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Adolescence is a sensitive period when brain regions involved in cognitive control exhibit a slower, more protracted development compared to the socio-emotional system which develops at a faster rate (Steinberg, 2008). Regions included in the frontoparietal network such as the insula, dorsal anterior cingulate cortex (dACC), and middle frontal gyrus (MFG), have been implicated in cognitive control processing (Sebastian, 2013). It is established that socioeconomic status (SES) may impact neurocognitive development (Farah, 2017); however, recent theoretical models suggest that environmental harshness and unpredictability uniquely impact neurobiological mechanisms that might promote faster life history strategy (Belsky et al., 2012; Ellis et al., 2022). This study aimed to examine if environmental harshness (mean socioeconomic status) and environmental unpredictability (variability in socioeconomic status) across four years of adolescence predict later functional connectivity within the frontoparietal network during cognitive control. Identifying neural mechanisms associated with socioeconomic risk can facilitate early identification of vulnerability for later psychosocial outcomes associated with cognitive functioning. Further evidence is needed to elucidate how changes or stability in SES may contribute to the development of cognitive control processing during this time.
Participants included 167 adolescents and young adults (53% male; Mage = 14 years at Time 1) and were assessed across five time points, with approximately one year between each assessment. Parents reported annual income at Times 1-4, and an income-to-needs ratio was calculated by dividing family income by federal poverty threshold for a family of that size. A grand mean composite and an intraindividual variation score was calculated using income-to-needs ratio during those four years. Neural cognitive control was assessed by blood-oxygen level dependent response (frontoparietal activation) during the Multi-Source Interference Task (MSIT; Bush et al., 2003) at Times 1-5. SPM 12 was used to process and analyze the fMRI data at Time 5. Functional connectivity analyses were conducted using the gPPI toolbox, and both seed regions and ROIs were create using the MarsBar region of interest toolbox (Brett et al., 2002). All further analyses were conducted at a threshold of p <.001 and corrected using family-wise-error cluster-level correction (p < .05).
SES mean was significantly associated with functional connectivity, such that higher socioeconomic status across four years of adolescence was associated with lower connectivity between the insula and MFG at Time 5 (r = -.451; p < .0001). In contrast, SES variability across adolescence did not predict functional connectivity within the fronto-parietal network (insula, dACC, MFG). Prior studies show less activation in the frontoparietal network during cognitive control to be associated with better behavioral cognitive control (Kim-Spoon et al., 2021) and that these decreases reflect more efficient neural processing (Luna at al., 2010). These findings suggest experiences of environmental harshness, indexed by mean socioeconomic status, but not socioeconomic variability, may predict neural connectivity that reflect less efficient cognitive processing. Future studies should investigate other proximal factors associated with environmental unpredictability such as more frequent changes in residence, cohabitation, and parental occupation. Intervention efforts may consider protective mechanisms for strengthening cognitive control despite facing chronic socioeconomic adversity.
Morgan Lindenmuth, Virginia Polytechnic Institute and State University
Presenting Author
Ya-Yun Chen, Virginia Polytechnic Institute and State University
Tae-Ho Lee, Virginia Polytechnic Institute and State University
Alexis Brieant, Yale University
Jacob Lee, Virginia Tech Carilion School of Medicine and Research Institute
Megan Egan, Virginia Tech
Brooks King-Casas, Fralin Biomedical Research Institute at VTC, and Virginia Tech
Jungmeen Kim-Spoon, Virginia Tech