Developmental Cognitive NeuroscienceNeurosciencePeer-reviewed journal article
Decomposing complex links between the childhood environment and brain structure in school-aged youth
Seok‐Jun HongiD, Lucinda M. SiskiD, Camila CaballeroiD, Anthony MekhanikiD, Amy Krain RoyiD, Michael Peter MilhamiD, Dylan G. GeeiD
Abstract
Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called Similarity Network Fusion, which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with increased myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with increased cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures differentially influence neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences.
- organism: human
- assay: bulk RNA-seq
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
- Citations
- 37
- FWCI
- 4.77
- Field percentile
- top 5%
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