No abstract

Inhibitory control, the capacity to suppress an inappropriate response, is a process employed for guiding action selection in the service of goal-directed behavior. Under neutral circumstances, inhibitory control success improves from childhood to adulthood and has been associated with developmental shifts in functional activation and connectivity of the pFC. However, the ability to exercise inhibitory control is challenged in certain contexts by including appetitive cues, a phenomenon that may be particularly pronounced in youths. Here, we examine the magnitude and temporal persistence of learned value's influence on inhibitory control in a cross-sectional sample of 8- to 25-year-olds. Participants first underwent conditioning of a motor approach response to two initially neutral cues, with one cue reinforced with monetary reward and the other with no monetary outcome. Subsequently, during fMRI, participants reencountered these cues as no-go targets in a nonreinforced go/„no-go paradigm. Although the influence of learned value increasingly disrupted inhibitory control with increasing age, in young adults this pattern remitted over the course of the task, whereas during adolescence the impairing effect of reward history persisted. Successful no-go performance to the previously rewarded target was related to greater recruitment of the right inferior frontal gyrus and age-related increase in functional connectivity between the inferior frontal gyrus and the ventromedial pFC for the previously rewarded no-go target over the control target. Together, results indicate the complex influence of value on goals over development relies upon the increased coordination of distinct higher-order regions in the pFC.

Background: Adolescence is characterized by substantial changes in sleep behavior, heightened exposure to stressful life events (SLEs), and elevated risk for internalizing problems like anxiety and depression. Although SLEs are consistently associated with the onset of internalizing psychopathology, the mechanisms underlying this relationship remain poorly understood, especially at the within-person level. Here, we leverage a high-frequency longitudinal design to examine sleep as a potential mechanism linking SLEs to increases in anxiety and depression symptoms over a one-year period. Methods: Thirty female adolescents aged 15-17 years completed 12 monthly inlaboratory assessments of exposure to SLEs and symptoms of anxiety and depression (n = 355 monthly assessments), and wore an actigraphy wristband for continuous monitoring of sleep for the duration of the study (n = 6,824 sleep days). Multilevel models examined concurrent and lagged within-person associations between SLEs, sleep duration and timing regularity, and anxiety and depression symptoms. Results: Within-person fluctuations in SLEs were associated with variability in sleep duration both concurrently and prospectively, such that when adolescents experienced greater SLEs than was typical for them, they exhibited more variable sleep duration that same month as well as the following month. In turn, within-person increases in sleep duration variability predicted greater anxiety symptoms in the same month and mediated the association between SLEs and anxiety. Conclusions: These findings highlight sleep disruptions as a mechanism underlying the longitudinal associations between SLEs and anxiety symptoms, and suggest that interventions promoting sleep schedule consistency may help mitigate risk for stress-related psychopathology in adolescence.

Stressful life events (SLEs) are strongly associated with the emergence of adolescent anxiety and depression, but the underlying mechanisms remain poorly understood, especially at the within-persons level. We investigated how adolescent social communication (i.e., frequency of calls and texts) following SLEs relates to changes in internalizing symptoms in a multitimescale, intensive, year-long study ( N = 30; n = 355 monthly observations; n ≈ 5,000 experience-sampling observations). Within-persons increases in SLEs were associated with receiving more calls than usual at both the month and moment levels and making more calls at the month level. Increased calls were prospectively associated with worsening internalizing symptoms at the month level only, suggesting that SLEs rapidly influence phone communication patterns, but these communication changes may have a more protracted, cumulative influence on internalizing symptoms. Finally, increased incoming calls prospectively mediated the association between SLEs and anxiety at the month level. We identify adolescent social communication fluctuations as a potential mechanism conferring risk for stress-related internalizing psychopathology.

Background Wearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep. Objective This study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest. Methods The pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward–sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each. Results Actigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data. Conclusions We discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.