Neighborhood deprivation, prefrontal morphology and neurocognition in late childhood to early adolescence

Abstract

Background: Neighborhood deprivation adversely effects neurodevelopment and cognitive function; however, mechanisms remain unexplored. Neighborhood deprivation could be particularly impactful in late childhood/early adolescence, in neural regions with protracted developmental trajectories, e.g., prefrontal cortex (PFC). Methods: The Adolescent Brain Cognitive Development (ABCD) study recruited 10,205 youth. Geocoded residential history was used to extract individual neighborhood characteristics. A general cognitive ability index and MRI scans were completed. Associations with neurocognition were examined. The relation of PFC surface area and cortical thickness to neighborhood deprivation was tested. PFC subregions and asymmetry, with putative differential environmental susceptibility during key developmental periods, were explored. Analyses tested PFC area as a possible mediating mechanism. Results: Neighborhood deprivation predicted neurocognitive performance ( β = −0.11), even after accounting for parental education and household income ( β = −0.07). Higher neighborhood deprivation related to greater overall PFC surface area ( η 2 p = 0.003), and differences in leftward asymmetry were observed for area ( η 2 p = 0.001), and thickness ( η 2 p = 0.003). Subregion analyses highlighted differences among critical areas that are actively developing in late childhood/early adolescence and are essential to modulating high order cognitive function. These included orbitofrontal, superior frontal, rostral middle frontal, and frontal pole regions (Cohen’s d = 0.03–0.09). PFC surface area partially mediated the relation between neighborhood deprivation and neurocognition. Discussion: Neighborhood deprivation related to cognitive function (a foundational skill tied to a range of lifetime outcomes) and PFC morphology, with evidence found for partial mediation of PFC on neurocognitive function. Results inform public health conceptualizations of development and environmental vulnerability.

Ecological stress, amygdala reactivity, and internalizing symptoms in preadolescence: Is parenting a buffer?

“…They also found that Ecological stress, amygdala reactivity, and internalizing problems higher neighborhood poverty was negatively associated with scores across all seven cognitive domains. Likewise, Vargas et al, (2020) reported that neighborhood deprivation, as measured by ADI quantiles (1-5; five being the highest deprivation), was negatively related to cognitive function and that this association was partially mediated by PFC surface area. Although all of these analyses considered cross-sectional associations, it will be important to consider how SES and brain associations change across time --a strength of the ABCD design.…”

“…Although all of these analyses considered cross-sectional associations, it will be important to consider how SES and brain associations change across time --a strength of the ABCD design. Furthermore, although the brain was identified as mediator in cross-sectional analyses of the ABCD sample (Assari, 2020;Assari et al, 2020;Vargas et al, 2020), it will be important to reexamine the mechanistic role of specific brain regions using the longitudinal ABCD data to distinguish between cross-sectional and longitudinal effects. Finally, to describe how SES manifests in the brain (Farah, 2017) and relates to cognitive and behavioral outcomes, future work should examine differences in the analytic pipeline that contribute to convergence and differences in findings.…”

Subjective Socioeconomic Status and Children’s Amygdala Volume: Minorities’ Diminish Returns

“…As described in detail by Casey et al (2018), participants completed a high-resolution T1-weighted structural MRI scan (1-mm isotropic voxels) using scanners from Philips Healthcare (Philips, Andover, Massachusetts, USA), GE Healthcare (General Electrics, Waukesha, WI, USA), or Siemens Healthcare (Siemens, Erlangen, Germany) [ 72 ]. All the structural MRI data were processed using FreeSurfer version 5.3.0, available at http://surfer.nmr.mgh.harvard.edu/ [ 80 , 81 ], according to standard processing pipelines [ 72 ]. Processing included removal of nonbrain tissue, segmentation of gray and white matter structures [ 82 ], and cortical parcellation [ 83 ].…”

Racial Variation in the Association between Childhood Depression and Frontal Pole Volume among American Children

“…All the structural MRI data were processed using FreeSurfer version 5.3.0 (B. Fischl, Sereno, & Dale, 1999;Vargas, Damme, & Mittal, 2020), in line with the standard processing pipelines (Casey et al, 2018). The process included the removal of nonbrain tissue, the segmentation of gray and white matter (Bruce Fischl et al, 2002) and the parcellation of the cerebral cortex (Bruce Fischl et al, 2004).…”