The evidence for amygdala processing of emotional items outside the focus of attention is mixed. We hypothesized that differences in attentional demands may, at least in part, explain prior discrepancies. In the present study, attention was manipulated by parametrically varying the difficulty of a central task, allowing us to compare responses evoked by unattended emotion-laden faces while the attentional load of a central task was varied. Reduced responses to unattended emotional stimuli may also reflect an active suppression of amygdala responses during difficult non-emotional tasks (cognitive modulation). To explicitly assess cognitive modulation, an experimental condition was used in which subjects performed the central task without the presence of irrelevant emotional stimuli. Our findings revealed that amygdala responses were modulated by the focus of attention. Stronger responses were evoked during a sex task (when faces were attended) relative to a bar-orientation task (when faces were unattended). Critically, a valence effect was observed in the right amygdala during low attentional demand conditions, but not during medium or high demand conditions. Moreover, performing a difficult non-emotional task alone was associated with signal decreases in a network of brain regions, including the amygdala. Such robust decreases demonstrate that cognitive modulation comprises a powerful factor in determining amygdala responses. Collectively, our findings reveal that both attentional resources and cognitive modulation govern the fate of unattended fearful faces in the amygdala.

Patients with epilepsy are at risk of traffic accidents when they have seizures while driving. However, driving is an essential part of normal daily life in many communities, and depriving patients of driving privileges can have profound consequences for their economic and social well being. In the current study, we collected ictal performance data from a driving simulator and two other video games in patients undergoing continuous video/EEG monitoring. We captured 22 seizures in 13 patients and found that driving impairment during seizures differed both in terms of magnitude and character, depending on the seizure type. Our study documents the feasibility of the prospective study of driving and other behaviors during seizures through the use of computerbased tasks. This methodology may be applied to further describe differential driving impairment in specific types of seizures and to gain data on anatomical networks disrupted in seizures that impair consciousness and driving safety.

Event knowledge is organized on the basis of goals that enable the selection of specific event sequences to organize everyday life activities. Although the medial prefrontal cortex represents event knowledge, little is known about its role in mediating event knowledge complexity. We used functional MRI to investigate the patterns of brain activation while healthy volunteers were engaged in the task of evaluating the complexity (i.e. numbers of events) of daily life activities selected on the basis of normative data. Within a left frontoparietal network, we isolated the medial frontopolar cortex as the only region that showed a linear relationship between changes in the blood oxygen level-dependent signal and changes in event knowledge complexity. Our results specify the importance of the medial frontopolar cortex in subserving event knowledge that is required to build and execute complex behavior.

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

Background Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved. Methods We present OASIS +, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS. Results Using a test set of 9566 admissions extracted from the MIMIC-III database, we show that OASIS + outperforms nine previously developed severity scoring methods (including OASIS) in predicting in-hospital mortality. Furthermore, our results show that the supervised learning algorithms considered in our experiments demonstrated higher predictive performance when trained using the observed clinical variables as opposed to OASIS subscores. Conclusions Our results suggest that there is room for improving the prognostic accuracy of the OASIS severity scores by replacing the simple linear additive scoring function with more sophisticated non-linear machine learning models such as RF and XGB.

Performance incentives for preventive care may encourage inappropriate testing, such as cancer screening for patients with short life expectancies. Defining screening colonoscopies for patients with a >50% 4-year mortality risk as inappropriate, the authors performed a pre-post analysis assessing the effect of introducing a cancer screening incentive on the proportion of screening colonoscopy orders that were inappropriate. Among 2078 orders placed by 23 attending physicians in 4 academic general internal medicine practices, only 0.6% (n = 6/1057) of screening colonoscopy orders in the preintervention period and 0.6% (n = 6/1021) of screening colonoscopy orders in the postintervention period were deemed "inappropriate." This study found no evidence that the incentive led to an increase in inappropriate screening colonoscopy orders.

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