Korean J Fam Pract 2021; 11(1): 61-66  https://doi.org/10.21215/kjfp.2021.11.1.61
Regional Cortical Thickness in Children and Adolescents with Obesity
Young Kyun Kim1, Se-Hong Kim2,*, Tae-Hong Kim2, Ju-Hye Chung3, Young-Mi Eun3
1Department of Family Medicine, Uijeongbu St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Uijeongbu; 2Department of Family Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon; 3Department of Family Medicine, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
Se-Hong Kim
Tel: +82-31-249-8316, Fax: +82-31-249-8006
E-mail: iron1600@catholic.ac.kr
ORCID: https://orcid.org/0000-0001-6465-8993
Received: August 21, 2020; Accepted: November 15, 2020; Published online: February 20, 2021.
© The Korean Academy of Family Medicine. All rights reserved.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: Previous studies have demonstrated obesity-associated changes in the brain in adults; however, no study has evaluated the cortical thickness or subcortical volumes in obese children and adolescents. The purpose of our study was to investigate changes in cortical thickness in asymptomatic children and adolescents with obesity.
Methods: A total of 21 participants (10 patients with obesity and 11 subjects without obesity), aged 6–18 years, underwent 3T brain magnetic resonance imaging (MRI) scanning, and cortical thickness was compared between the obese group and the control group across multiple locations. The subcortical volumes were also compared on a structure-by-structure basis.
Results: No significant differences between the obese and non-obese control group were observed with respect to the mean volumes of the total white matter in each hemisphere. However, the obese group showed a significant reduction in the mean cortical thickness of both hemispheres compared to the control group. Group comparison analysis of the regional cortical thicknesses between the two groups also revealed a significant reduction in the cortical thickness of the left supramarginal, inferior parietal, pars orbitalis, and pars opercularis cortices in the obese group compared with that in the control group (P<0.05, false discovery rate corrected).
Conclusion: We demonstrated a significant reduction in the thickness of the cortical areas of obese patients, especially in areas involved in body weight control. Our results suggest the existence of structural brain abnormalities in obese children and adolescents, and further prospective studies are required to evaluate this relationship.
Keywords: Obesity; Cortical Thickness; Children; Adolescent; Magnetic Resonance Imaging

In recent decades, the prevalence of obesity has increased dramatically worldwide to epidemic proportions.1) It has been reported that one-third of the adult population in most developed countries is obese,2) which has resulted in an increasing number of people with type 2 diabetes, cardiovascular disease, stroke, cancer, metabolic syndrome, liver disease, and other conditions.3) In particular, childhood obesity is a global health problem,4) and increasing rates of obesity, beginning in childhood, are expected to cause a significant decrease in life expectancy.5,6) Indeed, obesity in this age group is associated with adverse outcomes such as diabetes, high blood pressure, coronary artery disease, depressive symptoms, lower life satisfaction, and problematic patterns of alcohol use.7) Moreover, longitudinal studies have shown that obesity in childhood independently predicts future adverse health outcomes, including increased risk of mortality.8)

Interestingly, obesity is known to cause changes in brain structure. Brain imaging studies have demonstrated that obesity may be associated with both generalized and regional brain atrophy, as well as structural changes in white matter.9,10) However, only a few studies on the regional distribution of brain atrophy in children and adolescents have been reported so far. These studies of small, selected samples have reported associations of obesity with changes in brain structure and behavior, while neuroimaging studies have indicated decreased cortical thickness and subcortical volume.11) Another study on the mechanisms leading to childhood obesity has shown that obese adolescents have lower volumes of gray matter (GM) than lean individuals.12)

Although previous studies have demonstrated an association between adult obesity and changes in cerebral white matter integrity,13) no study to date has evaluated either cortical thickness or subcortical volumes in adolescents using magnetic resonance imaging (MRI). Therefore, it remains unclear whether early structural brain changes occur in asymptomatic children and adolescents. Since brain structure and functional changes during development in adults may not be generalizable to younger people, it is essential to conduct separate studies in children and adolescents. However, the effects of obesity on the brain in children and adolescents are poorly understood. This study investigated the brain structure in lean and obese adolescents using MRI and evaluated the associations between BMI and cortical thickness.


1. Subjects

In this study, 21 right-handed asymptomatic participants, aged 6–18 years, were recruited from the outpatient clinic at St. Vincent’s Hospital in South Korea. All subjects underwent comprehensive health screening and a brain MRI scan between March 2014 and September 2016. The participants included 10 patients with obesity and 11 without obesity.

The exclusion criteria were as follows: 1) psychological conditions, 2) history of endocrine disorders (including abnormal thyroid function and type 2 diabetes), 3) active neurological or psychiatric conditions, 4) alcohol or drug abuse (and/or history of substance abuse or addiction), and 5) mental retardation. A clinical neuroradiologist examined the brain MRIs of all subjects; no gross abnormalities were reported in any of the participants, and all showed apparently normal white matter. This study was approved by the Research Ethical Commitee of St. Vincent’s Hospital and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants prior to inclusion.

2. Risk factor assessment

Anthropometric, clinical, and laboratory investigations were performed for all subjects. The height of each participant was measured to the nearest 0.1 cm using a fixed wall-scale measuring device. Body weight was measured to the nearest 0.1 kg using a digital scale that was calibrated prior to each measurement. Body mass index (BMI; kg/m2) was calculated as the weight in kilograms (kg) divided by the square of height in meters (m). Waist circumference was measured twice at the end of a normal expiration on a horizontal plane immediately superior to the left iliac crest. Two blood pressure measurements were taken from all subjects at a 5-min interval and then averaged for analysis. Fasting plasma glucose, total cholesterol, triglycerides, and high-density lipoprotein (HDL)-cholesterol levels were measured after a 12-h fast using an auto-analyzer (Hitachi 747 auto-analyzer; Hitachi, Tokyo, Japan). Smoking status and alcohol use were also investigated.

3. Brain imaging and data processing

T1-weighted optimized high-resolution 3D magnetization-prepared rapid acquisition of gradient echo (3D-MPRAGE) images of the brain were acquired using a 3T whole-body scanner equipped with a 32-channel head coil (Verio; Siemens, Erlangen, Germany). There were 208 contiguous coronal slices with the following scanning parameters: TR/TI/TE=1,900/900/2.5 ms, FOV=250×250 mm, flip angle (FA)=9°, 256×256 matrix; isotropic voxel dimensions of 1.0 mm, thickness of 0.8 mm, and acquisition time of 7.34 min. FreeSurfer 5.1.0 (http://surfer.nmr.mgh.harvard.edu) was used to perform cortical reconstruction and volumetric segmentation of the brain. This software provides one of the best validated automated brain segmentation methods, the technical details of which have been described previously.14,15)

Briefly, the processing stream includes a Talairach transform of each subject’s native brain, removal of the non-brain tissue, and segmentation of the gray matter–white matter tissue. The cortical surface of each hemisphere was inflated to an average spherical surface to locate both the pial surface and the gray–white matter boundary. Blinded to the identity of the participant, we visually inspected the entire cortex of each subject and manually corrected any topologic defects. The cortical thickness was computed as the shortest distance between the pial surface and the gray–white matter boundary at each point throughout the cortical mantle. The global mean cortical thickness for each subject was computed by averaging the cortical thickness at each vertex, and the cortical thicknesses of the right and left hemispheres were also averaged separately; these values were used in the statistical analyses. The regional thickness value at each vertex for each subject was mapped to the surface of an average brain template, allowing visualization of data across the entire cortical surface (described at http://surfer.nmr.mgh.harvard.edu/fswiki/FsAverage). In addition, the entire cerebral cortex was parcellated into 34 regions,16) and various surface-based data, including maps of the cortical volume, surface area, curvature, and sulcal depth, were created. Data were resampled for all subjects using a common spherical coordinate system. The cortical map of each subject was smoothed using a Gaussian kernel of 10 mm full width at half-maximum for the analyses of the entire cortex. The subcortical volumes were obtained from the automated procedure for the volumetric measurement of brain structures implemented in FreeSurfer. A total of 40 volumetric measures were investigated, and eight subcortical structures (white matter, caudate, thalamus, pallidum, putamen, hippocampus, accumbens, and amygdala) were extracted from each hemisphere. All of these measures were corrected for their estimated total intracranial volume (eTIV) before statistical analysis. Reliability studies on measurements of cortical thickness and subcortical volumes reported that within-scanner variabilities in cortical thickness and subcortical volume measurements using FreeSurfer were estimated to be <0.03 mm and 4.3%, respectively.17)

4. Statistical analysis

The data were analyzed using the Statistical Package for the Social Sciences version 21 (IBM Co., Armonk, NY, USA) and are presented as means±standard deviation. Assumptions of normality were assessed using the Kolmogorov–Smirnov test for all continuous variables. All variables were normally distributed. An independent t-test or a χ2 test was used to test baseline comparisons between the obese and control groups for all demographic variables. The multivariate general linear model was implemented at each vertex in the whole brain to identify the regions in which obese patients showed significant differences in cortical thickness relative to the controls. The effects of age, education, total intracranial volume (TIV), and sex were regressed out in these models. All analyses were performed separately for the right and left hemispheres. Only regions with clusters of 10 or more contiguous voxels were reported, and the threshold was set at P<0.05, with a false discovery rate (FDR) correction for multiple comparisons. A two-tailed P-value<0.05 was considered statistically significant.


1. Baseline characteristics of the study participants

Table 1 summarizes the baseline characteristics of the study participants. There was no significant difference in sex, age, education, and glucose or lipid profile between the two groups. The BMI was significantly higher in the obese group than in the control group (25.14±2.75 kg/m2 vs. 21.21±2.08 kg/m2, P<0.05).

Table 1

General characteristics of the study participants (n=21)

VariableObese (n=10)Control (n=11)P-value
Age (y)15.40±4.0612.82±5.780.255
BMI (kg/m2)25.14±2.7521.21±2.080.001
Total cholesterol (mg/dL)136.67±16.56183.50±64.350.285
HDL cholesterol (mg/dL)49.50±10.6150.33±17.210.956
Triglyceride (mg/dL)64.50±27.58135.67±90.150.377
LDL cholesterol (mg/dL)69.50±33.23110.00±35.540.292
SBP (mmHg)118.25±11.29107.00±9.750.094
DBP (mmHg)72.63±8.6766.60±6.150.205
Glucose (mg/dL)95.56±15.7092.67±5.050.673
AST (IU)21.78±12.1818.57±3.640.515
ALT (IU)30.78±35.3516.29±6.400.306
Education (y)8.60±3.506.18±5.290.237
Sex (male/female)6/46/50.801

Values are mean±standard deviation or number.

BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP, systolic blood pressure; DBP, diastolic blood pressure; AST, aspartate transaminase; ALT, alanine aminotransferase.

Statistical significance was tested using independent t-tests or χ2 test.

2. Cortical thickness differences between the obese and control groups

No significant differences between the obese group and the control group were observed with regard to the mean volumes of the total white matter in each hemisphere. However, compared to the control group, the obese group showed a significant reduction in the mean cortical thickness in both hemispheres (Table 2). The ANCOVA, adjusted for age, education, TIV, and sex, revealed a significant cortical thickness reduction of the right inferior parietal lingual, middle temporal, pars orbitalis, pars triangularis, and rostral middle frontal cortices. For the left hemisphere, the thicknesses of the left inferior temporal, lateral orbitofrontal, medial orbitofrontal, pars orbitalis, and pars triangularis cortices were higher in the control group than in the obese group (P<0.05). Group comparison analysis of regional cortical thickness between the obese and control groups revealed a significant reduction in the thickness of the left supramarginal, inferior parietal, pars orbitalis, and pars opercularis cortices in the obese group compared with that in the control group (P<0.05; FDR corrected) (Figure 1 and Table 3).

Table 2

Cortical thickness between two groups

RegionObese group(n=10)Control group(n=11)P-value
Inferior parietal2.573±0.2232.787±0.1550.018
Middle temporal2.937±0.5003.284±0.1680.042
Pars orbitalis2.815±0.3113.090±0.1980.025
Pars triangularis2.503±0.2682.807±0.2890.022
Rostral middlefrontal2.460±0.1382.692±0.2970.036
Mean thickness2.561±0.2082.735±0.1430.036
Inferior parietal2.662±0.1792.760±0.1760.026
Inferior temporal2.751±0.4963.105±0.1640.037
Literal orbitofrontal2.640±0.2912.961±0.2980.022
Medial orbitofrontal2.546±0.1422.862±0.2560.003
Pars opercularis2.600±0.2632.793±0.1550.012
Pars orbitalis2.697±0.2703.135±0.2800.002
Pars triangularis2.556±0.1782.824±0.2560.013
Mean thickness2.570±0.1422.737±0.1510.017

Values are mean±standard deviation.

Statistical significance was tested using analysis of covariance adjusted for age, education, total intracranial volume, and gender.

Table 3

Mean cortical thickness for clusters where a significant cortical atrophy was observed in obese patients compared to controls (FDR corrected, P<0.05)

RegionCortical thickness (mm)No. of vertexes in clusterCluster size(mm2)Talairach coordinates(X, Y, Z)

Obese group (n=10)Control group (n=11)
Inferior parietal2.751±0.4963.105±0.16412878.73-44.5-70.523
Pars orbitalis2.697±0.2703.135±0.2809042.21-44.240.2-13.4
Pars opercularis2.600±0.2632.793±0.155145.72-

Values are mean±standard deviation.

FDR, false discovery rate.

Statistical significance was tested using analysis of covariance adjusted for age, education, total intracranial volume, and gender.

Figure 1. Statistical maps, corrected for age, education, and sex, showing reduced cortical thickness in obese patients relative to that in controls (P<0.05; FDR corrected). The anatomical terms follow the Desikan template.16) FDR, false discovery rate.

To the best of our knowledge, this is the first study to evaluate the pattern of cortical atrophy in adolescent obesity using MRI. Our results demonstrated a significant reduction in the thickness of the left supramarginal, inferior parietal, pars orbitalis, and pars opercularis cortices in the obese group compared with that in the control group. These findings suggest that structural differences in cortical areas may be related to obesity in children and adolescents.

We also showed significant reductions in the cortical thickness of the left supramarginal, inferior parietal, pars orbitalis, and pars opercularis cortices of adolescents in the obese group compared with that in the control group. This finding has implications for the nature of the relationship between brain anatomy and weight gain. Consistent with our results, a similar pattern of cortical change was described in previous studies on obesity, which reported an association between BMI and cortical thickness in adults. However, the relationship between obesity and brain volume in older adolescents is less well established. In one study, obese adolescents (14–21 years old) were found to have reduced orbitofrontal cortex volume, high scores on all domains of the Three Factor Eating Questionnaire, and impaired cognitive task performance, notably during tests on inhibitory control. It was hypothesized to reflect a relationship between body weight, orbitofrontal cortex (OFC) function, and a tendency to disinhibit eating.18) Another functional imaging study in women with a mean age of 18 years, found a global decrease in gray matter in obese individuals compared to that in lean or overweight individuals.12) In contrast, no significant association between cortical thickness and BMI was found in children.19)

A recent structural MRI study of obesity indicated that BMI is negatively associated with the gray matter corresponding to the somatosensory maps of the mouth, lips, and tongue.20) In that study, obese subjects presented with lower GM densities than lean individuals in the frontal operculum and postcentral gyrus, which include cortical areas representing taste processing. Another positron emission tomography scan (PET) study showed higher increases in the neural activity of the frontal operculum and postcentral gyrus in response to the administration of a liquid meal in obese women.21) They also suggested that this higher increase in cerebral blood flow of the parietal cortex in response to food exposure in obese subjects might be due to an initially lower neural activity of this brain area at rest.22) Consistent with previous studies, our results showed that the obese group presented with significantly lower cortical thickness of the left pars opercularis and parietal cortices. Although there was a reduction in the cortical thickness of the parietal lobe in the obese group, no change in the frontal gray matter was observed in our study. A similar pattern of cortical atrophy has been described in early Alzheimer’s disease in a longitudinal MRI study,23) in which gray matter loss began in the temporal, entorhinal, and parietal lobes before progressing to orbitofrontal regions and beyond in the left hemisphere. These findings suggest early vulnerability of the fronto-parietal executive system according to adolescent obesity.

Interestingly, our study showed a structural change in brain regions related to feeding and body weight control in children and adolescents with obesity. A significant reduction in the cortical thickness of the left frontal and parietal cortices was observed in the obese group compared with that in the control group. Moreover, numerous functional imaging studies have reported that the parietal cortex plays an important role in the response to food stimuli and the control of food intake. The fasted or “hungry” state is associated with increased resting-state regional cerebral blood flow and increased activity in response to visual food cues in the insular cortex.24,25) Given the importance of regulating eating behavior, cortical atrophy in this area may diminish its capacity to receive information on changes in energy balance after food consumption, which may lead to excessive food intake. Increased activity in the parietal cortex in response to visual food exposure was also reported in a previous study of obese women, but a similar response was not observed in normal-weight women.22) In addition, in a large study of 1,428 individuals, higher BMIs were associated with smaller volumes in the frontal, temporal, and parietal cortices, as well as the cerebellum.26)

This study has some limitations. First, not all subcortical regions can be examined using the present methods; for instance, FreeSurfer software does not offer automatic segmentation for the hypothalamus. Another limitation of the present study is its cross-sectional nature; as a result, we were unable to determine causality between structural brain changes and obesity or whether they are reversible. Furthermore, due to the small sample size, we found differences in subcortical volume in the subjects with obesity, which failed to reach significance after correction for multiple comparisons. However, in other studies on childhood obesity, BMI was correlated with subcortical reward-related regions.11,12,27) Further prospective studies with larger sample sizes are required to evaluate the relationship between changes in brain structure and childhood obesity over time.

In conclusion, we demonstrated a significant reduction in the cortical thicknesses, especially of the areas involved in body weight control and cognitive function, such as the left supramarginal, inferior parietal, pars orbitalis, and pars opercularis cortices, of obese patients. Based on these findings, it is likely that obese children and adolescents exhibit structural brain abnormalities. Further prospective studies are required to evaluate the relationship between early structural brain changes and BMI in children and adolescents with obesity.


This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1F1A1062937) and St. Vincent’s Hospital, Research Institute of Medical Science Foundation (SVHR-2015-03).


No potential conflict of interest relevant to this article was reported.

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Funding Information
  • Ministry of Science and ICT, South Korea
      No. 2019R1F1A1062937
  • St. Vincent’s Hospital, Research Institute of Medical Science Foundation

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