Developmental Cognitive NeuroscienceNeurosciencePeer-reviewed journal article
Multivariate environmental exposures are reflected in whole-brain functional connectivity and cognition in youth
Sarah D. LichensteiniD, Yihe WengiD, Heather Robinson, Lester RodrígueziD, Marzieh Babaeianjelodar, Joliza Maynard, Menessa Metayer, Suhani Suneja, Amar OjhaiD, Corey Horien, Abigail S. GreeneiD, Robert Todd ConstableiD, Tyler M. MooreiD, Ran BarzilayiD, Sarah W. YipiD, Arielle S. KelleriD
Abstract
Each individuals complex, multidimensional environment, known as their "exposome", plays an essential role in shaping cognitive neurodevelopment. Understanding the mechanisms whereby childrens exposome influences their development is crucial to facilitate the design of interventions to foster positive developmental trajectories for all youth. Recent work has identified a general exposome factor associated with socio-economic inequality that is strongly related to cognition and individual differences in the spatial organization of functional brain networks in youth. Building on these findings, the current study explores whether alterations in functional connectivity may represent a potential mechanism linking variation in the exposome to cognitive performance. We apply a data-driven, cross-validated, whole-brain machine learning approach, connectome-based statistical inference, to identify patterns of functional connectivity associated with exposome scores among early adolescents enrolled in the Adolescent Brain Cognitive Development (ABCD) Study using data collected during three cognitive tasks and during rest. Additionally, we investigate whether the identified patterns of functional connectivity relate to individual differences in cognitive performance across three domains: General Cognition, Executive Functioning, and Learning/Memory. Models incorporating 10-fold cross-validation over 100 iterations identified consistent functional connections associated with the exposome across task and rest conditions (model performance: ns = 6,137 - 8,391, rs = 0.34 - 0.44, ps View larger version (33K): org.highwire.dtl.DTLVardef@b5cc3aorg.highwire.dtl.DTLVardef@172f954org.highwire.dtl.DTLVardef@14f0d01org.highwire.dtl.DTLVardef@deccb5_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: machine learning (classical)
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 1th percentile
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