Mother-Child Closeness and Adolescent Structural Neural Networks: A Prospective Longitudinal Study of Low-Income Families

dc.contributor.authorHong, Sunghyun H.
dc.contributor.authorHardi, Felicia A.
dc.contributor.authorTillem, Scott
dc.contributor.authorGoetschius, Leigh Gayle
dc.contributor.authorBrooks-Gunn, Jeanne
dc.contributor.authorMcLoyd, Vonnie
dc.contributor.authorLopez-Duran, Nestor L.
dc.contributor.authorMitchell, Colter
dc.contributor.authorHyde, Luke W.
dc.contributor.authorMonk, Christopher S.
dc.date.accessioned2024-12-11T17:02:29Z
dc.date.available2024-12-11T17:02:29Z
dc.date.issued2024-11-08
dc.description.abstractMother-child closeness, a mutually trusting and affectionate bond, is an important factor in shaping positive youth development. However, little is known about the neural pathways through which mother-child closeness are related to brain organization. Utilizing a longitudinal sample primarily from low-income families (N=181; 76% African American youth and 54% female), this study investigated the associations between mother-child closeness at ages 9 and 15 and structural connectivity organization (network integration, robustness, and segregation) at age 15. The assessment of mother-child closeness included perspectives from both mother and child. The results revealed that greater mother-child closeness is linked with increased global efficiency and transitivity, but not modularity. Specifically, both the mother’s and child’s report of closeness at age 15 predicted network metrics but report at age 9 did not. Our findings suggest that mother-child closeness is associated with neural white matter organization, as adolescents who experienced greater mother-child closeness displayed topological properties indicative of more integrated and robust structural networks.
dc.description.sponsorshipThis study is supported by National Institute of Mental Health (R01MH103761 (PI: Monk); R01 MH121079 (PIs: Hyde, Mitchell, Monk)), Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32 HD007109 (PIs: Gelman, Monk)), and National Institute of Health Office of the Director (1S10OD012240 (PI: Noll))
dc.description.urihttps://academic.oup.com/scan/advance-article/doi/10.1093/scan/nsae083/7886859
dc.format.extent30 pages
dc.genrejournal articles
dc.genrepostprints
dc.identifierdoi:10.13016/m25lzy-8kof
dc.identifier.citationHong, Sunghyun H, Felicia A Hardi, Scott Tillem, Leigh G Goetschius, Jeanne Brooks-Gunn, Vonnie McLoyd, Nestor L Lopez-Duran, Colter Mitchell, Luke W Hyde, and Christopher S Monk. “Mother-Child Closeness and Adolescent Structural Neural Networks: A Prospective Longitudinal Study of Low-Income Families.” Social Cognitive and Affective Neuroscience, November 8, 2024, nsae083. https://doi.org/10.1093/scan/nsae083.
dc.identifier.urihttps://doi.org/10.1093/scan/nsae083
dc.identifier.urihttp://hdl.handle.net/11603/37073
dc.language.isoen_US
dc.publisherOxford University Press
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofA. All Hilltop Institute (UMBC) Works
dc.relation.ispartofUMBC Staff Collection
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International CC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleMother-Child Closeness and Adolescent Structural Neural Networks: A Prospective Longitudinal Study of Low-Income Families
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-6814-5634

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