Multimodal brain predictors of current weight and weight gain in children enrolled in the ABCD study ®. (June 2021) - Adise, Shana; Allgaier, Nicholas; Laurent, Jennifer; Hahn, Sage; Chaarani, Bader; Owens, Max; Yuan, DeKang; Nyugen, Philip; Mackey, Scott; Potter, Alexandra; Garavan, Hugh P.
Highlights: BMI was associated with widespread structural differences in cortical thickness, surface area, subcortical gray matter volumes and in white matter estimates of fractional anisotropy and mean diffusivity. BMI was also associated with altered resting-state functional connectivity and working memory during an EN-back task but, contrary to some extant findings, was not related to reward or inhibitory control (as assessed by the Monetary Incentive Delay task and Stop Signal Task). Excessive weight gain (i.e., more than 20 pounds in a year) was associated at baseline with thicker cortices, and differences in surface area and white matter in regions associated with attention and appetite control (e.g., insula, parahippocampal gyrus), but no functional associations were observed. All analyses quantified generalizability to an unseen test set. These findings suggest that brain structure, resting state and working memory are associated with current weight and that brain structure may have potential as an MRI biomarker to identify children at risk for pathological weight gain. Abstract: 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; AUCtest = 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.