White matter integrity disparities between normal-weight and overweight/obese adolescents: an automated fiber quantification tractography study

Carbine, Kaylie A.; Duraccio, Kara M.; Hedges-Muncy, Ariana; Barnett, Kimberly A.; Kirwan, C. Brock; Jensen, Chad D.

doi:10.1007/s11682-019-00036-4

Cited by 31 publications

(51 citation statements)

References 59 publications

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“…Yet, few studies have converged on a consistent set of findings. Obesity in children has been related to the cortical thinness of the frontal cortex ( Alosco et al, 2014 ; Ronan et al, 2019 ; Carbine et al, 2019 ) and/or greater subcortical volumes of specific appetitive regions like the hippocampus ( Mestre et al, 2017 ), pallidum ( de Groot et al, 2017 ), amygdala and/or accumbens ( Perlaki et al, 2018 ; Rapuano et al, 2017 ). In our study, BMI showed global (i.e., widespread) associations within multiple modalities (e.g., cortical thickness, surface area).…”

Section: Discussionmentioning

confidence: 99%

“…Structural (s) and functional (f) magnetic resonance imaging (MRI) studies have associated childhood obesity to differences in brain anatomy (e.g., cortical thickness ( Laurent et al, 2019 ), total gray matter volume ( Mestre et al, 2017 ; de Groot et al, 2017 ; Alosco et al, 2014 ; Ronan et al, 2019 ; Perlaki et al, 2018 ; Rapuano et al, 2017 ), white matter architecture ( Carbine et al, 2019 ; Geserick et al, 2018 )), and altered functioning between networks at rest ( Black et al, 2014 ; Moreno-Lopez et al, 2016 ) and, in response to rewarding stimuli in regions implicated in reward ( Yokum et al, 2014a ; Bohon, 2017 ; Batterink et al, 2010 ; Bruce et al, 2010 ) and inhibitory control ( Bruce et al, 2010 ; Davids et al, 2010 ; Bruce et al, 2013 ; Van et al, 2016 ). However, inconsistent findings are a hallmark of MRI studies investigating childhood obesity with some studies finding differences in broad cortical brain territories, like the frontal cortex ( Laurent et al, 2019 ; Alosco et al, 2014 ; Ronan et al, 2019 ; Carbine et al, 2019 ), or specific subcortical structures such as the hippocampus ( Mestre et al, 2017 ), pallidum ( de Groot et al, 2017 ), amygdala and accumbens ( Perlaki et al, 2018 ; Rapuano et al, 2017 ), while others report no relationships ( de Groot et al, 2017 ; Alosco et al, 2014 ; Sharkey et al, 2015 ). There have also been inconsistent fMRI findings.…”

Section: Introductionmentioning

confidence: 99%

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

Adise

Allgaier

Laurent

et al. 2021

Developmental Cognitive Neuroscience

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Adise

Allgaier

Laurent

et al. 2021

Developmental Cognitive Neuroscience

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“…FA reductions in several major WM fiber tracts have been linked to numerous cognitive processes including reward-related behavior, cognitive and inhibitory control, or memory and decision-making 8,[29][30][31][32][33][34] . On the other hand, higher BMI or waist circumference (WC) was associated with increased FA in several studies 28,35 . Moreover, reported WM tracts vary across studies, which makes it challenging to interpret the pattern of alterations.…”

Section: Dti and Obesitymentioning

confidence: 99%

“…Twenty-one out of twenty-eight retrieved studies [23][24][25][26][27][28]32,33,46,[49][50][51][52][53][54][55][56][57][58][59] used samples of adults. Five studies [60][61][62][63][64] were performed on adolescents between 12 and 19 years, and two studies 65,66 were performed on children between 7 and 11 years. Twenty-six studies [23][24][25][26][27][28]32,33,46,50,[52][53][54][56][57][58][60][61][62][63][64][65][66]…”

Section: Sample Types and Dti Approachmentioning

confidence: 99%

“…Five studies [60][61][62][63][64] were performed on adolescents between 12 and 19 years, and two studies 65,66 were performed on children between 7 and 11 years. Twenty-six studies [23][24][25][26][27][28]32,33,46,50,[52][53][54][56][57][58][60][61][62][63][64][65][66][67][68][69] used a sample composed of both men and women, while two studies 59,70 were performed on women only. All selected studies used a crosssectional approach.…”

Section: Sample Types and Dti Approachmentioning

confidence: 99%

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A systematic review of diffusion tensor imaging studies in obesity

Okudzhava

Heldmann

Münte

2021

Preprint

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Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018

et al. 2023

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ImportanceAside from widely known cardiovascular implications, higher weight in children may have negative associations with brain microstructure and neurodevelopment.ObjectiveTo evaluate the association of body mass index (BMI) and waist circumference with imaging metrics that approximate brain health.Design, Setting, and ParticipantsThis cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study to examine the association of BMI and waist circumference with multimodal neuroimaging metrics of brain health in cross-sectional and longitudinal analyses over 2 years. From 2016 to 2018, the multicenter ABCD study recruited more than 11 000 demographically representative children aged 9 to 10 years in the US. Children without any history of neurodevelopmental or psychiatric disorders were included in this study, and a subsample of children who completed 2-year follow-up (34%) was included for longitudinal analysis.ExposuresChildren’s weight, height, waist circumference, age, sex, race and ethnicity, socioeconomic status, handedness, puberty status, and magnetic resonance imaging scanner device were retrieved and included in the analysis.Main Outcomes and MeasuresAssociation of preadolescents’ BMI z scores and waist circumference with neuroimaging indicators of brain health: cortical morphometry, resting-state functional connectivity, and white matter microstructure and cytostructure.ResultsA total of 4576 children (2208 [48.3%] female) at a mean (SD) age of 10.0 years (7.6 months) were included in the baseline cross-sectional analysis. There were 609 (13.3%) Black, 925 (20.2%) Hispanic, and 2565 (56.1%) White participants. Of those, 1567 had complete 2-year clinical and imaging information at a mean (SD) age of 12.0 years (7.7 months). In cross-sectional analyses at both time points, higher BMI and waist circumference were associated with lower microstructural integrity and neurite density, most pronounced in the corpus callosum (fractional anisotropy for BMI and waist circumference at baseline and second year: P &lt; .001; neurite density for BMI at baseline: P &lt; .001; neurite density for waist circumference at baseline: P = .09; neurite density for BMI at second year: P = .002; neurite density for waist circumference at second year: P = .05), reduced functional connectivity in reward- and control-related networks (eg, within the salience network for BMI and waist circumference at baseline and second year: P &lt; .002), and thinner brain cortex (eg, for the right rostral middle frontal for BMI and waist circumference at baseline and second year: P &lt; .001). In longitudinal analysis, higher baseline BMI was most strongly associated with decelerated interval development of the prefrontal cortex (left rostral middle frontal: P = .003) and microstructure and cytostructure of the corpus callosum (fractional anisotropy: P = .01; neurite density: P = .02).Conclusions and RelevanceIn this cross-sectional study, higher BMI and waist circumference among children aged 9 to 10 years were associated with imaging metrics of poorer brain structure and connectivity as well as hindered interval development. Future follow-up data from the ABCD study can reveal long-term neurocognitive implications of excess childhood weight. Imaging metrics that had the strongest association with BMI and waist circumference in this population-level analysis may serve as target biomarkers of brain integrity in future treatment trials of childhood obesity.

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White matter integrity disparities between normal-weight and overweight/obese adolescents: an automated fiber quantification tractography study

Cited by 31 publications

References 59 publications

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

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

A systematic review of diffusion tensor imaging studies in obesity

Association of Body Mass Index and Waist Circumference With Imaging Metrics of Brain Integrity and Functional Connectivity in Children Aged 9 to 10 Years in the US, 2016-2018

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