Synchrony of physiological activity during mother-child interaction: moderation by maternal history of major depressive disorder

The family environment plays an important role in the intergenerational transmission of MDD, but less is known about how day-to-day mother-child interactions may be disrupted in families with a history of MDD. Disruptions in mother-child synchrony, the dynamic and convergent exchange of physiological and behavioral cues during interactions, may be one important risk factor. Although maternal MDD is associated with a lack of mother-child synchrony at the behavioral level, no studies have examined the impact of maternal MDD on physiological synchrony. Therefore, the current study examined whether maternal history of MDD moderates mother-child physiological synchrony (measured via RSA) during positive and negative discussions

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Selective attention toward angry faces and risk for major depressive disorder in women: Converging evidence from retrospective and prospective analyses.

The current study examined selective attention toward emotional images as a risk factor for major depressive disorder (MDD). Using multiple indices of attention in a dot-probe task (i.e., reaction time [RT] and eye-tracking-based measures) in a retrospective, high-risk design, we found that women with remitted MDD, compared with controls, exhibited greater selective attention toward angry faces across RT and eye-tracking indices and greater attention toward sad faces for RT measures. Second, we followed women with remitted MDD prospectively to determine if the attentional biases retrospectively associated with MDD history would predict MDD recurrence across a 2-year follow-up. We found that women who spent a greater proportion of time looking at angry faces during the dot-probe task at the baseline assessment had a significantly shorter time to MDD onset. Taken together, these findings provide converging retrospective and prospective evidence that selective attention toward angry faces may increase risk for MDD recurrence

Influence of worry on sustained attention to emotional stimuli: Evidence from the late positive potential.

There is preliminary evidence to suggest that worry is associated with dysregulated emotion processing resulting from sustained attention to emotional versus neutral stimuli; however, this hypothesis has not been directly tested in prior research. Therefore, the current study used the event-related late positive potential (LPP) to directly examine if high levels of trait worry moderate sustained attention to emotional versus neutral stimuli. Electroencephalogram data was recorded while twenty-two women passively viewed neutral, positive, dysphoric, and threatening emotional images. Consistent with our hypotheses, higher levels of worry were associated with larger LPP amplitudes for emotional images but not neutral images. Importantly, the positive correlations between trait worry and LPP responses to threatening and positive images were maintained even when controlling for the influence of current anxiety symptoms, suggesting that worry may influence emotion processing whether or not the person is currently anxious. This sustained attention to emotional information may be one mechanism underlying how trait worry increases risk for anxiety disorders

Pupillary reactivity to negative stimuli prospectively predicts recurrence of major depressive disorder in women.

There is a large body of research supporting the association between disrupted physiological reactivity to negative stimuli and depression. The present study aimed to examine whether physiological reactivity to emotional stimuli, assessed via pupil dilation, served as a biological marker of risk for depression recurrence among individuals who are known to be at a higher risk due to having previous history of depression. Participants were 57 women with a history of major depressive disorder (MDD). Pupil dilation to angry, happy, sad, and neutral faces was recorded. Participants’ diagnoses and symptoms were assessed 24 months after the initial assessment. We found that women’s pupillary reactivity to negative (sad or angry faces) but not positive stimuli prospectively predicted MDD recurrence. Additionally, we found that both hyper- and hypopupillary reactivity to angry faces predicted risk for MDD recurrence. These findings suggest that disrupted physiological response to negative stimuli indexed via pupillary dilation could serve as a physiological marker of MDD risk, thus presenting clinicians with a convenient and inexpensive method to predict which of the at-risk women are more likely to experience depression recurrence