Literature DB >> 33518264

Emerging Evidence for Putative Neural Networks and Antecedents of Pediatric Anxiety in the Fetal, Neonatal, and Infant Periods.

Colleen M Doyle1, Carolyn Lasch2, Jed T Elison3.   

Abstract

Anxiety disorders are the most prevalent psychiatric disorders in youth and are associated with profound individual impairment and public health costs. Research shows that clinically significant anxiety symptoms manifest in preschool-aged children, and correlates of anxiety symptoms are observable in infancy. Yet, predicting who is at risk for developing anxiety remains an enduring challenge. Predictive biomarkers of anxiety are needed before school age when anxiety symptoms typically consolidate into diagnostic profiles. Increasing evidence indicates that early neural measures implicated in anxiety and anxious temperament may be incorporated with traditional measures of behavioral risk (i.e., behavioral inhibition) to provide more robust classification of pediatric anxiety problems. This review examines the phenomenology of anxiety disorders in early life, highlighting developmental research that interrogates the putative neurocircuitry of pediatric anxiety. First, we discuss enduring challenges in identifying and predicting risk for pediatric anxiety. Second, we summarize emerging evidence for putative neural antecedents and networks underlying risk for pediatric anxiety in the fetal, neonatal, and infant periods that represent novel potential avenues for risk identification and prediction. We focus on evidence examining the importance of early amygdala and extended amygdala circuitry development to the emergence of anxiety. Finally, we discuss the utility of integrating developmental psychopathology and neuroscience to facilitate future research and clinical work.
Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Amygdala; Anxiety; Infancy; Prediction

Year:  2020        PMID: 33518264     DOI: 10.1016/j.biopsych.2020.11.020

Source DB:  PubMed          Journal:  Biol Psychiatry        ISSN: 0006-3223            Impact factor:   13.382


  1 in total

1.  A Genetic Neural Net Model for the Relationship between Pre-School and Attention in Early Childhood.

Authors:  Liping Wang; Na Yao
Journal:  Comput Intell Neurosci       Date:  2022-06-06
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.