2021
DOI: 10.1001/jamapediatrics.2021.2511
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Associations of Early-Life Threat and Deprivation With Executive Functioning in Childhood and Adolescence

Abstract: IMPORTANCEMany studies have demonstrated an association between early-life adversity (ELA) and executive functioning in children and adolescents. However, the aggregate magnitude of this association is unknown in the context of threat and deprivation types of adversity and various executive functioning domains.OBJECTIVE To test the hypothesis that experiences of deprivation are more strongly associated with reduced executive functioning compared with experiences of threat during childhood and adolescence.DATA … Show more

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Cited by 88 publications
(73 citation statements)
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References 164 publications
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“…These child adversity dimensions may yield distinct transdiagnostic markers of psychopathology, proximal to their theorized neurocognitive mechanism. For instance, early exposure to deprivation has been found to contribute specifically to the proximal outcome, poor cognitive functioning (Bos et al, 2009;Eigsti et al, 2011;Pollak et al, 2010;Sheridan et al, 2017;Tibu et al, 2016), when measured by results from neuropsychological testing, questionnaires of executive functioning, and neural functioning during cognitive control tasks (Johnson et al, 2021). Early exposure to threat has been found to contribute specifically to the proximal outcome, emotion regulation, when measured by attention to emotional faces and fMRI results from mood induction paradigms (Gold et al, 2016;McCrory et al, 2013;McLaughlin et al, 2015;Pollak & Tolley-Schell, 2003).…”
Section: A Dimensional Model Of Neurodevelopmental Adversitymentioning
confidence: 99%
“…These child adversity dimensions may yield distinct transdiagnostic markers of psychopathology, proximal to their theorized neurocognitive mechanism. For instance, early exposure to deprivation has been found to contribute specifically to the proximal outcome, poor cognitive functioning (Bos et al, 2009;Eigsti et al, 2011;Pollak et al, 2010;Sheridan et al, 2017;Tibu et al, 2016), when measured by results from neuropsychological testing, questionnaires of executive functioning, and neural functioning during cognitive control tasks (Johnson et al, 2021). Early exposure to threat has been found to contribute specifically to the proximal outcome, emotion regulation, when measured by attention to emotional faces and fMRI results from mood induction paradigms (Gold et al, 2016;McCrory et al, 2013;McLaughlin et al, 2015;Pollak & Tolley-Schell, 2003).…”
Section: A Dimensional Model Of Neurodevelopmental Adversitymentioning
confidence: 99%
“…Stress, related to the COVID-19 spread and social distancing, was found to be associated with loneliness and depression [ 7 , 9 , 10 ]. Recent studies showed a high level of depression and anxiety in adolescents in different pandemic periods [ 9 , 11 15 ]. The COVID-19 diagnosis or close contact with an infected person, low social support, and negative coping has been found to relate to higher levels of depression and anxiety [ 14 , 16 ].…”
Section: Introductionmentioning
confidence: 99%
“…The spatial attention (SA) mechanism is a soft attention mechanism approach, which is designed to use a mask formation mechanism to compute a weight mask that identifies important features and allows the convolutional neural network to efficiently learn the region of interest, thus mapping the spatial information in the original image to another space to retain the important features in the image. The spatial domain attention mechanism is represented by the spatial transformation network (STN), which improves the performance of a convolutional neural network by spatially transforming the feature map tensor to extract key information and reduce the impact of minor features in the feature map tensor on the convolutional neural network [ 13 ]. Since the useful features contained in each channel in the feature map tensor are not the same, the use of the CA module can effectively weaken or suppress the adverse effects and interference of the features on the redundant channels in the feature map tensor on facial expression recognition.…”
Section: The Design Of An Attentional Genetic Neural Network Modelmentioning
confidence: 99%