pISSN 1738-6586   eISSN 2005-5013
Open Access, Peer-reviewed
    J Clin Neurol. 2023 Mar;19(2):138-146. English.
    Published online Jan 02, 2023.
    https://doi.org/10.3988/jcn.2022.0021
    Copyright © 2023 Korean Neurological Association
    Original Article

    Cortical Thickness and Brain Glucose Metabolism in Healthy Aging

    Kyoungwon Baik,a Seun Jeon,a Soh-Jeong Yang,a Yeona Na,a Seok Jong Chung,a Han Soo Yoo,a Mijin Yun,b Phil Hyu Lee,a Young H. Sohn,a and Byoung Seok Yea
      • aDepartment of Neurology, Yonsei University College of Medicine, Seoul, Korea.
      • bDepartment of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Korea.
    • Correspondence: Byoung Seok Ye, MD, PhD. Department of Neurology, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea. Tel +82-2-2228-1609, Fax +82-2-393-0705, Email: romel79@gmail.com
    Received January 10, 2022; Revised August 04, 2022; Accepted August 07, 2022.

    This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

    Abstract

    Background and Purpose

    We aimed to determine the effect of demographic factors on cortical thickness and brain glucose metabolism in healthy aging subjects.

    Methods

    The following tests were performed on 71 subjects with normal cognition: neurological examination, 3-tesla magnetic resonance imaging, 18F-fluorodeoxyglucose positron-emission tomography, and neuropsychological tests. Cortical thickness and brain metabolism were measured using vertex- and voxelwise analyses, respectively. General linear models (GLMs) were used to determine the effects of age, sex, and education on cortical thickness and brain glucose metabolism. The effects of mean lobar cortical thickness and mean lobar metabolism on neuropsychological test scores were evaluated using GLMs after controlling for age, sex, and education. The intracranial volume (ICV) was further included as a predictor or covariate for the cortical thickness analyses.

    Results

    Age was negatively correlated with the mean cortical thickness in all lobes (frontal and parietal lobes, p=0.001; temporal and occipital lobes, p<0.001) and with the mean temporal metabolism (p=0.005). Education was not associated with cortical thickness or brain metabolism in any lobe. Male subjects had a lower mean parietal metabolism than did female subjects (p<0.001), while their mean cortical thicknesses were comparable. ICV was positively correlated with mean cortical thickness in the frontal (p=0.016), temporal (p=0.009), and occipital (p=0.007) lobes. The mean lobar cortical thickness was not associated with cognition scores, while the mean temporal metabolism was positively correlated with verbal memory test scores.

    Conclusions

    Age and sex affect cortical thickness and brain glucose metabolism in different ways. Demographic factors must therefore be considered in analyses of cortical thickness and brain metabolism.

    Keywords
    MRI; cortical thickness; FDG; brain glucose metabolism; healthy aging

    INTRODUCTION

    Cortical atrophy in MRI is a useful imaging biomarker for detecting and monitoring several dementia diseases, including Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and vascular dementia.1, 2 Advances in neuroimaging techniques, especially in PET using specific tracers for β-amyloid and dopamine transporter (DAT), made accurate in vivo diagnoses of AD and DLB possible.3, 4, 5 Characteristic patterns of cerebral glucose metabolism on 18F-fluorodeoxyglucose (FDG) PET are also useful for the differential diagnosis of neurodegenerative diseases.6, 7

    Reliable methods have been developed for quantitatively measuring brain atrophy and metabolism. However, the confounding effects of demographic factors such as aging and sex need to be considered in studies on dementia diseases, since these factors affect brain atrophy and metabolism. The effect of age on cortical atrophy has varied among neuroimaging studies; aging was found to be related to widespread cortical thinning8, 9 or to thinning in some parts of the brain such as the anterior frontal and temporal lobes,10 or the occipital and temporal lobes.11 A sex effect has not been consistently found in previous studies, with females found to have thicker cortexes than males11, 12, 13 or vice versa.10 The effect of education has also been inconclusive.10, 11

    Neuroimaging studies related to brain metabolism have found inconsistent results for the effects of demographic factors. Age-related hypometabolism was relatively localized in several brain areas compared with cortical thinning. Higher age was related to decreased metabolism in the anterior brain, but there has been inconsistency regarding the specific cortical region.8, 9, 14, 15 The sex effect differed among studies, with female participants having higher overall cerebral glucose metabolism than male participants,15 and the regions with high brain metabolism differing between the sexes.8 Few studies have investigated the effects of education on brain metabolism.15, 16

    These inconsistent results might be attributable to differences in study populations and comorbid neuropathologies. We are not aware of any database that contains extensive MRI and FDG-PET data collected from subjects with normal cognition. We therefore collected structural MRI and FDG-PET data on subjects with normal cognition who underwent β-amyloid PET and DAT-PET in order to exclude underlying neurodegenerative changes. We evaluated the effects of demographic factors including age, sex, and education on cortical thickness by using structural MRI and on brain glucose metabolism by using FDG-PET, and their relationships with cognition scores. We hypothesized that demographic factors, especially age, significantly affect brain atrophy and glucose metabolism.

    METHODS

    Participants

    This study initially included 141 subjects who did not have subjective cognitive dysfunction. The inclusion criteria of this study were 1) age >50 years and 2) no objective cognitive dysfunction in the detailed neuropsychological test as described below. The exclusion criteria were 1) score on the Korean version of the Mini-Mental State Examination (K-MMSE) of <26; 2) difficulty in participating in coordinating interviews and self-administered surveys (literacy, hearing impairment, and speech impairment); 3) previous history of neurological or psychiatric disorders such as territorial cerebral infarction, severe head trauma, brain surgery, intracranial hematoma with permanent brain lesion, major affective disorder, schizophrenia, or schizoaffective disorder; 4) contraindication to MRI; or 5) underwent radiation therapy or radiation exposure tests in another clinical study. Among the 141 subjects, 57 were excluded, comprising 2 subjects who had abnormal K-MMSE scores, 43 who had abnormalities in the neuropsychological test, and 12 who had abnormalities in brain MRI. Further evaluations were finally performed on 84 subjects (37 males and 47 females), which consisted of PET scans for β-amyloid deposition, DAT uptake, and glucose metabolism for the subjects that used 18F-florbetaben (FBB) PET, 18F-N-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl) nortropane (FP-CIT) PET, and FDG-PET. FBB-PET was applied to 32 of the 37 male subjects, which revealed 2 (6.3%) who were amyloid positive, and to 33 of the 47 female subjects, which revealed 2 (6.1%) who were amyloid positive. FP-CIT-PET scans were performed on 26 male and 30 female subjects, and 1 female (3.3%) had decreased striatal DAT uptake. All male subjects and 38 of the 47 female subjects received FDG-PET. Participants who had abnormal PET findings or did not receive FDG-PET were excluded. Finally, 71 participants were included for further analysis (Supplementary Fig. 1 in the online-only Data Supplement).

    Standard protocol approval, registration, and patient consent

    This study was approved by the Institutional Review Board of Yonsei University College of Medicine (No. 4-2015-0551). Informed consent was obtained from all participants.

    Neuropsychological tests

    All participants underwent the Seoul Neuropsychological Screening Battery17 and standardized z scores based on age- and education-matched norms were available for attention, language, visuospatial function, memory, and frontal/executive function. We included the digit-span backward test for the attention domain; the Korean version of the Boston Naming Test for the language domain; copying item of the Rey–Osterrieth Complex Figure (RCFT) test for the visuospatial domain; immediate recall, 20-minute delayed recall, and recognition items of the RCFT and Seoul Verbal Learning Test (SVLT) for the memory domain; and the semantic Controlled Oral Word Association Test (COWAT), phonemic COWAT and the Stroop color reading test for the frontal/executive domain. The K-MMSE was used to assess global cognitive performance.

    Image acquisition and interpretation

    All MRI scans were acquired using a 3.0-tesla scanner (Philips Intera, Philips Medical System, Best, the Netherlands) using a previously described protocol.2 The head of each subject was firmly fixed during the scan using foam padding, neck cushions, and Velcro straps to minimize motion artifacts. A high-resolution T1-weighted MRI volume data set was obtained from all subjects using a three-dimensional T1-TFE sequence configured with the following acquisition parameters: axial acquisition with a 224×256 matrix, 256×256 reconstructed matrices with 182 slices, 220 mm field of view, 0.98×0.98×1.2 mm3 voxels, 4.6 milliseconds echo time, 9.6 milliseconds repetition time, 8° flip angle, and 0 mm slice gap. FDG-PET scans were performed using a Discovery 600 scanner (General Electric Healthcare, Milwaukee, MI, USA). FDG-PET scans were performed according to the following protocol: Approximately 4.1 MBq/kg (body weight) 18F-FDG was administered intravenously to the patient. After a 60-minute uptake period, PET images were acquired for 15 minutes. A spiral computed tomography scan was performed for attenuation correction with a 0.8 second rotation time at 60 mA and 120 kVp, and with 3.75 mm section thickness, 0.625 mm collimation, and 9.375 mm table feed per rotation. FDG-PET images were reconstructed using the ordered subset expectation maximization algorithm with 4 iterations and 32 subsets.

    Amyloid positivity was assessed using a surface-based PET image analysis based on the cutoff value of the global standardized uptake value ratio of 1.478.18 The detailed methods of FBB-PET image analysis were reported for our previous study.2 DAT-PET and FDG-PET were assessed by a nuclear medicine physician (M.Y.) using visual interpretation. DAT-PET image interpretation was based on a dichotomous classification (normal/abnormal), with homogeneous and symmetrical striatal DAT uptake regarded as normal and asymmetrically or subregionally decreased striatal DAT uptake regarded as abnormal.19 FDG-PET image interpretation was also dichotomous,and based on visual assessments of spatial patterns in FDG uptake and the degree of alterations.20

    Cortical thickness measurement

    We used the CIVET pipeline (http://mcin.ca/civet/) to measure the cortical thickness. In brief, the T1-weighted image of each subject was corrected for intensity inhomogeneities and linearly registered to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) atlas of the Montreal Neurological Institute (MNI), which is a T1-weighted template for older adults.21, 22 The images were then classified based on tissue type,23 and the inner and outer cortical surfaces were extracted, resulting in 40,962 vertex points per hemisphere.24 Cortical thickness was calculated as the Laplacian distance between the linked vertices of the inner and outer surfaces. The measured cortical thickness was smoothed using a surface-based diffusion smoothing kernel (full width at half maximum [FWHM] of 30 mm). The mean lobar cortical thickness was calculated for the frontal, temporal, parietal, and occipital cortices. One participant was further excluded due to insufficient data quality for the cortical thickness analysis, and 70 participants were finally analyzed in the study.

    FDG-PET processing

    We linearly registered FDG-PET images to individual T1-weighted MRI using rigid-body transformation. We then spatially normalized the images to the ADNI-MNI atlas using nonlinear warping fields acquired in the T1-weighted image processing stage, and then smoothed them using a FWHM of 4 mm with a Gaussian kernel. To calculate the FDG subject residual profile (FDG-SRP), each data set was transformed into its logarithmic form, and the data matrix was centered by subtracting the mean of each subject and the group mean voxel profile from the data.25 The gray matter (GM) probability map obtained from the tissue classification was nonlinearly transformed into the ADNI-MNI atlas. We averaged all individual GM probability maps and assigned each voxel to either the foreground or background by binarizing more than 30% of the map to generate a study-specific GM mask. Statistical analyses of the FDG-SRP were performed within this GM mask. The mean lobar FDG-SRP was calculated for the frontal, temporal, parietal, and occipital cortices based on the automated anatomical labelling atlas.26

    Statistical analyses

    Statistical analyses for demographic and clinical data were performed using the Statistical Package for the Social Sciences (version 26.0, IBM, Chicago, IL, USA). We used the SurfStat toolbox (http://www.math.mcgill.ca/keith/surfstat/) developed at the MNI to perform vertex- and voxelwise statistical analyses. General linear models (GLMs) for vertexwise cortical thickness and voxelwise FDG-SRP were performed to evaluate the independent effects of age, sex, and education. The regional cortical thickness was analyzed by adding the intracranial volume (ICV) to the statistical model to account for individual differences in head size. The false discovery rate (FDR) method was used to correct for multiple statistical tests across multiple vertices or voxels.

    The effects of demographic factors on the mean lobar cortical thickness and brain glucose metabolism were evaluated using GLMs with age, sex, and education as predictors. The effects of mean lobar cortical thickness and brain glucose metabolism on neuropsychological test scores were then evaluated using GLMs after controlling for age, sex, and education. All GLMs for the mean cortical thickness itself and for the mean cortical thickness as a predictor further included the ICV in the statistical models. The FDR method was used to correct for multiple comparisons across 4 lobes or 13 neuropsychological test scores.

    RESULTS

    Demographics and imaging characteristics of study participants

    The 70 participants included 35 males. The age was 63.71±8.67 years (mean±standard deviation), and they had received 15.24±3.77 years of education. The total K-MMSE score was 29.27±0.93. Detailed demographic and imaging characteristics of study participants are presented in Table 1 and Supplementary Fig. 2 (in the online-only Data Supplement).

    Table 1
    Demographic and imaging characteristics

    Effects of demographic factors on regional cortical thickness and brain metabolism

    Evaluating the effects of age, education, and sex on regional cortical thickness and brain metabolism revealed that age was negatively correlated with cortical thickness in many regions except for the lateral temporal and medial frontal lobes (Fig. 1). Sex and education had no significant effects on regional cortical thickness. Age was negatively correlated with regional brain metabolism in the frontal, temporal, and parietal lobes, but positively correlated with regional metabolism in the pons, cerebellum, basal ganglia, thalamus, and several cortical areas mostly in the frontal, parietal, and occipital regions (Fig. 2). Sex and education were not associated with any regional brain metabolism.

    Fig. 1
    Effects of demographic factors on regional cortical thickness. A vertexwise general linear model was used to find the effects of age (negative correlation) (A), education (positive correlation) (B), and sex (female-male) (C) on regional cortical thickness. The three predictors were simultaneously included in the statistical model. The brain regions delineated with white lines are significant after the corrections with the false discovery rate (FDR) method for multiple tests across multiple vertices.

    Fig. 2
    Effects of demographic factors on regional brain glucose metabolism. A general linear model was used to determine the effects of age (positive correlation) (A), education (negative correlation) (B), and sex (female-male) (C) on regional brain glucose metabolism. The three predictors were simultaneously included in the statistical model. The brain regions delineated with white lines are significant regions after the corrections with the false discovery rate (FDR) method for multiple tests across multiple voxels.

    Predictors of mean lobar cortical thickness and brain glucose metabolism

    Evaluating the effects of age, education, and sex on mean lobar cortical thickness revealed that age was negatively correlated with the mean lobar cortical thickness in the frontal, temporal, parietal, and occipital cortices (Table 2). Sex and education were not associated with the mean cortical thickness. ICV was positively correlated with the mean cortical thickness in the frontal, temporal, and occipital lobes.

    Table 2
    Predictors of mean lobar cortical thickness and brain glucose metabolism

    GLM analysis for mean lobar brain glucose metabolism indicated that age was negatively correlated with the mean temporal lobe metabolism (Table 2). The mean level of parietal lobe metabolism was higher in female than in male subjects. Education had no significant effect on the mean lobar brain metabolism.

    Effects of mean lobar cortical thickness and brain metabolism on neuropsychological test scores

    The mean lobar cortical thickness was not significantly associated with neuropsychological test scores in any of the four lobar regions (Table 3). However, the mean temporal metabolism was positively correlated with the immediate and delayed recall items of SVLT (Table 4).

    Table 3
    Effects of mean lobar cortical thickness on neuropsychological test scores

    Table 4
    Effects of mean lobar glucose metabolism on neuropsychological test scores

    DISCUSSION

    This study evaluated the effects of demographic factors on cortical thickness and brain glucose metabolism and their relationships with cognition in healthy aging subjects. The major findings of our study were as follows: First, age was negatively correlated with cortical thickness in many cortical regions and with decreased mean brain glucose metabolism in the temporal lobe. Second, female subjects had higher cortical metabolism in the parietal lobe than did male subjects, while their mean cortical thicknesses were comparable. Third, the mean temporal metabolism was positively correlated with memory function, while the mean lobar cortical thickness was not associated with cognition scores. Together our results suggest that demographic factors affect cortical thickness and brain glucose metabolism differently and should therefore be considered when analyzing brain atrophy and glucose metabolism.

    Higher age was associated with cortical thinning in many cortical regions. Aging is a powerful predictor of brain atrophy.27, 28 However, the effect of aging has varied among studies.8, 9, 10, 11 These differences suggest that the aging effect on cortical thinning is not consistent in the brain. The pathology study indicated that both frontal and temporal cortical thickness decreased with aging; however, the frontal lobe had steeper age-related decreases in cortical thickness than did the temporal lobe. In this study, higher age was associated with cortical thinning in many cortical regions; however, the effect size was not consistent among cortices. Considering the standardized coefficient β, the age effect was prominent in the frontal, temporal, and occipital cortices, suggesting that these regions were the core of the aging process.

    In our study, aging was associated with decreased mean temporal glucose metabolism. Although voxelwise analysis indicated a negative correlation between age and brain metabolism in many cortical regions (Fig. 2), there was also a positive correlation mostly in the frontal, parietal, and occipital regions. Our study was consistent with previous studies in finding that the temporal lobe was vulnerable to age-related change.9 It was particularly interesting that aging was related to increased brain metabolism in the pons, cerebellum, basal ganglia, thalamus, and some cortical regions. Age-related hypermetabolic patterns have also been reported in previous studies.29, 30, 31 Considering that regional hypermetabolism related to aging is prominent in the cerebellum, basal ganglia, and motor cortex, aging-related regional hypermetabolism could be associated with reduced motor control inhibition.32 Hypermetabolism was also found to be related to tau deposition in patients with MCI and low amyloid levels,33 and with other degenerative diseases including frontotemporal dementia34 and DLB.35 Although we excluded subjects with significant amyloid deposition, we cannot completely exclude the possibility of preclinical neurodegenerative processes.

    Female sex was associated with higher cerebral glucose metabolism in the parietal cortex, which is consistent with previous studies finding higher global36 and parietal9 brain metabolism in females than in males. This sex-related metabolic difference might be explained by biological factors (i.e., sex chromosomes or hormones) or social factors (i.e., smoking or alcohol consumption). However, since we did not compare the brain metabolic differences among younger patients (premenopause, <45 years old) or the exact statuses of social factors, further studies are needed. There was also no sex-related difference in cortical thickness. Only one previous study that we are aware of simultaneously evaluated brain atrophy and metabolic changes related to demographic factors in normal aging,9 which also indicated that sex-related changes in brain atrophy and metabolism are not completely parallel. Our results suggest that changes in brain metabolism are more sensitive to sex-related factors than structural changes.

    This study had several limitations. First, the age distribution was not even, since 51 of 70 subjects (72.9%) were younger than 69 years (but older than 50 years old), 14 (20.4%) were between 70 and 79 years old, and only 5 (7.1%) were older than 80 years. The inclusion of more subjects with normal cognition is needed to establish mean cortical thickness values for those aged 70–89 years. Second, the mean education duration was too long to represent the general population, which limits the generalizability of our results. Third, not all subjects underwent FBB-PET and FP-CIT-PET simultaneously. Although all subjects showed normal visual results in FDG-PET, we cannot exclude the possibility that very early changes in degenerative diseases had already begun. There were also several neurodegenerative disorders that were not associated with amyloid pathology, dopaminergic depletion, or abnormal brain glucose metabolism; we therefore cannot exclude the possibility of these diseases having effects. Notwithstanding these limitations, our results suggest that demographic factors affect cortical thickness and brain glucose metabolism differently in normal aging, and therefore the effects of demographic factors must be considered when interpreting the results obtained in studies of the cortical thickness and brain metabolism.

    Supplementary Materials

    The online-only Data Supplement is available with this article at https://doi.org/10.3988/jcn.2022.0021.

    Supplementary Fig. 1

    Flow chart of the subject selection process. DAT, dopamine transporter; FBB, florbetaben; FDG, fluorodeoxyglucose; K-MMSE, Korean version of the Mini-Mental State Examination.

    Click here to view.(22K, pdf)

    Supplementary Fig. 2

    Age distribution of subjects.

    Click here to view.(14K, pdf)

    Notes

    Author Contributions:

    • Conceptualization: Kyoungwon Baik, Seun Jeon, Byoung Seok Ye.

    • Data curation: Kyoungwon Baik, Seun Jeon, Soh-Jeong Yang, Yeona Na.

    • Formal analysis: Kyoungwon Baik, Seun Jeon, Byoung Seok Ye.

    • Funding acquisition: Byoung Seok Ye.

    • Investigation: Kyoungwon Baik, Soh-Jeong Yang, Yeona Na.

    • Methodology: Kyoungwon Baik, Seun Jeon, Byoung Seok Ye.

    • Project administration: Seok Jong Chung, Han Soo Yoo, Phil Hyu Lee, Young H. Sohn, Byoung Seok Ye.

    • Resources: Seun Jeon, Byoung Seok Ye.

    • Software: Seun Jeon.

    • Supervision: Phil Hyu Lee, Young H. Sohn, Byoung Seok Ye.

    • Validation: Kyoungwon Baik, Seun Jeon, Byoung Seok Ye.

    • Visualization: Kyoungwon Baik, Seun Jeon.

    • Writing—original draft: Kyoungwon Baik, Seun Jeon, Byoung Seok Ye.

    • Writing—review & editing: all authors.

    Conflicts of Interest:The authors have no potential conflicts of interest to disclose.

    Funding Statement:This study was funded by Eisai Korea Inc.

    Availability of Data and Material

    The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

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    MeSH Terms
    Brain*
    Cognition
    Demography
    Education
    Female
    Glucose*
    Healthy Aging*
    Humans
    Linear Models
    Magnetic Resonance Imaging
    Male
    Memory
    Metabolism*
    Neurologic Examination
    Neuropsychological Tests
    Occipital Lobe
    Parietal Lobe
    Positron-Emission Tomography
    Rabeprazole
    Respect
    Metrics
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