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Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®

Multimodal neuroimaging assessments were utilized to identify generalizable brain correlates of current body mass index (BMI) and predictors of pathological weight gain (i.e., beyond normative development) one year later. Multimodal data from children enrolled in the Adolescent Brain Cognitive Devel...

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Autores principales: Adise, Shana, Allgaier, Nicholas, Laurent, Jennifer, Hahn, Sage, Chaarani, Bader, Owens, Max, Yuan, DeKang, Nyugen, Philip, Mackey, Scott, Potter, Alexandra, Garavan, Hugh P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066422/
https://www.ncbi.nlm.nih.gov/pubmed/33862325
http://dx.doi.org/10.1016/j.dcn.2021.100948
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author Adise, Shana
Allgaier, Nicholas
Laurent, Jennifer
Hahn, Sage
Chaarani, Bader
Owens, Max
Yuan, DeKang
Nyugen, Philip
Mackey, Scott
Potter, Alexandra
Garavan, Hugh P.
author_facet Adise, Shana
Allgaier, Nicholas
Laurent, Jennifer
Hahn, Sage
Chaarani, Bader
Owens, Max
Yuan, DeKang
Nyugen, Philip
Mackey, Scott
Potter, Alexandra
Garavan, Hugh P.
author_sort Adise, Shana
collection PubMed
description Multimodal neuroimaging assessments were utilized to identify generalizable brain correlates of current body mass index (BMI) and predictors of pathological weight gain (i.e., beyond normative development) one year later. Multimodal data from children enrolled in the Adolescent Brain Cognitive Development Study® at 9-to-10-years-old, consisted of structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), resting state (rs), and three task-based functional (f) MRI scans assessing reward processing, inhibitory control, and working memory. Cross-validated elastic-net regression revealed widespread structural associations with BMI (e.g., cortical thickness, surface area, subcortical volume, and DTI), which explained 35% of the variance in the training set and generalized well to the test set (R(2) = 0.27). Widespread rsfMRI inter- and intra-network correlations were related to BMI (R(2)(train) = 0.21; R(2)(test) = 0.14), as were regional activations on the working memory task (R(2)(train) = 0.20; (R(2)(test) = 0.16). However, reward and inhibitory control tasks were unrelated to BMI. Further, pathological weight gain was predicted by structural features (Area Under the Curve (AUC)(train) = 0.83; AUC(test) = 0.83, p < 0.001), but not by fMRI nor rsfMRI. These results establish generalizable brain correlates of current weight and future pathological weight gain. These results also suggest that sMRI may have particular value for identifying children at risk for pathological weight gain.
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spelling pubmed-80664222021-04-27 Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ® Adise, Shana Allgaier, Nicholas Laurent, Jennifer Hahn, Sage Chaarani, Bader Owens, Max Yuan, DeKang Nyugen, Philip Mackey, Scott Potter, Alexandra Garavan, Hugh P. Dev Cogn Neurosci Original Research Multimodal neuroimaging assessments were utilized to identify generalizable brain correlates of current body mass index (BMI) and predictors of pathological weight gain (i.e., beyond normative development) one year later. Multimodal data from children enrolled in the Adolescent Brain Cognitive Development Study® at 9-to-10-years-old, consisted of structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), resting state (rs), and three task-based functional (f) MRI scans assessing reward processing, inhibitory control, and working memory. Cross-validated elastic-net regression revealed widespread structural associations with BMI (e.g., cortical thickness, surface area, subcortical volume, and DTI), which explained 35% of the variance in the training set and generalized well to the test set (R(2) = 0.27). Widespread rsfMRI inter- and intra-network correlations were related to BMI (R(2)(train) = 0.21; R(2)(test) = 0.14), as were regional activations on the working memory task (R(2)(train) = 0.20; (R(2)(test) = 0.16). However, reward and inhibitory control tasks were unrelated to BMI. Further, pathological weight gain was predicted by structural features (Area Under the Curve (AUC)(train) = 0.83; AUC(test) = 0.83, p < 0.001), but not by fMRI nor rsfMRI. These results establish generalizable brain correlates of current weight and future pathological weight gain. These results also suggest that sMRI may have particular value for identifying children at risk for pathological weight gain. Elsevier 2021-03-30 /pmc/articles/PMC8066422/ /pubmed/33862325 http://dx.doi.org/10.1016/j.dcn.2021.100948 Text en © 2021 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research
Adise, Shana
Allgaier, Nicholas
Laurent, Jennifer
Hahn, Sage
Chaarani, Bader
Owens, Max
Yuan, DeKang
Nyugen, Philip
Mackey, Scott
Potter, Alexandra
Garavan, Hugh P.
Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®
title Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®
title_full Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®
title_fullStr Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®
title_full_unstemmed Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®
title_short Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®
title_sort multimodal brain predictors of current weight and weight gain in children enrolled in the abcd study ®
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066422/
https://www.ncbi.nlm.nih.gov/pubmed/33862325
http://dx.doi.org/10.1016/j.dcn.2021.100948
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