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Open Access Publications from the University of California

Scholarly Works (32 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2019)

Highly structured data collected in a variety of biomedical applications such as electroencephalography (EEG) are discrete samples of a smooth functional process observed across both temporal and spatial dimensions. EEG data is conceptualized as region-referenced longitudinal functional data in which the functional dimension captures local signal dynamics, the longitudinal dimension tracks changes over the course of an experiment, and the regional dimension indexes spatial information across electrodes on the scalp. This complex data structure exhibits intricate dependencies with rich information but its dimensionality and size produce significant obstacles for interpretation, estimation, and inference. Motivated by a series of EEG studies in children with autism spectrum disorder (ASD), a set of computationally efficient methods for these high-dimensional data structures are proposed that both maintain information along each dimension and yield interpretable components and inferences.

The first half of the work considers decompositions of the total variation. To begin, a multi-dimensional functional principal components analysis (MD-FPCA) is introduced which decomposes the total variation into subject- and electrode-level components and for each level employs a two-stage functional principal components decomposition sequentially across functional and longitudinal time. Next, a hybrid principal components analysis (HPCA) for region-referenced longitudinal functional EEG data is proposed which utilizes both vector and functional principal components analyses and does not collapse information along any of the three dimensions of the data. The second half of the work shifts to modeling associations and introduces a covariate-adjusted region-referenced generalized functional linear model (CARR-GFLM) for modeling scalar outcomes from region-referenced functional predictors. CARR-GFLM 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 non-functional covariates, such as age. Proposed methods not only help identify neurodevelopmental differences between typically developing and ASD children but can also be used to study the heterogeneity within children with ASD. The performance of all proposed methods is studied via extensive simulations.

    Cover page: Modeling Region-Referenced Longitudinal Functional Electroencephalography Data
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2023)
    Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.
      Cover page: Bayesian adaptive design for covariate-adaptive historical control information borrowing.
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2019)
      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.
        Cover page: Covariate‐adjusted region‐referenced generalized functional linear model for EEG data
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2015)
        Prevention of mother-to-child transmission services are integrated into antenatal care in South Africa, but post-birth care is at HIV clinics. Almost all Mothers Living with HIV (MLH) in 24 township neighborhoods (N = 324) reported engagement in HIV care from pregnancy to 36 months post-birth. Less than half re-engaged in HIV care at 6 months (45%), 52.5% at 18 months, and 62.5% at 36 months. Most were prescribed antiretroviral drugs if re-engaged in care, yet only about half (53%) are on antiretroviral drugs at 36 months post-birth. Implementation of Option B+ will require substantially better engagement in care.
          Cover page: Re-engagement in HIV care among mothers living with HIV in South Africa over 36 months post-birth
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2020)
          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.
            Cover page: Hybrid principal components analysis for region-referenced longitudinal functional EEG data.
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2017)
            Mothers living with HIV (MLH) and their children are typically studied to ensure that perinatal HIV transmission is blocked. Yet, HIV impacts MLH and their children lifelong. We examine child outcomes from pregnancy to 3 years post-birth among a peri-urban population of pregnant MLH and mothers without HIV (MWOH). Almost all pregnant women in 12 neighborhoods (98 %; N = 584) in Cape Town, South Africa were recruited and repeatedly assessed within 2 weeks of birth (92 %), at 6 months (88 %), 18 months (84 %), and 3 years post-birth (86 %). There were 186 MLH and 398 MWOH. Controlling for neighborhood and repeated measures, child and maternal outcomes were contrasted over time using longitudinal random effects regression analyses. For measures collected only at 3 years, outcomes were analyzed using multiple regressions. Compared to MWOH, MLH had less income, more informal housing and food insecurity, used alcohol more often during pregnancy, and were more depressed during pregnancy and over time. Only 4.8 % of MLH's children were seropositive; seropositive children were excluded from additional analyses. Children of MLH tended to have significantly lower weights (p < .10) over time (i.e., lower weight-for-age Z-scores) and were also hospitalized significantly more often than children of MWOH (p < .01). Children of MLH and MWOH died at similar rates (8.5 %) and were similar in social and behavioral adjustment, vocabulary, and executive functioning at 3 years post-birth. Despite living in households with fewer resources and having more depressed mothers, only the physical health of children of MLH is compromised, compared to children of MWOH. In township neighborhoods with extreme poverty, social, behavioral, language, and cognitive functioning appear similar over the first three years of life between children of MLH and MWOH.
              Cover page: Adjustment of a Population of South African Children of Mothers Living With/and Without HIV Through Three Years Post-Birth
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2015)

              Background

              Self-monitoring by mobile phone applications offers new opportunities to engage patients in self-management. Self-monitoring has not been examined thoroughly as a self-directed intervention strategy for self-management of multiple behaviors and states by people living with HIV (PLH).

              Methods

              PLH (n = 50), primarily African American and Latino, were recruited from 2 AIDS services organizations and randomly assigned to daily smartphone (n = 34) or biweekly Web-survey only (n = 16) self-monitoring for 6 weeks. Smartphone self-monitoring included responding to brief surveys on medication adherence, mental health, substance use, and sexual risk behaviors, and brief text diaries on stressful events. Qualitative analyses examine biweekly open-ended user-experience interviews regarding perceived benefits and barriers of self-monitoring, and to elaborate a theoretical model for potential efficacy of self-monitoring to support self-management for multiple domains.

              Results

              Self-monitoring functions include reflection for self-awareness, cues to action (reminders), reinforcements from self-tracking, and their potential effects on risk perceptions, motivations, skills, and behavioral activation states. Participants also reported therapeutic benefits related to self-expression for catharsis, nonjudgmental disclosure, and in-the-moment support. About one-third of participants reported that surveys were too long, frequent, or tedious. Some smartphone group participants suggested that daily self-monitoring was more beneficial than biweekly due to frequency and in-the-moment availability. About twice as many daily self-monitoring group participants reported increased awareness and behavior change support from self-monitoring compared with biweekly Web-survey only participants.

              Conclusions

              Self-monitoring is a potentially efficacious disruptive innovation for supporting self-management by PLH and for complementing other interventions, but more research is needed to confirm efficacy, adoption, and sustainability.
                Cover page: Smartphone Self-Monitoring to Support Self-Management Among People Living With HIV
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2017)
                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.
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2018)
                  Diverse genetic and environmental etiologies converge onto circuit level brain dysfunction in autism spectrum disorder (ASD), manifesting at a macroscopic level as aberrant neural connectivity. Previous studies have described atypical patterns of decreased short range and increased long range connectivity in ASD [1]. However, it remains unclear whether group level features of circuit dysfunction are consistently present across the range of cognitive function seen in the autism spectrum. The dynamics of neural oscillations in the alpha range (6-12 Hz) are exquisitely sensitive to healthy development of functional and structural connectivity. Alpha-band coherence, measured with high temporal-precision electroencephalography (EEG) therefore represents an ideal tool for studying neural connectivity in developmental populations. Here we examined spontaneous alpha phase coherence in a heterogeneous sample of 59 children with ASD and 39 age matched typically developing children. Using a data driven approach, we conducted an unbiased examination of all possible atypical connectivity patterns across all cortical regions. Long-range hypoconnectivity was present in children with ASD compared to typically developing children, with temporal interhemispheric connectivity showing the largest difference between the two groups. Decreased long range alpha coherence distinguishes a heterogeneous group of ASD children from typically developing children. Interhemispheric temporal hypoconnectivity represents a fundamental functional difference in children with ASD across a wide cognitive and age range that may reflect white matter disturbances or increased signal variability at temporal sites in ASD.
                    Cover page: Interhemispheric alpha-band hypoconnectivity in children with autism spectrum disorder
                    • Article
                    • Peer Reviewed
                    UC San Francisco Previously Published Works (2024)
                    OBJECTIVE: Selective serotonin reuptake inhibitor (SSRI) use is common in pregnancy. It is associated with delayed neonatal adaptation. Most previous studies have not adjusted for the severity of maternal mental health disorders or examined the impact of SSRI type and dosage. We examined whether treatment with SSRIs in late pregnancy (after 20 weeks) is associated with delayed neonatal adaptation independent of maternal depression and anxiety. DESIGN, SETTING AND PATIENTS: Retrospective population-based birth cohort of 280 090 term infants born at 15 Kaiser Permanente Northern California hospitals, 2011-2019. Individual-level pharmacy, maternal, pregnancy and neonatal data were obtained from electronic medical records. EXPOSURE: Dispensed maternal SSRI prescription after 20 weeks of pregnancy. MAIN OUTCOME MEASURES: Delayed neonatal adaptation defined as a 5 min Apgar score ≤5, resuscitation at birth or admission to a neonatal intensive care unit for respiratory support. Secondary outcomes included each individual component of the primary outcome and more severe neonatal outcomes (pulmonary hypertension, hypoxic-ischaemic encephalopathy and seizures). RESULTS: 7573 (2.7%) infants were exposed to SSRIs in late pregnancy. Delayed neonatal adaptation occurred in 11.2% of exposed vs 4.4% of unexposed infants (relative risk 2.52 (95% CI 2.36 to 2.70)). After multivariable adjustment, there was an association between SSRI exposure and delayed neonatal adaptation (adjusted OR 2.14 (95% CI 1.96 to 2.32)). This association was dose dependent. Escitalopram and fluoxetine were associated with the highest risk of delayed neonatal adaptation. CONCLUSIONS: Infants exposed to SSRIs have increased risks of delayed adaptation in a type and dose-dependent relationship, pointing toward a causal relationship.
                      Cover page: Maternal treatment with selective serotonin reuptake inhibitors during pregnancy and delayed neonatal adaptation: a population-based cohort study.
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