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Speech-in-speech recognition can be challenging, and listeners vary considerably in their ability to accomplish this complex auditory-cognitive task. Variability in performance can be related to intrinsic listener factors as well as stimulus factors associated with energetic and informational masking. The current experiments characterized the effects of short-term audibility of the target, differences in target and masker talker sex, and intrinsic listener variables on sentence recognition in two-talker speech and speech-shaped noise. Participants were young adults with normal hearing. Each condition included the adaptive measurement of speech reception thresholds, followed by testing at a fixed signal-to-noise ratio (SNR). Short-term audibility for each keyword was quantified using a computational glimpsing model for target+masker mixtures. Scores on a psychophysical task of auditory stream segregation predicted speech recognition, with stronger effects for speech-in-speech than speech-in-noise. Both speech-in-speech and speech-in-noise recognition depended on the proportion of audible glimpses available in the target+masker mixture, even across stimuli presented at the same global SNR. Short-term audibility requirements varied systematically across stimuli, providing an estimate of the greater informational masking for speech-in-speech than speech-in-noise recognition and quantifying informational masking for matched and mismatched talker sex.

Purpose: The analysis of Ecological Momentary Assessment (EMA) data can be difficult to conceptualize due to the complexity of how the data are collected. The goal of this tutorial is to provide an overview of statistical considerations for analyzing observational data arising from EMA studies. Method: EMA data are collected in a variety of ways, complicating the statistical analysis. We focus on fundamental statistical characteristics of the data and general purpose statistical approaches to analyzing EMA data. We implement those statistical approaches using a recent study involving EMA. Results: The linear or generalized linear mixed-model statistical approach can adequately capture the challenges resulting from EMA collected data if properly set up. Additionally, while sample size depends on both the number of participants and the number of survey responses per participant, having more participants is more important than the number of responses per participant. Conclusion: Using modern statistical methods when analyzing EMA data and adequately considering all of the statistical assumptions being used can lead to interesting and important findings when using EMA. Supplemental Material: https://doi.org/10.23641/asha.17155961

IntroductionHealthy aging is typically associated with cognitive decline and lower negative affect. Previous studies have reported a significant and opposite role of the amygdala in relation to cognitive and affective processing in early adulthood. However, it remains unclear how aging impacts such relationships.MethodsSeventy-seven healthy participants including 40 young (mean age = 26.1 years) and 37 older (mean age = 61.8 years) adults completed a functional MRI Affective Stroop (AS) paradigm, a cognitive battery, and the state-trait anxiety inventory. The AS fMRI paradigm included “task trials,” where participants saw a positively, negatively or neutrally valenced distractor image, followed by a numerical display, followed by another distractor image. We extracted signal in both amygdalas during the AS Task and compared it across all conditions and age group. We further conducted moderation analyses to investigate the impact of aging on the relationship between amygdala activation and anxiety or cognitive variables, respectively.ResultsAt the behavioral level, older participants showed lower trait anxiety than the younger adults (p = 0.002). While overall slower during the AS task, older adults achieved comparable accuracy during the AS task, relative to the younger adults. At the brain level, we revealed a significant interaction between age group and trial types in amygdala activation (F = 4.9, p = 0.03), with the older group showing stronger activation during the most complex trials compared to the passive view trials. We further found that age significantly modulated the relationship between anxiety and the left amygdala activation during negative stimuli, where the younger adults showed a positive association while the older adults showed a negative association. Age also significantly modulated the relationship between verbal fluency and left amygdala activation during incongruent versus view trials, with the younger adults showing a negative association and the older adults showing a positive association.DiscussionThe current study suggests that the role of the amygdala on both emotional processing and cognitive traits changes between early and late adulthood.

ObjectiveThis study assessed the relationship between electrophysiological measures of the electrically evoked compound action potential (eCAP) and speech perception scores measured in quiet and in noise in post-lingually deafened adult cochlear implant (CI) users. It tested the hypothesis that how well the auditory nerve (AN) responds to electrical stimulation is important for speech perception with a CI in challenging listening conditions.DesignStudy participants included 24 post-lingually deafened adult CI users. All participants used Cochlear® Nucleus™ CIs in their test ears. In each participant, eCAPs were measured at multiple electrode locations in response to single-pulse, paired-pulse, and pulse-train stimuli. Independent variables included six parameters calculated from the eCAP recordings: the electrode-neuron interface (ENI) index, the neural adaptation (NA) ratio, NA speed, the adaptation recovery (AR) ratio, AR speed, and the amplitude modulation (AM) ratio. The ENI index quantified the effectiveness of the CI electrodes in stimulating the targeted AN fibers. The NA ratio indicated the amount of NA at the AN caused by a train of constant-amplitude pulses. NA speed was defined as the speed/rate of NA. The AR ratio estimated the amount of recovery from NA at a fixed time point after the cessation of pulse-train stimulation. AR speed referred to the speed of recovery from NA caused by previous pulse-train stimulation. The AM ratio provided a measure of AN sensitivity to AM cues. Participants’ speech perception scores were measured using Consonant-Nucleus-Consonant (CNC) word lists and AzBio sentences presented in quiet, as well as in noise at signal-to-noise ratios (SNRs) of +10 and +5 dB. Predictive models were created for each speech measure to identify eCAP parameters with meaningful predictive power.ResultsThe ENI index and AR speed had significant bivariate relationships with speech perception scores measured in this study, while the NA ratio, NA speed, the AR ratio, and the AM ratio did not. The ENI index was identified as the only eCAP parameter that had unique predictive power for each of the speech test results. The amount of variance in speech perception scores (both CNC words and AzBio sentences) explained by the eCAP parameters increased with increased difficulty in the listening condition. Over half of the variance in speech perception scores measured in +5 dB SNR noise (both CNC words and AzBio sentences) was explained by a model with only three eCAP parameters: the ENI index, NA speed, and AR speed.ConclusionsThe ENI index is the most informative predictor for speech perception performance in CI users. The response characteristics of the AN to electrical stimulation is more important for speech perception with a CI in noise than it is in quiet.

Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.

Visceral leishmaniasis (VL) is a neglected tropical disease that is globally distributed and has the potential to cause very serious illness. Prior literature highlights the emergence and spread of VL is influenced by multiple factors, such as socioeconomic status, sanitation levels or animal and human reservoirs. The study aimed to retrospectively investigate the presence and infectiousness of VL in Rio Grande do Norte (RN), Brazil between 2007 and 2020. We applied a hierarchical Bayesian approach to estimate municipality-specific relative risk of VL across space and time. The results show evidence that lower socioeconomic status is connected to higher municipality-specific VL risk. Overall, estimates reveal spatially heterogeneous VL risks in RN, with a high probability that VL risk for municipalities within the West Potiguar mesoregion are more than double the expected VL risk. Additionally, given the data available, results indicate there is a high probability of increasing VL risk in the municipalities of Natal, Patu and Pau dos Ferros. These findings demonstrate opportunities for municipality-specific public health policy interventions and warrant future research on identifying epidemiological drivers in at-risk regions.

Objectives:To explore associations between maternal pre-pregnancy exposure to arsenic in diet and non-cardiac birth defects.Design:This is a population-based, case–control study using maternal responses to a dietary assessment and published arsenic concentration estimates in food items to calculate average daily total and inorganic arsenic exposure during the year before pregnancy. Assigning tertiles of total and inorganic arsenic exposure, logistic regression analysis was used to estimate OR for middle and high tertiles, compared to the low tertile.Setting:US National Birth Defects Prevention Study, 1997–2011.Participants:Mothers of 10 446 children without birth defects and 14 408 children diagnosed with a non-cardiac birth defect.Results:Maternal exposure to total dietary arsenic in the middle and high tertiles was associated with a threefold increase in cloacal exstrophy, with weak positive associations (1·2–1·5) observed either in both tertiles (intercalary limb deficiency) or the high tertile only (encephalocele, glaucoma/anterior chamber defects and bladder exstrophy). Maternal exposure to inorganic arsenic showed mostly weak, positive associations in both tertiles (colonic atresia/stenosis, oesophageal atresia, bilateral renal agenesis/hypoplasia, hypospadias, cloacal exstrophy and gastroschisis), or the high (glaucoma/anterior chamber defects, choanal atresia and intestinal atresia stenosis) or middle (encephalocele, intercalary limb deficiency and transverse limb deficiency) tertiles only. The remaining associations estimated were near the null or inverse.Conclusions:This exploration of arsenic in diet and non-cardiac birth defects produced several positive, but mostly weak associations. Limitations in exposure assessment may have resulted in exposure misclassification. Continued research with improved exposure assessment is recommended to identify if these associations are true signals or chance findings.

BACKGROUND AND PURPOSE: To explore the potential for simplified measures of [(11)C]PIB uptake to serve as a surrogate for cerebral blood flow (CBF) measures, thereby, providing both pathological and functional information in the same scan. METHODS: Participants (N=24,16M,8F,57–87 years) underwent quantitative [(15)O]water imaging and dynamic [(11)C]PIB imaging. Time-activity curves (TACs) were created for each participant’s regional [(11)C]PIB data scaled in standardized uptake values (SUVs). The frame in which maximal uptake occurred was defined for each subject (i.e., “peak”). The concentration (SUV) for each region at the individual’s peak, during the 3.5 to 4 minute time interval and for the initial 6 minute sum were determined. R1 (i.e., relative delivery using cerebellum as reference tissue) from the simplified reference tissue model 2 (SRTM2) was determined for each region. PIB SUVs were compared to the absolute CBF global and regional values (in mL/min/100mL) and the R1 values were compared to the cerebellar-normalized rCBF. RESULTS: Significant linear relationships were found for all SUV measures with measures of absolute global and regional CBF that were comparable to the relationship between normalized CBF and R1. The individual SUV(peak) exhibited the strongest relationship both regionally and globally. All individuals and all regions had highly significant regression slopes. Age, gender or amyloid burden did not influence the relationship. CONCLUSION: Early PIB uptake has the potential to effectively serve as a surrogate for global and regional cerebral blood flow measures. The simple and readily obtainable individual’s SUV(peak) value was the strongest predictor regionally and globally of CBF.

The various thresholding quantities grouped under the "Basic Reproductive Number" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the "empirically adjusted reproductive number" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach.

Summary. An important aspect of statistical modelling involves the identification of cases that have a significant influence on certain inferential results. In modelling problems where data are missing, the predicted values for the missing observations are frequently of interest. To assist in the identification of cases that substantially affect these predicted values, we introduce a case deletion diagnostic which is often conveniently evaluated in the setting of the EM algorithm. Our diagnostic is defined as the Kullback-Leibler information between two versions of the conditional density of the missing data given the observed data: one based on the parameter estimates arising from the full data set; the other based on the parameter estimates arising from the case-deleted data set. We outline the computation of the diagnostic for two Gaussian frameworks: for bivariate data applications in which some of the data pairs are incomplete, and for time series forecasting applications in which the missing observations are future realizations of the series. Our analyses involve bivariate data from the 1998 American Major League Baseball season and a time series consisting of cardiovascular mortality readings from the Los Angeles area.

Most current Bayesian SEIR (Susceptible, Exposed, Infectious, Removed (or Recovered)) models either use exponentially distributed latent and infectious periods, allow for a single distribution on the latent and infectious period, or make strong assumptions regarding the quantity of information available regarding time distributions, particularly the time spent in the exposed compartment. Many infectious diseases require a more realistic assumption on the latent and infectious periods. In this article, we provide an alternative model allowing general distributions to be utilized for both the exposed and infectious compartments, while avoiding the need for full latent time data. The alternative formulation is a path-specific SEIR (PS SEIR) model that follows individual paths through the exposed and infectious compartments, thereby removing the need for an exponential assumption on the latent and infectious time distributions. We show how the PS SEIR model is a stochastic analog to a general class of deterministic SEIR models. We then demonstrate the improvement of this PS SEIR model over more common population averaged models via simulation results and perform a new analysis of the Iowa mumps epidemic from 2006.