Brain response to food cues across adolescence
Creators: name: Sadler, Jennifer
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Psychiatry and Psychology
subject: Medicine and Health Sciences
subject: Social and Behavioral Sciences
subject: Psychology
subject: FOS: Psychology
subject: adolescence
subject: fMRI
subject: food cues

description: Title: Brain response to food cues across adolescence Team: Appetite Lab, Johns Hopkins Medicine Lead: Jenny Sadler Senior PI: Susan Carnell Introduction Brief background Early to Late Adolescence represents a critical period of development Behavioral response to food cues changes between young adolescence to young adulthood (Martin et al 2019) Transient insulin resistance during puberty may affect brain response to food cues (Kromer et al 2013) Compared to adults, children may have different reactivity to food cues Children attend more strongly to unhealthy foods and do not shift their attention away from unhealthy foods like adults (Junghans et al 2015) This may be in part due to differences in traits like inhibitory control and the development of greater myelination and resting-state metabolism of the PFC during adolescence. Few studies have examined differences in neural response to food cues in adolescence as a function of age Charbonnier et al 2018 found that liking of HED - LED foods (e.g. hedonic difference) decreased with age. No differences in brain response to food images between age groups (children, teens, adults, elderly). Fasted state showed stronger dmPFC/dlPFC activation across age groups for high vs. low calorie food images. Van Meer et al 2016 found that “Children had a stronger activation in the left precentral gyrus than did adults in response to unhealthy compared with healthy foods. In children, unhealthy foods elicited stronger activation in the right inferior temporal and middle occipital gyri, left precentral gyrus, bilateral opercular part of the inferior frontal gyrus, left hippocampus, and left middle frontal gyrus.” Morys et al 2020 found that “age significantly influences brain response differences to food cues in the left insula [negative relationship] and the left fusiform gyrus [positive relationship]” in a meta-analysis of studies in adolescents and (mostly) adults Gap: While differences in brain response to food cues are associated with age, few studies have examined the effect of age in Aim &amp; Hypotheses Aim 1: Identify age-dependent brain response to food cues in an early adolescent sample (n = 22) and a late adolescent sample (n = 73). Hypothesis 1: Controlling for BMI, increasing age will be associated with greater response in the right fusiform gyrus to food images compared to nonfood images. Also, age will be negatively associated with response in the precentral gyrus to high energy-dense food images compared to low energy-dense food images. Aim 2: Test for differences in age-dependent brain response to food cues by child obesity as determined by child BMI category. Hypothesis 2: There will be difference in age-dependent brain response between BMI groups, such that among lean adolescents, there will be a positive relationship between age and insula response, while among overweight/obese adolescents, the relationship between age and insula response will be negative. Aim 3: Test for moderation of age-dependent brain response to food cues by obesity risk group as determined by maternal BMI. Hypothesis 2: Controlling for child BMI and prescan hunger, there will be an interaction of obesity risk group and age, where age-dependent response in the right fusiform gyrus to food images will be higher in young children with obesity risk compared to those without obesity risk. Methods Basic study design Cross-sectional analysis using data from two independent samples. Sample Data Status: Registration prior to analyses, but data from the project have been analyzed for other papers (most likely for a manuscript): As of the date of submission, the data have been published (in separate analyses/manuscripts) Sample description Late Adolescent Sample (n = 73) Participants were recruited to examine the influence of parental obesity on children’s weight, eating behavior, and brain response to food cues. Data collection included the following: Neuroimaging: participants completed two days of neuroimaging scans; one in a fasted state and one in a fed state. Imaging included anatomical and fMRI scans. fMRI task was a picture paradigm where adolescents were presented with food and non-food images. Food images were further split into high-calorie and low-calorie items. Behavioral data: adolescents and their mothers completed demographic and anthropometric measures at the baseline visit. Early Adolescent Sample (n = 22) Participants were recruited to examine the influence of parental obesity on children’s weight, eating behavior, and brain response to food cues. Data collection was designed to enrich the late adolescent sample. Data collection included the following: Neuroimaging: participants completed one fMRI scan. Participants did not receive instruction Behavioral data: adolescents and their mothers completed demographic and anthropometric measures at the baseline visit. Measures &amp; variables Measure/Variable Scoring and Manipulation IV, DV, or Control Variable Used How? Brain response to food cues Contrasts of interest: Food images &gt; nonfood images High calorie food images &gt; low calorie food images DV Outcome of analyses Age NA IV Used as covariate in analysis Child BMI-for-age percentile &amp; Child BMI group BMI calculated from Child’s measured height and weight, percentile calculated from CDC Growth Charts BMI groups are as followed: Healthy weight: 5-85 %tile Overweight: 85-95%tile Obese: 95%tile or higher IV Used as covariate in analysis Maternal BMI &amp; Maternal BMI group Calculated from Mother’s measured height and weight BMI groups are as followed: Healthy weight: less than 25kg/m2 Overweight: 25-30 kg/m2 Obese: 30kg/m2 or higher IV Used as covariate in analysis Child Sex NA Control Variable Used as a control variables in analysis Child prescan hunger rating NA Control Variable Used to determine which condition in the late-adolescent sample (fasted or fed) best matches the hunger state of the early- adolescent sample Used as a control variables in analysis Household Income Responses from the Early Adolescent sample included an extra category at the top end of the income scale ($150K or more) that was not included in the late adolescent sample. Control Variable Used to describe samples Maternal Highest Level of Education NA Control Variable Used to describe samples Race NA Control Variable Used to describe samples Ethnicity NA Control Variable Used to describe samples Data processing &amp; Analysis Plan Data processing Behavioral Data Processing Common measures across the two studies were aggregated into one data file. Household income response options were different between the two samples, where responses from the early adolescent sample included an extra category at the top end of the income scale ($150K or more as highest response) that was not included in the late adolescent sample ($100K or more as highest response), thus “$150K or more” responses in the early-adolescent sample were recoded to “$100K or more” to allow for direct comparisons. Neuroimaging Data Preprocessing Following data collection, the following preprocessing steps were completed on data from both samples: Motion Correction and Artifact Detection: completed using Artifact Detection Tools (ART) in SPM. Threshold for motion was set at 3mm, and subjects with more than 10% of time points with motion spikes were excluded from the analysis. Slice Timing Correction: performed in SPM. Realignment: performed in SPM. Normalisation: performed in SPM. Smoothing: performed in SPM. Model Estimation (First Level): performed in SPM. Contrasts: Food Images &gt; Non-Food Images High Calorie Food Images &gt; Low Calorie Food Images Statistical modeling Aim 1: Age-dependent brain response to food cues Individual level contrast: food image vs. non-food image, high calorie food image vs. low calorie food image Age as a continuous covariate at the group-level (SPM level 2) (model example). Continuous variable, mean-centered Control variables included after age: sample (dichotomous, 1 or 2), child prescan hunger rating (continuous, mean centered), child BMI-for-age percentile (continuous, mean-centered) Group level contrasts: Positive effect of age (1 0 0 0) Negative effect of age (-1 0 0 0) Aim 2: Interaction of child age and child BMI group on brain response to food cues Individual level contrast: food image vs. non-food image, high calorie food image vs. low calorie food image Age as a continuous, mean centered covariate and child BMI group (Healthy weight, overweight, obese) as a categorical variable. Control variables included after age: sample (dichotomous, 1 or 2), child prescan hunger rating (continuous, mean centered) Group level contrast: F-test of interaction of age and child BMI group on brain response to food cues (0 0 0 1 1) Aim 2: Interaction of child age and maternal BMI group on brain response to food cues Individual level contrast: food image vs. non-food image, high calorie food image vs. low calorie food image Age as a continuous, mean centered covariate and maternal BMI group (Healthy weight, overweight, obese) as a categorical variable Control variables included after age: sample (dichotomous, 1 or 2), child prescan hunger rating (continuous, mean centered) If interaction of child BMI and age (aim 2) shows significant effect, we will control from child BMI group as well. Group level contrast: F-test of interaction of age and maternal BMI group on brain response to food cues (0 0 0 1 1) Statistical Thresholding Behavioral Analysis To adjust for multiple comparisons, group comparisons of the two samples will use a p-threshold adjusted for the False Discovery Rate (pFDR) using the Benjamini-Hochberg procedure &lt; 0.05. Neuroimaging Analysis To adjust for multiple comparisons, we will use probabilistic Threshold-free Cluster Enhancement (TFCE) implemented using the pTFCE toolbox in SPM https://spisakt.github.io/pTFCE/ From the toolbox documentation… “The core of pTFCE is a conditional probability, calculated based on Bayes' rule, from the probability of voxel intensity and the threshold-wise likelihood function of the measured cluster size. The current implementation provides an estimation of these distributions based on Gaussian Random Field (GRF) theory. Smoothness parameters of the GRF can be estimated based on the data. The conditional probabilities are then aggregated across cluster-forming thresholds by a novel incremental aggregation method. Our approach is validated on simulated and real fMRI data.” - Tamás Spisák, Zsófia Spisák, Matthias Zunhammer, Ulrike Bingel, Stephen Smith, Thomas Nichols, Tamás Kincses, Probabilistic TFCE: a generalised combination of cluster size and voxel intensity to increase statistical power. Neuroimage, 185:12-26. DOI: 10.1016/j.neuroimage.2018.09.078 The resulting significant peaks will be reported.