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Open Access Publications from the University of California

Department of Statistics

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Open Access Policy Deposits

This series is automatically populated with publications deposited by UC Irvine Donald Bren School of Information and Computer Sciences Department of Statistics researchers in accordance with the University of California’s open access policies. For more information see Open Access Policy Deposits and the UC Publication Management System.

Symptoms of obstructive sleep apnea are associated with less frequent exercise and worse subjective cognitive function across adulthood.


To determine whether subjective measures of exercise and sleep are associated with cognitive complaints and whether exercise effects are mediated by sleep. This study analyzed questionnaire data from adults (18-89) enrolled in a recruitment registry. The Cognitive Function Instrument (CFI) assessed cognitive complaints. Medical Outcomes Study Sleep Scale (MOS-SS) subscales and factor scores assessed sleep quality, daytime sleepiness, nighttime disturbance, and insomnia and obstructive sleep apnea (OSA)-like symptoms. Exercise frequency was defined as the weekly number of exercise sessions. Exercise frequency, MOS-SS subscales, and factor scores were examined as predictors of CFI score, adjusting for age, body mass index, education, sex, cancer diagnosis, antidepressant usage, psychiatric conditions, and medical comorbidities. Analyses of covariance examined the relationship between sleep duration groups (short, mid-range, and long) and CFI score, adjusting for covariates. Mediation by sleep in the exercise-CFI score relationship was tested. Data from 2106 adults were analyzed. Exercise and MOS-SS subscales and factor scores were associated with CFI score. Higher Sleep Adequacy scores were associated with fewer cognitive complaints, whereas higher Sleep Somnolence, Sleep Disturbance, Sleep Problems Index I, Sleep Problems Index II, and factor scores were associated with more cognitive complaints. MOS-SS subscales and factor scores, except Sleep Disturbance and the insomnia factor score, mediated the association between exercise and cognitive complaints. The relationship between exercise frequency and subjective cognitive performance is mediated by sleep. In particular, the mediation effect appears to be driven by symptoms possibly suggestive of OSA which are negatively associated with exercise engagement, sleep quality, daytime sleepiness, and subjective cognitive performance.

Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events.


The hippocampus is critical to the temporal organization of our experiences. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain unclear. Here we recorded hippocampal activity as rats remembered an extended sequence of nonspatial events unfolding over several seconds, as in daily life episodes in humans. We then developed statistical machine learning methods to analyze the ensemble activity and discovered forms of sequential organization and coding important for order memory judgments. Specifically, we found that hippocampal ensembles provide significant temporal coding throughout nonspatial event sequences, differentiate distinct types of task-critical information sequentially within events, and exhibit theta-associated reactivation of the sequential relationships among events. We also demonstrate that nonspatial event representations are sequentially organized within individual theta cycles and precess across successive cycles. These findings suggest a fundamental function of the hippocampal network is to encode, preserve, and predict the sequential order of experiences.

Cover page of A cross-species assay demonstrates that reward responsiveness is enduringly impacted by adverse, unpredictable early-life experiences.

A cross-species assay demonstrates that reward responsiveness is enduringly impacted by adverse, unpredictable early-life experiences.


Exposure to early-life adversity (ELA) is associated with several neuropsychiatric conditions, including major depressive disorder, yet causality is difficult to establish in humans. Recent work in rodents has implicated impaired reward circuit signaling in anhedonic-like behavior after ELA exposure. Anhedonia, the lack of reactivity to previously rewarding stimuli, is a transdiagnostic construct common to mental illnesses associated with ELA. Here, we employed an assay of reward responsiveness validated across species, the Probabilistic Reward Task (PRT). In the PRT, healthy participants reliably develop a response bias toward the more richly rewarded stimulus, whereas participants with anhedonia exhibit a blunted response bias that correlates with current and future anhedonia. In a well-established model of ELA that generates a stressful, chaotic, and unpredictable early-life environment, ELA led to blunted response biases in the PRT in two separate cohorts, recapitulating findings in humans with anhedonia. The same ELA rats had blunted sucrose preference, further supporting their anhedonic-like phenotypes. Probing the aspects of ELA that might provoke these deficits, we quantified the unpredictability of dam/pup interactions using entropy measures and found that the unpredictability of maternal care was significantly higher in the ELA groups in which PRT and sucrose preference reward deficits were present later in life. Taken together, these data position the PRT, established in clinical patient populations, as a potent instrument to assess the impact of ELA on the reward circuit across species. These findings also implicate the unpredictability of maternal signals during early life as an important driver of reward sensitivity deficits.

Cover page of Predictors of Test Positivity, Mortality, and Seropositivity during the Early Coronavirus Disease Epidemic, Orange County, California, USA.

Predictors of Test Positivity, Mortality, and Seropositivity during the Early Coronavirus Disease Epidemic, Orange County, California, USA.


We conducted a detailed analysis of coronavirus disease in a large population center in southern California, USA (Orange County, population 3.2 million), to determine heterogeneity in risks for infection, test positivity, and death. We used a combination of datasets, including a population-representative seroprevalence survey, to assess the actual burden of disease and testing intensity, test positivity, and mortality. In the first month of the local epidemic (March 2020), case incidence clustered in high-income areas. This pattern quickly shifted, and cases next clustered in much higher rates in the north-central area of the county, which has a lower socioeconomic status. Beginning in April 2020, a concentration of reported cases, test positivity, testing intensity, and seropositivity in a north-central area persisted. At the individual level, several factors (e.g., age, race or ethnicity, and ZIP codes with low educational attainment) strongly affected risk for seropositivity and death.

Cover page of A Horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging

A Horseshoe mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging


Most classical screening methods partition the units (e.g., brain regions) into two classes: significant and non-significant. In our context, some binary grouping methods lead to overly simplistic discoveries by filtering out weak but important signals, that could be adulterated by the noise present in the data. To overcome this limitation, we introduce a new Bayesian approach that can classify the brain regions into several tiers with varying degrees of relevance. Our approach is based on a combination of shrinkage priors - widely used in regression and multiple hypothesis testing problems - and mixture models - commonly used in model-based clustering. In contrast to the existing regularizing prior distributions, which either use the spike-and-slab prior or continuous scale mixtures, our class of priors is based on a discrete mixture of continuous scale mixtures and As a result, our approach provides a more general setting for Bayesian sparse estimation, drastically reduces the number of shrinkage parameters needed, and creates a framework for sharing information across units of interest. This approach leads to more biologically meaningful and interpretable results in our brain imaging problem, allowing the discrimination between active and inactive regions, while ranking the discoveries into clusters representing tiers of similar importance.

Cover page of Prenatal maternal mood entropy is associated with child neurodevelopment.

Prenatal maternal mood entropy is associated with child neurodevelopment.


Emerging evidence indicates that the predictability of signals early in life may influence the developing brain. This study examines links between a novel indicator of maternal mood dysregulation, mood entropy, and child neurodevelopmental outcomes. Associations between prenatal maternal mood entropy and child neurodevelopment were assessed in 2 longitudinal cohorts. Maternal mood was measured several times over pregnancy beginning as early as 15 weeks' gestation. Shannon's mood entropy was applied to distributions of mothers' responses on mood questionnaires. Child cognitive and language development were evaluated at 2 and 6-9 years of age. Higher prenatal maternal mood entropy was associated with lower cognitive development scores at 2 years of age and lower expressive language scores at 6-9 years of age. These associations persisted after adjusting for maternal pre and postnatal mood levels and for other relevant sociodemographic factors. Our findings identify maternal mood entropy as a novel predictor of child neurodevelopment. Characterizing components of maternal mood in addition to level of severity or valence may further our understanding of specific processes by which maternal mood shapes child development. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

The case for formal methodology in scientific reform.


Current attempts at methodological reform in sciences come in response to an overall lack of rigor in methodological and scientific practices in experimental sciences. However, most methodological reform attempts suffer from similar mistakes and over-generalizations to the ones they aim to address. We argue that this can be attributed in part to lack of formalism and first principles. Considering the costs of allowing false claims to become canonized, we argue for formal statistical rigor and scientific nuance in methodological reform. To attain this rigor and nuance, we propose a five-step formal approach for solving methodological problems. To illustrate the use and benefits of such formalism, we present a formal statistical analysis of three popular claims in the metascientific literature: (i) that reproducibility is the cornerstone of science; (ii) that data must not be used twice in any analysis; and (iii) that exploratory projects imply poor statistical practice. We show how our formal approach can inform and shape debates about such methodological claims.

Cover page of To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data

To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data


With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. Our simulations and applications to real data suggest that the estimated spike data outperform calcium trace data for both clustering and PCA. Although calcium trace data show higher predictability than spike data at each time point, spike history or cumulative spike counts is comparable to or better than calcium traces in population decoding.

Cover page of A Bayesian nonparametric model for textural pattern heterogeneity

A Bayesian nonparametric model for textural pattern heterogeneity


Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumour heterogeneity through patterns of enhancement, texture, morphology and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of grey-level co-occurrence matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine learning approaches currently used in cancer radiomics with simulation studies.

Cover page of Time-varying $\ell_0$ optimization for Spike Inference from Multi-Trial Calcium Recordings

Time-varying $\ell_0$ optimization for Spike Inference from Multi-Trial Calcium Recordings


Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying $\ell_0$ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate function across trials due to learning.