Veterans with a history of psychotic disorders and evaluated four domains of correlates of community integration: perception, non-social cognition, social cognition, and motivation. Baseline assessments occurred after participants were engaged in supported housing services but before they received housing, and again after 12 months. Ninety-five homeless Veterans with a history of psychosis were assessed at baseline and 53 returned after 12 months. We examined both cross-sectional and longitudinal relationships with 12-month community integration. RESULTS: The strongest longitudinal association was between a baseline motivational measure and social integration at 12 months. We also observed cross-sectional associations at baseline between motivational measures and community integration, including social, work, and independent living. Cross-lagged panel analyses did not suggest causal associations for the motivational measures. Correlations with perception and non-social cognition were weak. One social cognition measure showed a significant longitudinal correlation with independent living at 12 months that was significant for cross-lagged analysis, consistent with a causal relationship and potential treatment target. CONCLUSIONS: The relatively selective associations for motivational measures differ from what is typically seen in psychosis, in which all domains are associated with community integration. These findings are presented along with a partner paper (Study 2) to compare findings from this study to an independent sample without a history of psychotic disorders to evaluate the consistency in findings regarding community integration across projects.

Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.

Electroencephalography experiments produce region-referenced functional data representing brain signals in the time or the frequency domain collected across the scalp. The data typically also have a multilevel structure with high-dimensional observations collected across multiple experimental conditions or visits. Common analysis approaches reduce the data complexity by collapsing the functional and regional dimensions, where event-related potential (ERP) features or band power are targeted in a pre-specified scalp region. This practice can fail to portray more comprehensive differences in the entire ERP signal or the power spectral density (PSD) across the scalp. Building on the weak separability of the high-dimensional covariance process, the proposed multilevel hybrid principal components analysis (M-HPCA) utilizes dimension reduction tools from both vector and functional principal components analysis to decompose the total variation into between- and within-subject variance. The resulting model components are estimated in a mixed effects modeling framework via a computationally efficient minorization-maximization algorithm coupled with bootstrap. The diverse array of applications of M-HPCA is showcased with two studies of individuals with autism. While ERP responses to match vs mismatch conditions are compared in an audio odd-ball paradigm in the first study, short-term reliability of the PSD across visits is compared in the second. Finite sample properties of the proposed methodology are studied in extensive simulations.

A study of longitudinal trends in time-frequency transformations of EEG data during a learning experiment.

EEG experiments yield high-dimensional event-related potential (ERP) data in response to repeatedly presented stimuli throughout the experiment. Changes in the high-dimensional ERP signal throughout the duration of an experiment (longitudinally) is the main quantity of interest in learning paradigms, where they represent the learning dynamics. Typical analysis, which can be performed in the time or the frequency domain, average the ERP waveform across all trials, leading to the loss of the potentially valuable longitudinal information in the data. Longitudinal time-frequency transformation of ERP (LTFT-ERP) is proposed to retain information from both the time and frequency domains, offering distinct but complementary information on the underlying cognitive processes evoked, while still retaining the longitudinal dynamics in the ERP waveforms. LTFT-ERP begins by time-frequency transformations of the ERP data, collected across subjects, electrodes, conditions and trials throughout the duration of the experiment, followed by a data driven multidimensional principal components analysis (PCA) approach for dimension reduction. Following projection of the data onto leading directions of variation in the time and frequency domains, longitudinal learning dynamics are modeled within a mixed effects modeling framework. Applications to a learning paradigm in autism depict distinct learning patterns throughout the experiment among children diagnosed with Autism Spectrum Disorder and their typically developing peers. LTFT-ERP time-frequency joint transformations are shown to bring an additional level of specificity to interpretations of the longitudinal learning patterns related to underlying cognitive processes, which is lacking in single domain analysis (in the time or the frequency domain only). Simulation studies show the efficacy of the proposed methodology.

Hybrid principal components analysis for region-referenced longitudinal functional EEG data.

Electroencephalography (EEG) data possess a complex structure that includes regional, functional, and longitudinal dimensions. Our motivating example is a word segmentation paradigm in which typically developing (TD) children, and children with autism spectrum disorder (ASD) were exposed to a continuous speech stream. For each subject, continuous EEG signals recorded at each electrode were divided into one-second segments and projected into the frequency domain via fast Fourier transform. Following a spectral principal components analysis, the resulting data consist of region-referenced principal power indexed regionally by scalp location, functionally across frequencies, and longitudinally by one-second segments. Standard EEG power analyses often collapse information across the longitudinal and functional dimensions by averaging power across segments and concentrating on specific frequency bands. We propose a hybrid principal components analysis for region-referenced longitudinal functional EEG data, which utilizes both vector and functional principal components analyses and does not collapse information along any of the three dimensions of the data. The proposed decomposition only assumes weak separability of the higher-dimensional covariance process and utilizes a product of one dimensional eigenvectors and eigenfunctions, obtained from the regional, functional, and longitudinal marginal covariances, to represent the observed data, providing a computationally feasible non-parametric approach. A mixed effects framework is proposed to estimate the model components coupled with a bootstrap test for group level inference, both geared towards sparse data applications. Analysis of the data from the word segmentation paradigm leads to valuable insights about group-region differences among the TD and verbal and minimally verbal children with ASD. Finite sample properties of the proposed estimation framework and bootstrap inference procedure are further studied via extensive simulations.

Motivational and cognitive factors linked to community integration in homeless veterans: Study 2 - clinically diverse sample.

BACKGROUND: In an initial study (Study 1), we found that motivation predicted community integration (i.e. functional recovery) 12 months after receiving housing in formerly homeless Veterans with a psychotic disorder. The current study examined whether the same pattern would be found in a broader, more clinically diverse, homeless Veteran sample without psychosis. METHODS: We examined four categories of variables as potential predictors of community integration in non-psychotic Veterans: perception, non-social cognition, social cognition, and motivation at baseline (after participants were engaged in a permanent supported housing program but before receiving housing) and a 12-month follow-up. A total of 82 Veterans had a baseline assessment and 41 returned for testing after 12 months. RESULTS: The strongest longitudinal association was between an interview-based measure of motivation (the motivation and pleasure subscale from the Clinical Assessment Interview for Negative Symptoms) at baseline and measures of social integration at 12 months. In addition, cross-lagged panel analyses were consistent with a causal influence of general psychiatric symptoms at baseline driving social integration at 12 months, and reduced expressiveness at baseline driving independent living at 12 months, but there were no significant causal associations with measures of motivation. CONCLUSIONS: The findings from this study complement and reinforce those in Veterans with psychosis. Across these two studies, our findings suggest that motivational factors are associated at baseline and at 12 months and are particularly important for understanding and improving community integration in recently-housed Veterans across psychiatric diagnoses.

New-Onset Diabetes, Longitudinal Trends in Metabolic Markers, and Risk of Pancreatic Cancer in a Heterogeneous Population.

BACKGROUND & AIMS: Observational studies of predominantly white populations have found new-onset diabetes to be associated with increased risk of pancreatic cancer. We sought to determine whether this relationship applies to other races or ethnicities and to identify metabolic profiles associated with increased risk of pancreatic cancer. METHODS: We conducted a population-based cohort study of Asian, black, Hispanic and white patients from Kaiser Permanente Southern California from 2006 through 2016 (n = 1,499,627). Patients with diabetes were identified based on glucose and hemoglobin A1c (HbA1c) measurements. We used Cox regression to assess the relationship between diabetes status and duration and pancreatic cancer. For patients with recent diagnoses of diabetes (1 year or less) we compared longitudinal changes in glucose, HbA1c, and weight, from time of diabetes diagnosis through 3 years prior to the diagnosis, in patients with vs without pancreatic cancer. RESULTS: We identified 2,002 incident cases of pancreatic cancer from nearly 7.5 million person-years of follow-up. Compared to patients without diabetes, individuals who received a recent diagnosis of diabetes had an almost 7-fold increase in risk of pancreatic cancer (relative risk, 6.91; 95% CI, 5.76-8.30). Among patients with a recent diagnosis of diabetes, those who developed pancreatic cancer had more rapid increases in levels of glucose (Δslope: cases, 37.47 mg/dL vs non-cases, 27.68 mg/dL) and HbA1c (Δslope: cases, 1.39% vs non-cases, 0.86%) in the month preceding the diagnosis of diabetes, and subtle weight loss in the prior years (slope: cases -0.18 kg/interval vs non-cases 0.33 kg/interval). These longitudinal changes in markers of metabolism were stronger for specific race and ethnic groups. CONCLUSIONS: In a study of a large ethnically diverse population, we found risk of pancreatic cancer to be increased among patients with a diagnosis of diabetes in the past year among different races and ethnicities. Weight loss and rapid development of poor glycemic control were associated with increased risk of pancreatic cancer in multiple races.

Evidence of shared familial factors influencing neurocognitive endophenotypes in adult- and childhood-onset schizophrenia.

BACKGROUND: The aggregation of neurocognitive deficits among the non-psychotic first-degree relatives of adult- and childhood-onset schizophrenia patients suggests that there may be a common etiology for these deficits in childhood- and adult-onset illness. However, there is considerable heterogeneity in the presentation of neurobiological abnormalities, and whether there are differences in the extent of familial transmission for specific domains of cognitive function has not been systematically addressed. METHODS: We employed variance components analysis, as implemented in SOLAR-Eclipse, to evaluate the evidence of familial transmission for empirically derived composite scores representing attention, working memory, verbal learning, verbal retention, and memory for faces. We contrast estimates for adult- and childhood-onset schizophrenia families and matched community control pedigrees, and compare our findings to previous reports based on analogous neurocognitive assessments. RESULTS: We observed varying degrees of familial transmission; attention and working memory yielded comparable, significant estimates for adult-onset and community control pedigrees; verbal learning was significant for childhood-onset and community control pedigrees; and facial memory demonstrated significant familial transmission only for childhood-onset schizophrenia. Model-fitting analyses indicated significant differences in familiality between adult- and childhood-onset schizophrenia for attention, working memory, and verbal learning. CONCLUSIONS: By comprehensively assessing a wide range of neurocognitive domains in adult- and childhood-onset schizophrenia families, we provide additional support for specific neurocognitive domains as schizophrenia endophenotypes. Whereas comparable estimates of familial transmission for certain dimensions of cognitive functioning support a shared etiology of adult- and childhood-onset neurocognitive function, observed differences may be taken as preliminary evidence of partially divergent multifactorial architectures.

Randomized controlled trial of family-focused treatment for child depression compared to individual psychotherapy: one-year outcomes.

OBJECTIVE: Childhood-onset depression is associated with increased risk of recurrent depression and high morbidity extending into adolescence and adulthood. This multisite randomized controlled trial evaluated two active psychosocial treatments for childhood depression: family-focused treatment for childhood depression (FFT-CD) and individual supportive psychotherapy (IP). Aims were to describe effects through 52 weeks postrandomization on measures of depression, functioning, nondepressive symptoms, and harm events. METHODS: Children meeting criteria for depressive disorders (N = 134) were randomly assigned to 15 sessions of FFT-CD or IP and evaluated at mid-treatment for depressive symptoms and fully at roughly 16 weeks (after acute treatment), 32 weeks, and 52 weeks/one year. See clinicaltrials.gov: NCT01159041. RESULTS: Analyses using generalized linear mixed models confirmed the previously reported FFT-CD advantage on rates of acute depression response (≥50% Children's Depression Rating Scale reduction). Improvements in depression and other outcomes were most rapid during the acute treatment period, and leveled off between weeks 16 and 52, with a corresponding attenuation of observed group differences, although both groups showed improved depression and functioning over 52 weeks. Survival analyses indicated that most children recovered from their index depressive episodes by week 52: estimated 76% FFT-CD, 77% IP. However, by the week 52 assessment, one FFT-CD child and six IP children had suffered recurrent depressive episodes. Four children attempted suicide, all in the IP group. Other indicators of possible harm were relatively evenly distributed across groups. CONCLUSIONS: Results indicate a quicker depression response in FFT-CD and hint at greater protection from recurrence and suicide attempts. However, outcomes were similar for both active treatments by week 52/one year. Although community care received after acute treatment may have influenced results, findings suggest the value of a more extended/chronic disease model that includes monitoring and guidance regarding optimal interventions when signs of depression-risk emerge.

The Orai Ca2+ channel inhibitor CM4620 targets both parenchymal and immune cells to reduce inflammation in experimental acute pancreatitis.

KEY POINTS: This work confirms previous reports that CM4620, a small molecule inhibitor of Ca2+ entry via store operated Ca2+ entry (SOCE) channels formed by stromal interaction molecule 1 (STIM1)/Orai complexes, attenuates acinar cell pathology and acute pancreatitis in mouse experimental models. Here we report that intravenous administration of CM4620 reduces the severity of acute pancreatitis in the rat, a hitherto untested species. Using CM4620, we probe further the mechanisms whereby SOCE via STIM1/Orai complexes contributes to the disease in pancreatic acinar cells, supporting a role for endoplasmic reticulum stress/cell death pathways in these cells. Using CM4620, we show that SOCE via STIM1/Orai complexes promotes neutrophil oxidative burst and inflammatory gene expression during acute pancreatitis, including in immune cells which may be either circulating or invading the pancreas. Using CM4620, we show that SOCE via STIM1/Orai complexes promotes activation and fibroinflammatory gene expression within pancreatic stellate cells. ABSTRACT: Key features of acute pancreatitis include excess cellular Ca2+ entry driven by Ca2+ depletion from the endoplasmic reticulum (ER) and subsequent activation of store-operated Ca2+ entry (SOCE) channels in the plasma membrane. In several cell types, including pancreatic acinar, stellate cells (PaSCs) and immune cells, SOCE is mediated via channels composed primarily of Orai1 and stromal interaction molecule 1 (STIM1). CM4620, a selective Orai1 inhibitor, prevents Ca2+ entry in acinar cells. This study investigates the effects of CM4620 in preventing or reducing acute pancreatitis features and severity. We tested the effects of CM4620 on SOCE, trypsinogen activation, acinar cell death, activation of NFAT and NF-κB, and inflammatory responses in ex vivo and in vivo rodent models of acute pancreatitis and human pancreatic acini. We also examined whether CM4620 inhibited cytokine release in immune cells, fibro-inflammatory responses in PaSCs, and oxidative burst in neutrophils, all cell types participating in pancreatitis. CM4620 administration to rats by i.v. infusion starting 30 min after induction of pancreatitis significantly diminished pancreatitis features including pancreatic oedema, acinar cell vacuolization, intrapancreatic trypsin activity, cell death signalling and acinar cell death. CM4620 also decreased myeloperoxidase activity and inflammatory cytokine expression in pancreas and lung tissues, fMLF peptide-induced oxidative burst in human neutrophils, and cytokine production in human peripheral blood mononuclear cells (PBMCs) and rodent PaSCs, indicating that Orai1/STIM1 channels participate in the inflammatory responses of these cell types during acute pancreatitis. These findings support pathological Ca2+ entry-mediated cell death and proinflammatory signalling as central mechanisms in acute pancreatitis pathobiology.

High Quality of Care Persists With Shifting Depression Services From VA Specialty to Integrated Primary Care.

BACKGROUND/OBJECTIVE: Offering depression collaborative care services in primary care (PC) settings can reduce use of nonintegrated mental health care resources and improve mental health care access, particularly for vulnerable PC patients. Tests of effects on depression care quality, however, are needed. We examined overall quality of depression care and tested whether increasing clinic engagement in Veterans Affairs (VA)'s Primary Care-Mental Health Integration (PC-MHI) services was associated with differences in depression care quality over time. METHODS: We conducted a retrospective longitudinal cohort study of 80,136 Veterans seen in 26 Southern California VA PC clinics (October 1, 2008-September 30, 2013). Using multilevel regression models adjusting for year, clinic, and patient characteristics, we predicted effects of clinic PC-MHI engagement (ie, percent of PC patients receiving PC-MHI services) on 3 VA-developed longitudinal electronic population-based depression quality measures among Veterans newly diagnosed with depression (n=12,533). RESULTS: Clinic PC-MHI engagement rates were not associated with significant depression care quality differences. Across all clinics, average rates of follow-up within 84 or 180 days were, 66.4% and 74.5%, respectively. Receipt of minimally appropriate treatment was 80.5%. Treatment probabilities were significantly higher for vulnerable PC patients (homeless: 4.5%, P=0.03; serious mental illness: 15.2%, P<0.001), than for otherwise similar patients without these characteristics. CONCLUSIONS/POLICY IMPLICATIONS: Study patients treated in PC clinics with greater PC-MHI engagement received similarly high quality depression care, and even higher quality for vulnerable patients. Findings support increasing use of PC-MHI models to the extent that they confer some advantage over existing services (eg, access, patient satisfaction) other than quality of care.

Covariate-adjusted region-referenced generalized functional linear model for EEG data.

Electroencephalography (EEG) studies produce region-referenced functional data in the form of EEG signals recorded across electrodes on the scalp. It is of clinical interest to relate the highly structured EEG data to scalar outcomes such as diagnostic status. In our motivating study, resting-state EEG is collected on both typically developing (TD) children and children with autism spectrum disorder (ASD) aged 2 to 12 years old. The peak alpha frequency (PAF), defined as the location of a prominent peak in the alpha frequency band of the spectral density, is an important biomarker linked to neurodevelopment and is known to shift from lower to higher frequencies as children age. To retain the most amount of information from the data, we consider the oscillations in the spectral density within the alpha band, rather than just the peak location, as a functional predictor of diagnostic status (TD vs ASD), adjusted for chronological age. A covariate-adjusted region-referenced generalized functional linear model is proposed for modeling scalar outcomes from region-referenced functional predictors, which utilizes a tensor basis formed from one-dimensional discrete and continuous bases to estimate functional effects across a discrete regional domain while simultaneously adjusting for additional nonfunctional covariates, such as age. The proposed methodology provides novel insights into differences in neural development of TD and ASD children. The efficacy of the proposed methodology is investigated through extensive simulation studies.

Adjunctive thyroid hormone treatment in rapid cycling bipolar disorder: A double-blind placebo-controlled trial of levothyroxine (L-T4 ) and triiodothyronine (T3 ).

OBJECTIVES: This report describes the first comparative double-blind, placebo-controlled trial of levothyroxine (L-T4 ) and triiodothyronine (T3 ) as adjunctive treatments in rapid cycling bipolar disorder. METHODS: Thirty-two treatment-resistant, rapid cycling patients who had failed a trial of lithium were randomized into three treatment arms: L-T4 , T3 , or placebo. They were followed for ≥4 months with weekly clinical and endocrine assessments. RESULTS: There were no statistically significant differences between the groups in age, gender, duration of illness, or thyroid status. Markov chain analyses were employed to assess treatment effects on cycling patterns among mood states (euthymia, depression, mania, and mixed). Within groups, post-treatment the L-T4 group spent significantly less time depressed or in a mixed state and greater time euthymic. The T3 and placebo groups did not differ significantly pre- and post-treatment in any mood state, although the pattern of effects was the same for the T3 group as for the L-T4 group. Between groups, the L-T4 group had a significantly greater increase in time euthymic and decrease in time in the mixed state than the placebo group. Other group differences were not significant, although they were in the expected direction. CONCLUSIONS: The findings in this first double-blind study directly comparing the effects of L-T4 and T3 therapy against placebo provide evidence for the benefit of adjunctive L-T4 in alleviating resistant depression, reducing time in mixed states and increasing time euthymic. Adjunctive T3 did not show statistically significant evidence of benefit over placebo in reducing the time spent in disturbed mood states.

A Bayesian confirmatory factor model for multivariate observations in the form of two-way tables of data.

Researchers collected multiple measurements on patients with schizophrenia and their relatives, as well as control subjects and their relatives, to study vulnerability factors for schizophrenics and their near relatives. Observations across individuals from the same family are correlated, and also the multiple outcome measures on the same individuals are correlated. Traditional data analyses model outcomes separately and thus do not provide information about the interrelationships among outcomes. We propose a novel Bayesian family factor model (BFFM), which extends the classical confirmatory factor analysis model to explain the correlations among observed variables using a combination of family-member and outcome factors. Traditional methods for fitting confirmatory factor analysis models, such as full-information maximum likelihood (FIML) estimation using quasi-Newton optimization (QNO), can have convergence problems and Heywood cases (lack of convergence) caused by empirical underidentification. In contrast, modern Bayesian Markov chain Monte Carlo handles these inference problems easily. Simulations compare the BFFM to FIML-QNO in settings where the true covariance matrix is identified, close to not identified, and not identified. For these settings, FIML-QNO fails to fit the data in 13%, 57%, and 85% of the cases, respectively, while MCMC provides stable estimates. When both methods successfully fit the data, estimates from the BFFM have smaller variances and comparable mean-squared errors. We illustrate the BFFM by analyzing data on data from schizophrenics and their family members.

Robust functional clustering of ERP data with application to a study of implicit learning in autism.

Motivated by a study on visual implicit learning in young children with Autism Spectrum Disorder (ASD), we propose a robust functional clustering (RFC) algorithm to identify subgroups within electroencephalography (EEG) data. The proposed RFC is an iterative algorithm based on functional principal component analysis, where cluster membership is updated via predictions of the functional trajectories obtained through a non-parametric random effects model. We consider functional data resulting from event-related potential (ERP) waveforms representing EEG time-locked to stimuli over the course of an implicit learning experiment, after applying a previously proposed meta-preprocessing step. This meta-preprocessing is designed to increase the low signal-to-noise ratio in the raw data and to mitigate the longitudinal changes in the ERP waveforms which characterize the nature and speed of learning. The resulting functional ERP components (peak amplitudes and latencies) inherently exhibit covariance heterogeneity due to low data quality over some stimuli inducing the averaging of different numbers of waveforms in sliding windows of the meta-preprocessing step. The proposed RFC algorithm incorporates this known covariance heterogeneity into the clustering algorithm, improving cluster quality, as illustrated in the data application and extensive simulation studies. ASD is a heterogeneous syndrome and identifying subgroups within ASD children is of interest for understanding the diverse nature of this complex disorder. Applications to the implicit learning paradigm identify subgroups within ASD and typically developing children with diverse learning patterns over the course of the experiment, which may inform clinical stratification of ASD.

Are Centers for Disease Control and Prevention Guidelines for Preexposure Prophylaxis Specific Enough? Formulation of a Personalized HIV Risk Score for Pre-Exposure Prophylaxis Initiation.

BACKGROUND: Preexposure prophylaxis (PrEP) has emerged as a human immunodeficiency virus (HIV) prevention tool for populations at highest risk for HIV infection. Current US Centers for Disease Control and Prevention (CDC) guidelines for identifying PrEP candidates may not be specific enough to identify gay, bisexual, and other men who have sex with men (MSM) at the highest risk for HIV infection. We created an HIV risk score for HIV-negative MSM based on Syndemics Theory to develop a more targeted criterion for assessing PrEP candidacy. METHODS: Behavioral risk assessment and HIV testing data were analyzed for HIV-negative MSM attending the Los Angeles LGBT Center between January 2009 and June 2014 (n = 9481). Syndemics Theory informed the selection of variables for a multivariable Cox proportional hazards model. Estimated coefficients were summed to create an HIV risk score, and model fit was compared between our model and CDC guidelines using the Akaike Information Criterion and Bayesian Information Criterion. RESULTS: Approximately 51% of MSM were above a cutpoint that we chose as an illustrative risk score to qualify for PrEP, identifying 75% of all seroconverting MSM. Our model demonstrated a better overall fit when compared with the CDC guidelines (Akaike Information Criterion Difference = 68) in addition to identifying a greater proportion of HIV infections. CONCLUSIONS: Current CDC PrEP guidelines should be expanded to incorporate substance use, partner-level, and other Syndemic variables that have been shown to contribute to HIV acquisition. Deployment of such personalized algorithms may better hone PrEP criteria and allow providers and their patients to make a more informed decision prior to PrEP use.

A multi-dimensional functional principal components analysis of EEG data.

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations.

Integrated Primary Medical-Behavioral Health Care for Adolescent and Young Adult Depression: Predictors of Service Use in the Youth Partners in Care Trial.

Objectives: Depression, a chronic and disabling condition, frequently has its first onset during adolescence, underscoring the value of early effective treatment and prevention. Integrated medical-behavioral health care provides one strategy for improving treatment access for adolescents and young adults (AYA). Methods: This study examined predictors of accessing treatment in a multisite randomized controlled trial evaluating an integrated collaborative care intervention aimed at improving access to evidence-based depression treatment through primary health care, compared with usual care. Results: The integrated care intervention was able to overcome barriers to care associated with an initial reluctance to pursue active treatment and older age. Service use was low in both conditions among less acculturated/non-English-speaking families. Conclusions: Results support the value of integrated medical-behavioral health care for improving rates of care. Findings highlight mechanisms by which integrated care may lead to improved rates of care and outcomes for AYA, an underserved and understudied group.