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The ability to monitor performance during a goal-directed behavior differs among children and adults in ways that can be measured with several tasks and techniques. As well, recent work has shown that individual differences in error monitoring moderate temperamental risk for anxiety and that this moderation changes with age. We investigated age differences in neural responses linked to performance monitoring using a multimodal approach. The approach combined functional MRI and source localization of event-related potentials (ERPs) in 12-year-old, 15-year-old, and adult participants. Neural generators of two components related to performance and error monitoring, the N2 and ERN, lay within specific areas of fMRI clusters. Whereas correlates of the N2 component appeared similar across age groups, age-related differences manifested in the location of the generators of the ERN component. The dorsal anterior cingulate cortex (dACC) was the predominant source location for the 12-year-old group; this area manifested posteriorly for the 15-year-old and adult groups. A fMRI-based ROI analysis confirmed this pattern of activity. These results suggest that changes in the underlying neural mechanisms are related to developmental changes in performance monitoring., (© 2023 The Authors. Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research.)

Age-related structural and functional changes that occur during brain development are critical for cortical development and functioning. Previous electroencephalography (EEG) and magnetoencephalography (MEG) studies have highlighted the utility of power spectra analyses and have uncovered age-related trends that reflect perceptual, cognitive, and behavioural states as well as their underlying neurophysiology. The aim of the current study was to investigate age-related change in aperiodic and periodic alpha activity across a large sample of pre- and school-aged children (N = 502, age range 4 -11-years-of-age). Power spectra were extracted from baseline EEG recordings (eyes closed, eyes open) for each participant and parameterized into aperiodic activity to derive the offset and exponent parameters and periodic alpha oscillatory activity to derive the alpha peak frequency and the associated power estimates. Multilevel models were run to investigate age-related trends and condition-dependent changes for each of these measures. We found quadratic age-related effects for both the aperiodic offset and exponent. In addition, we observed increases in periodic alpha peak frequency as a function of age. Aperiodic measures and periodic alpha power were larger in magnitude during eyes closed compared to the eyes open baseline condition. Taken together, these results advance our understanding of the maturational patterns/trajectories of brain development during early- to middle-childhood., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest, (Copyright © 2023. Published by Elsevier Inc.)

Behavioral Inhibition is a temperament identified in the first years of life that enhances the risk for development of anxiety during late childhood and adolescence. Amongst children characterized with this temperament, only around 40 percent go on to develop anxiety disorders, meaning that more than half of these children do not. Over the past 20 years, research has documented within‐child and socio‐contextual factors that support differing developmental pathways. This review provides a historical perspective on the research documenting the origins of this temperament, its biological correlates, and the factors that enhance or mitigate risk for development of anxiety. We review as well, research findings from two longitudinal cohorts that have identified moderators of behavioral inhibition in understanding pathways to anxiety. Research on these moderators has led us to develop the Detection and Dual Control (DDC) framework to understand differing developmental trajectories among behaviorally inhibited children. In this review, we use this framework to explain why and how specific cognitive and socio‐contextual factors influence differential pathways to anxiety versus resilience. Read the Commentary on this article at doi: 10.1111/jcpp.13771. [ABSTRACT FROM AUTHOR]