Executive Dysfunction and Depression Risk in Adolescence: fMRI Analysis of Transient Network States During a Working Memory Task
Year: 2024
Type: dissertation
Study design: Observational Study
Abstract: Executive Function (EF) deficits have been linked to risk for depression, but the potential neural correlates of this association remain unclear. Altered functioning of brain regions associated with the frontoparietal network (FPN) could mediate risk. Specifically, the temporal properties of transient network states overlapping with the canonical FPN may statistically mediate associations between general EF ability and future depressive symptoms. This study aimed to identify neural correlates of EF-related depression risk in an adolescent sample risk-enriched for familial mood disorders (n=154, ages 13-19), capturing a sensitive period in which ongoing EF development intersects with heightened risk for depression onset. We applied co-activation pattern analysis to identify a set of transient network states that emerged during a working memory updating task. Additionally, we used dynamic structural equation modeling (DSEM) to model depressive symptom trajectories for two years after baseline to capture severity, stability, and variance in depression scores over time for each person. General EF ability did not predict symptom trajectory features, nor did it relate to network dynamics of an FPN-like state. However, exploratory analyses revealed that worse accuracy on low working memory load conditions predicted greater future depression variability. This was partially mediated by longer persistence of the FPN-like state during these conditions. Greater time spent in a state resembling the default mode network (DMN) during high working memory load conditions also predicted greater depression variability. These results provide novel evidence that the temporal dynamics of FPN and DMN regions relate to ongoing task performance and can predict future depressive symptom trajectories.
Author: Peterson, Elena Chu
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7047032784