Cortical Thickness and Brain Glucose Metabolism in Healthy Aging
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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 positronemission 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.
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 positronemission 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.
참고문헌 (36)
- Ye BS, "Amyloid burden, cerebrovascular disease, brain atrophy, and cognition in cognitively impaired patients", Alzheimers Dement, 11, pp.494~503 (2015)
- Lee YG, "Amyloid-β-related and unrelated cortical thinning in dementia with Lewy bodies", Neurobiol Aging, 72, pp.32~39 (2018)
- McKhann GM, "The diagnosis of dementia due to Alzheimer’s disease : recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer’s disease", Alzheimers Dement, 7, pp.263~269 (2011)
- McKeith IG, "Diagnosis and management of dementia with Lewy bodies : fourth consensus report of the DLB consortium", Neurolog, 89, pp.88~100 (2017)
- Burke JF, "Assessment of mild dementia with amyloid and dopamine terminal positron emission tomography", Brain, 134(Pt 6), pp.1647~1657 (2011)
- Walker Z, "Lewy body dementias", Lancet, 386, pp.1683~1697 (2015)
- Sperling RA, "Toward defining the preclinical stages of Alzheimer’s disease : recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer’s disease", Alzheimers Dement, 7, pp.280~292 (2011)
- Nugent S, "Glucose hypometabolism is highly localized, but lower cortical thickness and brain atrophy are widespread in cognitively normal older adults", Am J Physiol Endocrinol Metab, 306, pp.E1315~E1321 (2014)
- Kakimoto A, "Age-related sex-specific changes in brain metabolism and morphology", J Nucl Med, 57, pp.221~225 (2016)
- Seo SW, "Effects of demographic factors on cortical thickness in Alzheimer’s disease", Neurobiol Aging, 32, pp.200~209 (2011)
- van Velsen EF, "Brain cortical thickness in the general elderly population : the Rotterdam Scan Study", Neurosci Lett, 550, pp.189~194 (2013)
- Sowell ER, "Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age", Cereb Cortex, 17, pp.1550~1560 (2007)
- Luders E, "Gender effects on cortical thickness and the influence of scaling", Hum Brain Mapp, 27, pp.314~324 (2006)
- Mosconi L, "Glucose metabolism in normal aging and Alzheimer’s disease : methodological and physiological considerations for PET studies", Clin Transl Imaging, 1, pp.217~233 (2013)
- Yoshizawa H, "Characterizing the normative profile of 18F-FDG PET brain imaging : sex difference, aging effect, and cognitive reserve", Psychiatry Res, 221, pp.78~85 (2014)
- Kim J, "The effect of education on regional brain metabolism and its functional connectivity in an aged population utilizing positron emission tomography", Neurosci Res, 94, pp.50~61 (2015)
- 안현정, "Seoul Neuropsychological Screening Battery-Dementia Version (SNSB-D): A Useful Tool for Assessing and Monitoring Cognitive Impairments in Dementia Patients", Journal of Korean Medical Science, 25(7), pp.1071~1076 (2010), 대한의학회
- Sabri O, "Beta-amyloid imaging with florbetaben", Clin Transl Imaging, 3, pp.13~26 (2015)
- McKeith I, "Sensitivity and specificity of dopamine transporter imaging with 123IFP-CIT SPECT in dementia with Lewy bodies : a phase III, multicentre study", Lancet Neurol, 6, pp.305~313 (2007)
- Kato T, "Brain fluorodeoxyglucose(FDG)PET in dementia", Ageing Res Rev, 30, pp.73~84 (2016)
- Collins DL, "Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space", J Comput Assist Tomogr, 18, pp.192~205 (1994)
- "Atrophy specific MRI brain template for Alzheimer’s disease and mild cognitive impairment" (2011)[URL]
- Zijdenbos AP, "Automatic"pipeline"analysis of 3-D MRI data for clinical trials : application to multiple sclerosis", IEEE Trans Med Imaging, 21, pp.1280~1291 (2002)
- Kim JS, "Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification", Neuroimage, 27, pp.210~221 (2005)
- Moeller JR, "A regional covariance approach to the analysis of functional patterns in positron emission tomographic data", J Cereb Blood Flow Metab, 11, pp.A121~A135 (1991)
- Rolls ET, "Automated anatomical labelling atlas 3", Neuroimage, 206, pp.116189~ (2020)
- Svennerholm L, "Changes in weight and compositions of major membrane components of human brain during the span of adult human life of Swedes", Acta Neuropathol, 94, pp.345~352 (1997)
- Scahill RI, "A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging", Arch Neurol, 60, pp.989~994 (2003)
- Kim IJ, "Age-and sex-associated changes in cerebral glucose metabolism in normal healthy subjects : statistical parametric mapping analysis of F-18 fluorodeoxyglucose brain positron emission tomography", Acta Radiol, 50, pp.1169~1174 (2009)
- Moeller JR, "The metabolic topography of normal aging", J Cereb Blood Flow Metab, 16, pp.385~398 (1996)
- Loessner A, "Regional cerebral function determined by FDG-PET in healthy volunteers : normal patterns and changes with age", J Nucl Med, 36, pp.1141~1149 (1995)
- Levin O, "Aging and motor inhibition : a converging perspective provided by brain stimulation and imaging approaches", Neurosci Biobehav Rev, 43, pp.100~117 (2014)
- Rubinski A, "FDG-PET hypermetabolism is associated with higher tau-PET in mild cognitive impairment at low amyloid-PET levels", Alzheimers Res Ther, 12, pp.133~ (2020)
- Dukart J, "Differential effects of global and cerebellar normalization on detection and differentiation of dementia in FDG-PET studies", Neuroimage, 49, pp.1490~1495 (2010)
- Lee YG, "Effects of Alzheimer and Lewy body disease pathologies on brain metabolism", Ann Neurol, 91, pp.853~863 (2022)
- Goyal MS, "Persistent metabolic youth in the aging female brain", Proc Natl Acad Sci U S A, 116, pp.3251~3255 (2019)