Impact of Telemedicine on Utilization of Psychiatric Resources in New York City during the COVID-19 Pandemic.

This study sought to evaluate the impact of telepsychiatry during the COVID-19 pandemic among patients discharged from psychiatric inpatient units in the New York City Health and Hospitals Corporation system. We compared patients discharged to telepsychiatry (April 2020, n = 739) and in-person follow-up (May 2019, n = 527); we collected number, timing and attendance for follow-up appointments and number and timing of emergency room (ER) visits and readmissions. We used logistic regression to evaluate the odds of having these encounters and Kaplan-Meier analyses to compare time to these encounters. Patients discharged in 2020 were more likely to have a follow-up (29.4 vs. 19.9%, p < 0.001) and an ER visit or readmission (40.5 vs. 28.7%, p < 0.001). Kaplan-Meier analyses showed shorter time to first follow-up (chi-square = 14.69, d.f.=1, p < 0.0001, follow-ups = 322) and ER visit or readmission (chi-square = 19.57, d.f.=1, p < 0.0001, ER visits or admissions = 450) in the 2020 cohort. In multivariable analyses, patients discharged in 2020 were more likely to have a follow-up visit (adjusted OR 1.85, 95% confidence interval 1.40, 2.45, p < 0.0001). We found an increase in psychiatric service utilization during the pandemic, with an increase in and shorter time until outpatient visits and ER visits or readmissions. Although increased use of psychiatric services during the height of the COVID-19 pandemic is encouraging, it also points to the depth of the crisis among vulnerable populations; this pattern warrants further exploration and intervention.

Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan.

Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation.SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan.

Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome.

Primarily based on laboratory studies, theories of affect propose that emotions are driven by the valence of outcomes as well as the difference between the outcome itself and the expected outcome (i.e., the prediction error [PE]). Yet no work has assessed the drivers of emotion using real-world, personally meaningful events on timescales over which human emotion unfolds. We developed an event-triggered, ecological momentary assessment procedure measuring positive and negative affect (PA and NA, respectively) in university students as they received exam grades for which they had made predictions. We split data into exploratory and confirmatory samples, and built computational models predicting the time course of PA and NA and demonstrate that a model incorporating both exam grade and grade PE accounted for the time course of PA and NA better than a model solely using exam grades. Further, grade PEs were stronger predictors of the time course of PA and NA than the grades themselves. Similarly, the effects of PEs also persisted longer for NA than PA. These data indicate that deviations from expectations are critical determinants of the temporal dynamics of real-world emotion. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Association between real-world experiential diversity and positive affect relates to hippocampal-striatal functional connectivity.

Experiential diversity promotes well-being in animal models. Here, using geolocation tracking, experience sampling and neuroimaging, we found that daily variability in physical location was associated with increased positive affect in humans. This effect was stronger for individuals who exhibited greater functional coupling of the hippocampus and striatum. These results link diversity in real-world daily experiences to fluctuations in positive affect and identify a hippocampal-striatal circuit associated with this bidirectional relationship.

Model-based learning and individual differences in depression: The moderating role of stress.

Inflexible decision-making has been proposed as a transdiagnostic risk factor for mood disorders. Evidence suggests that inflexible decision-making may emerge only when individuals are experiencing increased negative affect or stress. 151 participants completed symptom measures of depression and anxiety, followed by a two-stage decision-making task that distinguishes between habitual and goal-directed choice. An experimental manipulation to induce stress was introduced halfway through the task. Individuals with higher depression levels became less model-based after the manipulation than those with lower depression levels. There was no relationship between trait anxiety and the impact of the manipulation on decision-making. Controlling for main effects of anxiety did not attenuate the association between depression and impact of stress. Anhedonia was associated with the impact of the manipulation on model-based decision-making. These results suggest that risk for depression is associated with reflexive decision-making, but these effects may only emerge under conditions of stress.