Discriminating Formal Representations of Risk in Anterior Cingulate Cortex and Inferior Frontal Gyrus

Considerable debate persists around the definition of risk. Depending on the area of study, the concept of risk may be defined as the variance of the possible outcomes, the probability of a loss, or a combination of the loss probability and its maximum possible loss. Mounting evidence suggests the anterior cingulate cortex (ACC), including the surrounding medial prefrontal cortex (mPFC), and the anterior insula/inferior frontal gyrus (IFG) are key neural regions that represent perceived risks. Yet it remains unclear which of these formalisms best accounts for the pattern of activation in brain regions representing risk, and it is also difficult to disentangle risk from value, as both contribute to perceived utility. To adjudicate among the possible definitions, we used fMRI with a novel gambling task that orthogonalized the variance, loss probability, and maximum possible loss among the risky options, while maintaining a constant expected value across all monetary gambles to isolate the impact of risk rather than value. Here we show that when expected value is controlled for ACC and IFG activation reflect variance, but neither loss probability nor maximum possible loss. Across subjects, variance-related activation within the ACC correlates indirectly with risk aversion. Our results highlight the variance of the prospective outcomes as a formal representation of risk that is reflected both in brain activity and behavior, thus suggestive of a stronger link among formal economic theories of financial risk, naturalistic risk taking, and neural representations of risk

Neural activation during risky decision-making in youth at high risk for substance use disorders

Risky decision-making, particularly in the context of reward-seeking behavior, is strongly associated with the presence of substance use disorders (SUDs). However, there has been little research on the neural substrates underlying reward-related decision-making in drug-naïve youth who are at elevated risk for SUDs. Participants comprised 23 high-risk (HR) youth with a well-established SUD risk phenotype and 27 low-risk healthy comparison (HC) youth, aged 10-14. Participants completed the balloon analog risk task (BART), a task designed to examine risky decision-making, during functional magnetic resonance imaging. The HR group had faster reaction times, but otherwise showed no behavioral differences from the HC group. HR youth experienced greater activation when processing outcome, as the chances of balloon explosion increased, relative to HC youth, in ventromedial prefrontal cortex (vmPFC). As explosion probability increased, group-by-condition interactions in the ventral striatum/anterior cingulate and the anterior insula showed increasing activation in HR youth, specifically on trials when explosions occurred. Thus, atypical activation increased with increasing risk of negative outcome (i.e., balloon explosion) in a cortico-striatal network in the HR group. These findings identify candidate neurobiological markers of addiction risk in youth at high familial and phenotypic risk for SUDs

Identification and rejection of pile-up jets at high pseudorapidity with the ATLAS detector

The rejection of forward jets originating from additional proton–proton interactions (pile-up) is crucial for a variety of physics analyses at the LHC, including Standard Model measurements and searches for physics beyond the Standard Model. The identification of such jets is challenging due to the lack of track and vertex information in the pseudorapidity range |η| > 2.5. This paper presents a novel strategy for forward pile-up jet tagging that exploits jet shapes and topological jet correlations in pile-up interactions. Measurements of the per-jet tagging efficiency are presented using a data set of 3.2 fb−1 of proton–proton collisions at a centre-of-mass energy of 13 TeV collected with the ATLAS detector. The fraction of pile-up jets rejected in the range 2.5 < |η| < 4.5 is estimated in simulated events with an average of 22 interactions per bunch-crossing. It increases with jet transverse momentum and, for jets with transverse momentum between 20 and 50 GeV, it ranges between 49% and 67% with an efficiency of 85% for selecting hard-scatter jets. A case study is performed in Higgs boson production via the vector-boson fusion process, showing that these techniques mitigate the background growth due to additional proton–proton interactions, thus enhancing the reach for such signatures

Reward salience and risk aversion underlie differential ACC activity in substance dependence

The medial prefrontal cortex, especially the dorsal anterior cingulate cortex (ACC), has long been implicated in cognitive control and error processing. Although the association between ACC and behavior has been established, it is less clear how ACC contributes to dysfunctional behavior such as substance dependence. Evidence from neuroimaging studies investigating ACC function in substance users is mixed, with some studies showing disengagement of ACC in substance dependent individuals (SDs), while others show increased ACC activity related to substance use. In this study, we investigate ACC function in SDs and healthy individuals performing a change signal task for monetary rewards. Using a priori predictions derived from a recent computational model of ACC, we find that ACC activity differs between SDs and controls in factors related to reward salience and risk aversion between SDs and healthy individuals. Quantitative fits of a computational model to fMRI data reveal significant differences in best fit parameters for reward salience and risk preferences. Specifically, the ACC in SDs shows greater risk aversion, defined as concavity in the utility function, and greater attention to rewards relative to reward omission. Furthermore, across participants risk aversion and reward salience are positively correlated. The results clarify the role that ACC plays in both the reduced sensitivity to omitted rewards and greater reward valuation in SDs. Clinical implications of applying computational modeling in psychiatry are also discussed

Aligning staff schedules, testing, and isolation reduces the risk of COVID-19 outbreaks in carceral and other congregate settings: A simulation study

COVID-19 outbreaks in congregate settings remain a serious threat to the health of disproportionately affected populations such as people experiencing incarceration or homelessness, the elderly, and essential workers. An individual-based model accounting for individual infectiousness over time, staff work schedules, and testing and isolation schedules was developed to simulate community transmission of SARS-CoV-2 to staff in a congregate facility and subsequent transmission within the facility that could cause an outbreak. Systematic testing strategies in which staff are tested on the first day of their workweek were found to prevent up to 16% more infections than testing strategies unrelated to staff schedules. Testing staff at the beginning of their workweek, implementing timely isolation following testing, limiting test turnaround time, and increasing test frequency in high transmission scenarios can supplement additional mitigation measures to aid outbreak prevention in congregate settings

Analytic framework exploring effects of variable infectiousness through time, testing frequencies, and delays on SARS-CoV-2 transmission.

A) Example infectiousness profile for , tlatent = 4, tincubation = 5, tinfectious = 9, with line indicating infectiousness (rt) through time and shaded area demonstrating infectiousness slice removed if tiso = 7, leading to . B) as a function of tiso with same parameters as in A and point indicating scenario depicted in A. C) Boxplots showing distributions of as a function of testing frequency, f, and delay in obtaining test results, d, incorporating uncertainty in tlatent, tincubation, and tinfectious by drawing n = 100 parameter sets for each, with baseline . Boxplots indicate median, interquartile range, and full range of values of . D) Probability isolation occurs as a function of testing frequency, f, delay in obtaining test results, d, and days from exposure to isolation τ, i.e. tiso≤τ, demonstrating that delays in obtaining test results substantially reduce the probability of prompt isolation, particularly among most frequent testing scenarios.</p

Number of expected infections generated in a facility from model simulations comparing random and systematic testing strategies across transmission scenarios, test frequencies, and delays isolating infectious individuals who have tested positive.

Systematic testing strategies (■, ➕) prevent more infections than random strategies (●, ▲) across all transmission scenarios (indicated by community prevalence across the x axis and by reproduction number across the panels) and test frequencies (indicated by different colored symbols with blue corresponding to the highest test frequency of 4 tests per week and red the lowest test frequency of biweekly testing). More infections are expected in transmission scenarios with higher within-facility and higher community prevalence. Preventing delays between testing and isolation of positives (squares compared to crosses and triangles compared to circles) and increasing test frequency (red = lowest frequency, blue = highest frequency) also reduces the number of infections. The horizontal gray line serves as a reference to assess the testing frequency needed to maintain (corresponding to one infection every ten days) across different transmission scenarios. Error bars represent the interquartile range of derived from 100 simulations per scenario run for 180 days among 700 staff.</p

Distributions and parameter values used in analytic framework and model simulations.

The latent period is defined as the time between exposure and onset of infectiousness, the incubation period as the time between exposure and both symptoms and peak infectiousness (even in the absence of symptoms), and the infectious period as the total time a case is infectious.</p