Adolescent functional network connectivity prospectively predicts adult anxiety symptoms related to perceived COVID-19 economic adversity.
J Child Psychol Psychiatry
; 64(6): 918-929, 2023 06.
Article
in English
| MEDLINE | ID: covidwho-2315194
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 (ß = .138, p = .042), and this result remained after adjusting for additional covariates (ß = .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 (ß = .307, p = .006), and this remained after accounting for initial symptoms and many covariates (ß = .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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pandemics
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Qualitative research
Limits:
Adolescent
/
Adult
/
Humans
/
Young adult
Language:
English
Journal:
J Child Psychol Psychiatry
Year:
2023
Document Type:
Article
Affiliation country:
Jcpp.13749
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