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Comparison of Brain Activation to Food Images in Normal Weight and Obese Children

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Brain Activation to Food Images Differs in Healthy Normal Weight and Obese Children


Background and Purpose: Despite intervention efforts, childhood obesity remains a growing major health concern. Understanding how children process food stimuli may provide information relevant to how unhealthy eating habits develop in ways that promote childhood obesity. In this study, we used functional MRI (fMRI) to evaluate whether brain activation to food images differs between normal weight and obese young children.

Materials and MethodsTwenty-two healthy children (N=11/11 for normal weight/obese respectively) between 8-10 years of age were successfully scanned. Brian activation maps generated by responses to food images were compared between the normal weight and obese children. Region of interest (ROI) analyses were also applied to evaluate the group differences in mean activation area and mean activation strength.

Results: Greater activation of visual cortex was observed in both normal weight and obese children when viewing food versus non-food images. Average brain activation maps in response to food versus non-food images showed differences between normal weight and obese groups in areas related to memory and cognitive control. Specifically, normal weight children showed greater activation of posterior parahippocampal gyri (PPHG) and dorsomedial prefrontal cortex (DMPFC) regions than obese children. ROI analyses further indicated higher activation strength in the right PPHG (P=0.01) and higher activation strength (P<0.001) as well as a larger activation area (P=0.02) in the DMPFC in normal weight than obese children.

Conclusion Normal weight and obese children process food stimuli differently. The observed differences in brain activation to food images characterizing these children provide information that may assist in the development of intervention strategies to prevent childhood obesity.  Further studies are warranted to understand if brain differences precede obesity development, or if they are results from events that manifest once excess weight gain has taken place.


Childhood obesity is a major public health concern and  its prevalence has continued to increase in the last decades [12]. In 2011-2014, the rate of obesity among children and adolescents between 2 – 19 years of age in the United States was 17.0% and extreme obesity was 5.8% [1]. Overweight and obese children are at increased risk for being overweight or obese as adults and for the adverse health consequences associated with these conditions [34]. For example, childhood obesity is strongly linked to metabolic complications and chronic illnesses like hypertension, diabetes mellitus, and cardiovascular diseases [5-7], and cognitive function and academic performance may also be affected [8-10]. Childhood obesity is related to a number of risk factors such as genetic, physical activity and family environment [21112], but  unhealthy eating habits may also play an important role [1314]. A better understanding of the correlates and determinants of children’s eating habits would help develop effective interventions to address childhood obesity.

Recently, fMRI has been extensively used to study brain activation in response to food stimuli. When stimulated by food images, certain brain regions may be activated, including orbitofrontal cortex [15-19], insula [1718], striatum [16], and amygdala [1920]. Factors influencing the specific activation pattern observed include the motivational status of participants, which reflects how hungry they are [20], and their perception of the energy density of food images, i.e. high or low calorie content [21].  Studies comparing brain activation to food images in obese and normal weight adolescents have reported these groups differ in response patterns [22-24].  For example, obese individuals, in general, exhibit increased brain activation in regions related to motivation-reward pathways [25-27]. Although there have been several studies evaluating brain activation patterns to food images in obese and normal weight adults, there have been few of these studies in obese children. Examining and understanding how neuronal responses involved in processing food stimuli differ between normal weight and obese children at an early age could provide important information to guide strategies to deal with unhealthy eating habits and to prevent obesity in children. In this study, we used fMRI to determine whether brain activation patterns to food images differ in preadolescent normal weight and obese children.


Study population:

Healthy normal weight (BMI<75th percentile) and obese (BMI>95th percentile) children (age 8-10 years) were recruited for this study. All experimental procedures were approved by the institutional review board at the University of Arkansas for Medical Sciences, and informed consents/assents were obtained for all subjects. Inclusion criteria: parental reports of full-term gestation and birth weight being between 5th – 95th percentiles; and parental report of right hand dominance. Exclusion criteria: maternal diabetes; maternal alcohol, tobacco, or drug use during pregnancy; chronic sleep disorder; history of psychological or psychiatric diagnoses; history of neurological impairment or injury; surgical implant or other foreign object in the body; dental work which may cause artefacts in MRI; known claustrophobia; and high likelihood of inability to tolerate loud noise from the MRI scanner. Twenty seven children were initially enrolled; 1 voluntarily withdrew; 2 did not attempt the MRI; 1 did not complete the fMRI, and 1 had an invalid fMRI scan due to excessive motion. In total, 22 children (11/11 for normal weight/obese) completed the scan and have valid fMRI as well as structural imaging data and were included in this study. The demographic information for all subjects is listed in Table 1.

MRI/fMRI Data Acquisition

All subjects had an MRI/fMRI examination at the Arkansas Children’s Hospital on a 1.5 Tesla Achieva MRI scanner (Philips Healthcare, Best, The Netherlands) with an 8-channel SENSE head coil. All scans were done on Saturday mornings at around 9 am. Subjects all had breakfast before coming onsite for the scan. Imaging sequences included a sagittal T1-weighted 3D turbo field echo sequence for structural MRI, with 7.4 ms TR, 3.5 ms TE, 8º flip angle, no slice gap, 1 mm x 1 mm x 1 mm acquisition voxel size, 256 x 232 x 150 matrix size; and an axial single-shot gradient echo EPI sequence for fMRI, with 2500 ms TR, 50 ms TE, 2.4 mm x 2.4 mm x 5.0 mm acquisition voxel size, 128 x 128 reconstruction matrix size, 20 slices, 4 dummy scans, and 120 dynamics. An Eloquence fMRI system (Invivo Corporation, Orlando, FL, US) was used to display the fMRI paradigm (and play a movie during the structural MRI scan) and was synchronized with the MRI scanner. The fMRI paradigm included a picture viewing task, in which a single image of food or non-food item was displayed on the center of the screen for each trial and subjects were instructed to look at the screen all the time during the task.  A block design consisting of 6 non-food image blocks (which served as baseline condition) and 6 food image blocks (which served as activation condition) alternating with each other was used for the fMRI. Each block lasted for 25 seconds and included 10 trials with each trial displaying one image on the screen for 2.5 seconds. Therefore, there were 10 images for each block, and the 60 food and 60 non-food images in total for the whole fMRI paradigm were all different.

MRI/fMRI Data Analysis

All MRI data were exported to a workstation with FMRIB Software Library V5.0 (FSL, created by the Functional MRI of the Brain Analysis Group, University of Oxford, UK) installed on a VMware Linux virtual machine (VMware, Inc., Palo Alto, CA USA).  The FSL’s FEAT program was used for the fMRI data analysis. Specifically, all fMRI images were preprocessed for motion detection and correction using FSL commands such as fsl_motion_outliers and fsl_regfilt, and maximum rotation/translation were limited to 2.5º/1.5mm, respectively. Additional preprocessing tools in FEAT were used, including MCFLIRT for motion correction, BET for non-brain tissue removal, slice time correction, high pass temporal filtering, and spatial smoothing using a Gaussian kernel with a 5mm FWHM. By using both linear and non-linear registration programs (FLIRT/FNIRT), the preprocessed fMRI images were registered to the T1-weighted 3D structural images for each subject and then normalized to a customized template that was created in FSL using the T1 3D images for all subjects. Time series statistical analysis was performed using the FMRIB Improved Linear Model. Standard and extended motion parameters (as estimated by the MCFLIRT) were included as confounding explanatory variables in the model. Results were entered into higher level analysis of the FEAT program to compute for average activation maps for each group. Regions of interest (ROI) method was used to compare the brain activation in the normal weight and obese groups. Specifically, anatomical regions showed apparent activation difference on the average activation maps in FSL were sketched in a separated software MATLAB (The MathWorks, Inc., MA, USA) as the ROIs, with the sketching based on the anatomy of the full region as shown on the T1 weighted high resolution images, and then the mean Z score as well as activated imaging voxels in each ROI for each subject were calculated and compared between groups.


For the comparisons of demographic parameters and ROI analysis of fMRI activation results (including mean Z scores and numbers of activated imaging voxels) between normal weight and obese children, non-parametric Wilcoxon rank-sum tests were used for numerical parameters, while Chi Square Test were used for categorical variables. P < 0.05 was regarded as significant. For the calculation of average activation maps for each group in the FEAT program, statistical significance was defined as a threshold of Z score > 2.3 and P < 0.05 (whole brain cluster-wise corrected).



The normal weight and obese children groups did not differ in sex composition (P = 0.67), or age at MRI (P = 0.10), but as per experimental design were significantly different in BMI (P < 0.001). The picture viewing task with non-food images as baseline condition and food images as activation condition consistently activated the visual cortex for both normal weight and obese children (Figure 1). However, both left and right posterior parahippocampal gyri (PPHG) were activated by food images for the normal weight but not the obese children (Figure 1). Likewise, the dorsomedial prefrontal cortex (DMPFC) was activated for the normal weight but not the obese children (Figure 1).

The Left and right PPHG (Figure 2a) as well as the DMPFC (Figure 3a) were sketched based on anatomy using the customized T1-weighted imaging template as the ROIs for further post hoc analysis of the activation maps. These analyses revealed that in the right PPHG (RPPHG), normal weight compared with obese children had significantly stronger mean activation strength (P=0.01) and tended to have a larger mean activation area (P=0.08); in the left PPHG (LPPHG), there was a trend for stronger mean activation strength in normal weight than obese children but both groups showed comparable mean activation areas (Figure 2b,c). In the DMPFC, normal weight children had both significantly stronger mean activation strength (P<0.001) and larger mean activation area (P=0.02) compared with obese children (Figure 3b,c).


Our results showed significant differences in brain activation patterns to food versus non-food images between healthy normal weight and obese children. Specifically, the PPHG and the DMPFC were activated in normal weight but not in obese children based on the mean activation maps; additional ROI analysis showed significant differences in mean activation strength of the right PPHG, and in both mean activation strength and mean activation area of the DMPFC. Brain activation differences in response to food images between the normal weight and obese preadolescent children in our study are not surprising, as differences were previously documented in other age groups. However, previous studies were usually highlighting regions of increased activation in obese subjects and giving less attention to regions of increased activation in healthy subjects.  For example, several studies reported significant increase in activation in striato-limbic regions including putamen/caudate, insula, orbitofrontal cortex and amygdala in obese versus normal weight adults [2627]. Negative correlation was also seen between BMI and ventromedial and ventrolateral prefrontal cortex activation in adults [2829]. It is noteworthy that brain activation patterns in response to food stimuli can be affected by motivational status of participants and energy density of food [2021]. For example, hunger and satiety rates in adults were positively correlated with activation in orbitofrontal cortex and insula [1517]. In other adult studies, high calorie food increased activation in satiety-related regions like lateral orbitofrontal cortex, and low calorie food increased activation in hunger-related regions like medial orbitofrontal and insular cortex [2729]. In our study, all participants consumed breakfast before scans and the fMRI studies were performed at approximately the same morning time of the day, which made confounding factors such as motivational status less relevant between groups. The images presented to both groups were the same, which also excluded confounding of food energy density. Nevertheless, our fMRI paradigm may have resulted in differences in activation maps compared to other studies, due to different experimental designs.

Visual cortex was consistently activated in both normal weight and obese children when comparing viewing food versus non-food images. Activation of the occipital region is a common finding in viewing food images [182130-33]. In a meta-analysis conducted by Van der Laan et al. [21], lateral occipital complex (LOC, a component of visual association cortex) was one of the most commonly activated regions in viewing food vs. non-food images in adults. It was suggested that activation of LOC cannot be explained by its role in object recognition since food and non-food images were usually matched to neutralize this effect. A more likely explanation is that increased activation in LOC is related with higher alertness to food versus non-food objects and, as a result, stronger visual cortex activation [21]. No significant difference in activation of visual cortex was noticed in our study when comparing normal weight with obese children.

The PPHG was significantly more activated in normal weight versus obese children in our fMRI study. It is widely believed that hippocampal and parahippocampal networks are related to declarative memory functions. PPHG plays crucial role in memory encoding and retrieval [34-36]. Similar to our study, Davids et al. found increased activation in the PPHG when comparing normal weight versus obese children when viewing food stimuli [23]. It was suggested that this pattern of activation could also indicate increased alertness for food images. In the same study [23], normal weight children also showed increased activation in other areas such as anterior cingulate cortex, fusiform gyrus, thalamus, caudate and parts of the visual cortex. These differences were not observed in our study, presumably due to different paradigm designs that two groups of food images (neutral and pleasant) were used in their study. Another study showed that increased PPHG volume in children is associated with lower increase in BMI [37], suggesting a role of PPHG and its functioning associated with childhood obesity. PPHG activation was also noticed in healthy control subjects in response to food images in a study of hyperphagia in children with Prader-Willi Syndrome [38]. On the other hand, a meta-analysis showed increased activation in parahippocampal gyrus in obese compared with  normal weight  adults when processing food stimuli [39]. It was suggested that parahippocampus might be involved in food seeking behavior in hunger status, and hunger was not the case in our study. Nevertheless, these studies included food stimuli other than viewing images, such as actual food intake, and tastes/odors of food.. Moreover, most of the studies included in this meta-analysis were evaluating adult populations; except one study that enrolled adolescent subjects. . This could indicate differences in activation between children and adults in viewing food images.

The second brain region that was significantly more activated in normal weight versus obese children in our study was the DMPFC. Several neuroimaging studies in adults have linked DMPFC activation to social cognition, mentallization, and processing information related to social judgment [40-42]. DMPFC also plays an important role in decision making and response control in variety of contexts  [4344], particularly under conflicting and uncertain conditions [45-48]. In adults, value signal of different options is encoded in MPFC and is also activated when making food decisions [49-51]. Van Meer et al. suggested that children tend to make their food choices based on taste rather than healthiness [52]. We postulate that awareness of healthiness of food choices may have had an effect in our study. Higher DMPFC activation in normal weight children could indicate increased conflict and more information processing regarding value of food images. This could be associated with different attitudes toward food and retrieval of associations with different past experiences related to food choices. Another possible explanation for DMPFC activation could be related to its role in reward pathways [5354]. Higher DMPFC activation in normal weight children could indicate they were able to activate reward-related centers by just viewing food images whereas obese children did not have as strong activation without actually consuming food.

The food addiction model of obesity links obesity to abnormal activation in cognitive control and a number of motivation-reward pathways in the brain [55]. Nevertheless, this model of obesity is still controversial [5657]. Absence of activation differences in many important motivation-reward areas in our study suggest that childhood obesity may not be explained merely by the food addiction model of obesity. The differences we observed were mostly in cognitive control and memory areas rather than reward circuits as classically depicted in adult studies. This could suggest how normal weight and obese children differently recall past understanding related to food healthiness and react to food stimuli. It could also point toward the importance of early and sustained education in children about healthy food choices.

There are some limitations for this study. First, food calorie contents were not considered when presenting food images. Second, there were no food-related behavioral assessments to correlate with our neuroimaging findings. Finally, although the brain activation differences we observed were significant between groups, the sample size in our study was relatively small. Nevertheless, the results of our study provided further evidence that normal weight children have different neuronal activation associated with viewing food images when compared with obese children, even at a young age. It adds to our understanding of brain-obesity association in childhood obesity.


This study was supported in part by USDA-ARS Project 6026-51000-010-05S at the Arkansas Children’s Nutrition Center, and a Marion B. Lyon Award at the Arkansas Children’s Research Institute. Dr. Ou was also partly supported by NIH COBRE grants P20GM121293 (Center for Translational Pediatric Research at Arkansas Children’s Research Institute) and P30GM110702 (Center for Translational Neuroscience at University of Arkansas for Medical Sciences). We also would like to thank the staff of the Arkansas Children’s Nutrition Center Clinical Research Core for their assistance with these studies.


Table 1: Demographic information for the participants:

  Normal weight (N = 11) Obese (N = 11) P value
Sex (male / female) 5 / 6 6 / 5 0.67
Age at MRI (years) 9.77 ± 0.70 9.11 ± 0.91 0.10
BMI 15.85 ± 1.07 24.74 ± 3.37 <0.001



Figure 1: Mean activation maps for the normal weight and obese children (at a Z score threshold of 2.3 and P < 0.05, corrected for the voxel-wise multiple comparisons). Red arrows point to regions (PPHG and MPFC) that were significantly activated in the normal weight but not obese children.

Figure 2: Group comparison of activation strength and total activated area in the PPHG. a) illustration of the region-of-interest (ROI) selection; b) group comparison of activation strength (mean z value) in left and right PPHG; and c) group comparison of activation area (number of voxels activated) in left and right PPHG.

* P<0.05 for the non-parametric Wilcoxon rank-sum test.

Figure 3: Group comparison of activation strength and total activated area in the DMPFC. a) illustration of the ROI selection; b) group comparison of activation strength (mean z value) in DMPFC; and c) group comparison of activation area (number of voxels activated) in DMPFC.

* P<0.05 for the non-parametric Wilcoxon rank-sum test.


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