Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity
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Date
2022-12-29
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Citation of Original Publication
Hardi, F.A., Goetschius, L.G., McLoyd, V., Lopez-Duran, N.L., Mitchell, C., Hyde, L.W., Beltz, A.M. and Monk, C.S. (2023), Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity. J Child Psychol Psychiatr. https://doi.org/10.1111/jcpp.13749
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This is the peer reviewed version of the following article: Hardi, F.A., Goetschius, L.G., McLoyd, V., Lopez-Duran, N.L., Mitchell, C., Hyde, L.W., Beltz, A.M. and Monk, C.S. (2023), Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity. J Child Psychol Psychiatr. https://doi.org/10.1111/jcpp.13749, which has been published in final form at https://doi.org/10.1111/jcpp.13749. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Access to this item will begin on 12-29-2023
Access to this item will begin on 12-29-2023
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Abstract
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 (β = .138, p = .042), and this result remained after adjusting for additional covariates (β = .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 (β = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (β = .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.