[article] 
inJournal of Child Psychology and Psychiatry > 64-6 (June 2023) . - p.918-929

Titre :	Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Felicia A. HARDI, Auteur ; Leigh G. GOETSCHIUS, Auteur ; Vonnie MCLOYD, Auteur ; Nestor L. LOPEZ-DURAN, Auteur ; Colter MITCHELL, Auteur ; Luke W. HYDE, Auteur ; Adriene M. BELTZ, Auteur ; Christopher S. MONK, Auteur
Article en page(s) :	p.918-929
Langues :	Anglais (eng)
Index. décimale :	PER Périodiques
Résumé :	Background Stressful events, such as the COVID-19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population-based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences. Methods Data-driven network-based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self-reported at ages 15, 17, and 21 (during COVID-19). During COVID-19, participants reported on pandemic-related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup-adversity interaction were tested to predict change in symptoms over time. Results Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (??= .138, p = .042), and this result remained after adjusting for additional covariates (??= .194, p = .023). Furthermore, there was a subgroup-adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (??= .307, p = .006), and this remained after accounting for initial symptoms and many covariates (??= .237, p = .021). Conclusions A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.
En ligne :	http://dx.doi.org/https://doi.org/10.1111/jcpp.13749
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=504

[article] Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity [Texte imprimé et/ou numérique] / Felicia A. HARDI, Auteur ; Leigh G. GOETSCHIUS, Auteur ; Vonnie MCLOYD, Auteur ; Nestor L. LOPEZ-DURAN, Auteur ; Colter MITCHELL, Auteur ; Luke W. HYDE, Auteur ; Adriene M. BELTZ, Auteur ; Christopher S. MONK, Auteur . - p.918-929.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 64-6 (June 2023) . - p.918-929

Index. décimale :	PER Périodiques
Résumé :	Background Stressful events, such as the COVID-19 pandemic, are major contributors to anxiety and depression, but only a subset of individuals develop psychopathology. In a population-based sample (N = 174) with a high representation of marginalized individuals, this study examined adolescent functional network connectivity as a marker of susceptibility to anxiety and depression in the context of adverse experiences. Methods Data-driven network-based subgroups were identified using an unsupervised community detection algorithm within functional neural connectivity. Neuroimaging data collected during emotion processing (age 15) were extracted from a priori regions of interest linked to anxiety and depression. Symptoms were self-reported at ages 15, 17, and 21 (during COVID-19). During COVID-19, participants reported on pandemic-related economic adversity. Differences across subgroup networks were first examined, then subgroup membership and subgroup-adversity interaction were tested to predict change in symptoms over time. Results Two subgroups were identified: Subgroup A, characterized by relatively greater neural network variation (i.e., heterogeneity) and density with more connections involving the amygdala, subgenual cingulate, and ventral striatum; and the more homogenous Subgroup B, with more connections involving the insula and dorsal anterior cingulate. Accounting for initial symptoms, subgroup A individuals had greater increases in symptoms across time (??= .138, p = .042), and this result remained after adjusting for additional covariates (??= .194, p = .023). Furthermore, there was a subgroup-adversity interaction: compared with Subgroup B, Subgroup A reported greater anxiety during the pandemic in response to reported economic adversity (??= .307, p = .006), and this remained after accounting for initial symptoms and many covariates (??= .237, p = .021). Conclusions A subgrouping algorithm identified young adults who were susceptible to adversity using their personalized functional network profiles derived from a priori brain regions. These results highlight potential prospective neural signatures involving heterogeneous emotion networks that predict individuals at the greatest risk for anxiety when experiencing adverse events.
En ligne :	http://dx.doi.org/https://doi.org/10.1111/jcpp.13749
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=504