Abstract
Purpose
Human ageing is associated with a regional reduction in cerebral neuronal activity as assessed by numerous studies on brain glucose metabolism and perfusion, grey matter (GM) density and white matter (WM) integrity. As glucose metabolism may impact energetics to maintain myelin integrity, but changes in functional connectivity may also alter regional metabolism, we conducted a cross-sectional simultaneous FDG PET/MR study in a large cohort of healthy volunteers with a wide age range, to directly assess the underlying associations between reduced glucose metabolism, GM atrophy and decreased WM integrity in a single ageing cohort.
Methods
In 94 healthy subjects between 19.9 and 82.5 years (mean 50.1 ± 17.1; 47 M/47F, MMSE ≥ 28), simultaneous FDG-PET, structural MR and diffusion tensor imaging (DTI) were performed. Voxel-wise associations between age and grey matter (GM) density, RBV partial-volume corrected (PVC) glucose metabolism, white matter (WM) fractional anisotropy (FA) and mean diffusivity (MD), and age were assessed. Clusters representing changes in glucose metabolism correlating significantly with ageing were used as seed regions for tractography. Both linear and quadratic ageing models were investigated.
Results
An expected age-related reduction in GM density was observed bilaterally in the frontal, lateral and medial temporal cortex, striatum and cerebellum. After PVC, relative FDG uptake was negatively correlated with age in the inferior and midfrontal, cingulate and parietal cortex and subcortical regions, bilaterally. FA decreased with age throughout the entire brain WM. Four white matter tracts were identified connecting brain regions with declining glucose metabolism with age. Within these, relative FDG uptake in both origin and target clusters correlated positively with FA (0.32 ≤ r ≤ 0.71) and negatively with MD (− 0.75 ≤ r ≤ − 0.41).
Conclusion
After appropriate PVC, we demonstrated that regional cerebral glucose metabolic declines with age and that these changes are related to microstructural changes in the interconnecting WM tracts. The temporal course and potential causality between ageing effects on glucose metabolism and WM integrity should be further investigated in longitudinal cohort PET/MR studies.
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Abbreviations
- GM:
-
Grey matter
- WM:
-
White matter
- FDG:
-
Fluorodeoxyglucose
- PET/MR:
-
Positron emission tomography/magnetic resonance
- DTI:
-
Diffusion tensor imaging
- MRI:
-
Magnetic resonance imaging
- FA:
-
Fractional anisotropy
- PVC:
-
Partial volume correction
- MD:
-
Mean diffusivity
- SPM:
-
Statistical parametric mapping
- WMH:
-
White matter hyperintensities
- SPECT:
-
Single photon emission computed tomography
- CBF:
-
Cerebral blood flow
- ASL:
-
Arterial spin labelling
- CSF:
-
Cerebrospinal fluid
- GTM:
-
Geometric transfer matrix
- VOI:
-
Volume-of-interest
- ROI:
-
Region of interest
- MMSE:
-
Mini-mental state examination
- BDI:
-
Beck’s depression inventory
- TOF:
-
Time of flight
- OSEM:
-
Ordered subset expectation maximization
- FWHM:
-
Full width half maximum
- TR:
-
Repetition time
- TE:
-
Echo time
- FLAIR:
-
Fluid-attenuated inversion recovery
- VBM:
-
Voxel-based morphometry
- TIV:
-
Total intra-cranial volume
- FWE:
-
Family wise error
- kE :
-
Cluster extent
- RPD:
-
Reduction per decade
- RBV:
-
Region-based voxel-wise
- FSL:
-
FMRIB software library
- BIC:
-
Bayesian information criterion
References
DeCarli C, Massaro J, Harvey D, Hald J, Tullberg M, Au R, et al. Measures of brain morphology and infarction in the Framingham heart study: establishing what is normal. Neurobiol Aging. 2005;26:491–510.
Walhovd KB, Westlye LT, Amlien I, Espeseth T, Reinvang I, Raz N, et al. Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol Aging. 2011;32:916–32.
Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14:21–36.
Crivello F, Tzourio-Mazoyer N, Tzourio C, Mazoyer B. Longitudinal assessment of global and regional rate of grey matter atrophy in 1,172 healthy older adults: modulation by sex and age. PLoS One. 2014;9:e114478.
Walhovd KB, Fjell AM, Reinvang I, Lundervold A, Dale AM, Eilertsen DE, et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging. 2005;26:1261–70.
Kennedy KM, Erickson KI, Rodrigue KM, Voss MW, Colcombe SJ, Kramer AF, et al. Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry. Neurobiol Aging. 2009;30:1657–76.
Liu H, Yang Y, Xia Y, Zhu W, Leak RK, Wei Z, et al. Aging of cerebral white matter. Ageing Res Rev. 2017;34:64–76.
Cox SR, Ritchie SJ, Tucker-Drob EM, Liewald DC, Hagenaars SP, Davies G, et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat Commun. 2016;7:1–13.
de Groot M, Ikram MA, Akoudad S, Krestin GP, Hofman A, van der Lugt A, et al. Tract-specific white matter degeneration in aging: the Rotterdam Study. Alzheimer’s Dement. 2015;11:321–30.
Hsu JL, Van Hecke W, Bai CH, Lee CH, Tsai YF, Chiu HC, et al. Microstructural white matter changes in normal aging: a diffusion tensor imaging study with higher-order polynomial regression models. Neuroimage. 2010;49:32–43.
Salami A, Eriksson J, Nilsson LG, Nyberg L. Age-related white matter microstructural differences partly mediate age-related decline in processing speed but not cognition. Biochim Biophys Acta - Mol Basis Dis. 2012;1822:408–15.
de Groot M, Cremers LGM, Ikram MA, Hofman A, Krestin GP, van der Lugt A, et al. White matter degeneration with aging: longitudinal diffusion MR imaging analysis. Radiology. 2016;279:532–41.
Sexton CE, Walhovd KB, Storsve AB, Tamnes CK, Westlye LT, Johansen-Berg H, et al. Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J Neurosci. 2014;34:15425–36.
Lu H, Xu F, Rodrigue KM, Kennedy KM, Cheng Y, Flicker B, et al. Alterations in cerebral metabolic rate and blood supply across the adult lifespan. Cereb Cortex. 2011;21:1426–34.
Zhang N, Gordon ML, Ma Y, Chi B, Gomar JJ, Peng S, et al. The age-related perfusion pattern measured with arterial spin labeling MRI in healthy subjects. Front Aging Neurosci. 2018;10:214.
Pagani M, Salmaso D, Jonsson C, Hatherly R, Jacobsson H, Larsson SA, et al. Regional cerebral blood flow as assessed by principal component analysis and 99mTc-HMPAO SPET in healthy subjects at rest: normal distribution and effect of age and gender. Eur J Nucl Med. 2002;29:67–75.
Ishibashi K, Onishi A, Fujiwara Y, Oda K, Ishiwata K, Ishii K. Longitudinal effects of aging on 18F-FDG distribution in cognitively normal elderly individuals. Sci Rep. 2018;8:11557.
Kakimoto A, Ito S, Okada H, Nishizawa S, Minoshima S, Ouchi Y. Age-related sex-specific changes in brain metabolism and morphology. J Nucl Med. 2016;57:221–5.
Malpetti M, Ballarini T, Presotto L, Garibotto V, Tettamanti M, Perani D. Gender differences in healthy aging and Alzheimer’s dementia: a 18 F-FDG-PET study of brain and cognitive reserve. Hum Brain Mapp. 2017;38:4212–27.
Yoshizawa H, Gazes Y, Stern Y, Miyata Y, Uchiyama S. Characterizing the normative profile of 18F-FDG PET brain imaging: sex difference, aging effect, and cognitive reserve. Psychiatry Res - Neuroimaging. 2014;221:78–85.
Ishibashi K, Miura Y, Toyohara J, Ishii K, Ishiwata K. Comparison of imaging using 11 C-ITMM and 18 F-FDG for the detection of cerebellar ataxia. J Neurol Sci. 2017;375:97–102.
Fujimoto T, Matsumoto T, Fujita S, Takeuchi K, Nakamura K, Mitsuyama Y, et al. Changes in glucose metabolism due to aging and gender-related differences in the healthy human brain. Psychiatry Res - Neuroimaging. 2008;164:58–72.
Knopman DS, Jack CR, Wiste HJ, Lundt ES, Weigand SD, Vemuri P, et al. 18F-fluorodeoxyglucose positron emission tomography, aging, and apolipoprotein E genotype in cognitively normal persons. Neurobiol Aging. 2014;35:2096–106.
Greve DN, Svarer C, Fisher PM, Feng L, Hansen AE, Baare W, et al. Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data. Neuroimage. 2014;92:225–36.
Petit-Taboué MC, Landeau B, Desson JF, Desgranges B, Baron JC. Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping. Neuroimage. 1998;7:176–84.
Bonte S, Vandemaele P, Verleden S, Audenaert K, Deblaere K, Goethals I, et al. Healthy brain ageing assessed with 18F-FDG PET and age-dependent recovery factors after partial volume effect correction. Eur J Nucl Med Mol Imaging. 2017;44:838–49.
Van Laere K, Versijpt J, Audenaert K, Koole M, Goethals I, Achten E, et al. 99mTc-ECD brain perfusion SPET: variability, asymmetry and effects of age and gender in healthy adults. Eur J Nucl Med. 2001;28:873–87.
Mozley PD, Sadek AM, Alavi A, Gur RC, Muenz LR, Bunow BJ, et al. Effects of aging on the cerebral distribution of technetium-99m hexamethylpropylene amine oxime in healthy humans. Eur J Nucl Med. 1997;24:754–61.
Greve DN, Salat DH, Bowen SL, Izquierdo-Garcia D, Schultz AP, Catana C, et al. Different partial volume correction methods lead to different conclusions: an 18F-FDG-PET study of aging. Neuroimage. 2016;132:334–43.
Thomas BA, Cuplov V, Bousse A, Mendes A, Thielemans K, Hutton BF, et al. PETPVC: a toolbox for performing partial volume correction techniques in positron emission tomography. Phys Med Biol. 2016;61:7975–93.
Oyama S, Hosoi A, Ibaraki M, McGinnity CJ, Matsubara K, Watanuki S, et al. Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging. EJNMMI Phys. 2020;7:57.
Thomas BA, Erlandsson K, Modat M, Thurfjell L, Vandenberghe R, Ourselin S, et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2011;38:1104–19.
Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol. 2012;57:R119–59.
Chételat G, Landeau B, Salmon E, Yakushev I, Bahri MA, Mézenge F, et al. Relationships between brain metabolism decrease in normal aging and changes in structural and functional connectivity. Neuroimage. 2013;76:167–77.
Inoue K, Ito H, Uchida S, Taki Y, Kinomura S, Tsuji I, et al. Decrease in glucose metabolism in frontal cortex associated with deterioration of microstructure of corpus callosum measured by diffusion tensor imaging in healthy elderly. Hum Brain Mapp. 2008;29:375–84.
Kuczynski B, Targan E, Madison C, Weiner M, Zhang Y, Reed B, et al. White matter integrity and cortical metabolic associations in aging and dementia. Alzheimer’s Dement. 2010;6:54–62.
Kochunov P, Ramage AE, Lancaster JL, Robin DA, Narayana S, Coyle T, et al. Loss of cerebral white matter structural integrity tracks the gray matter metabolic decline in normal aging. Neuroimage. 2009;45:17–28.
De Leeuw FE, De Groot JC, Achten E, Oudkerk M, Ramos LMP, Heijboer R, et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry. 2001;70:9–14.
Rezaei A, Schramm G, Van Laere K, Nuyts J. Estimation of crystal timing properties and efficiencies for the improvement of (joint) maximum-likelihood reconstructions in TOF-PET. IEEE Trans Med Imaging. 2020;39:952–63.
Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–80.
Yang J, Huang SC, Mega M, Lin KP. Investigation of partial volume correction methods for brain FDG pet studies. IEEE Trans Nucl Sci. 1996;43:3322–7.
Nugent S, Croteau E, Potvin O, Castellano CA, Dieumegarde L, Cunnane SC, et al. Selection of the optimal intensity normalization region for FDG-PET studies of normal aging and Alzheimer’s disease. Sci Rep Nat Res. 2020;10:1–8.
Leemans A, Jeurissen B, Sijbers J, Jones D. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proc 17th Sci Meet Int Soc Magn Reson Med. 2009;3537.
Kass RE, Raftery AE. Bayes factors. J Am Stat Assoc. 1995;90:773–95.
van Rossum G. Python tutorial. Technical Report CS-R9526. Amsterdam; 1995.
Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019; 15: 565–81.
Allen JS, Bruss J, Brown CK, Damasio H. Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region. Neurobiol Aging. 2005;26:1245–60.
Bagarinao E, Watanabe H, Maesawa S, Mori D, Hara K, Kawabata K, et al. An unbiased data-driven age-related structural brain parcellation for the identification of intrinsic brain volume changes over the adult lifespan. Neuroimage. 2018;169:134–44.
McDonald CR, McEvoy LK, Gharapetian L, Fennema-Notestine C, Hagler DJ, Holland D, et al. Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology. 2009;73:457–65.
Choy SW, Bagarinao E, Watanabe H, Ho ETW, Maesawa S, Mori D, et al. Changes in white matter fiber density and morphology across the adult lifespan: a cross‐sectional fixel‐based analysis. Hum Brain Mapp. 2020;41:3198–211.
Freeman SH, Kandel R, Cruz L, Rozkalne A, Newell K, Frosch MP, et al. Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without alzheimer disease. J Neuropathol Exp Neurol. 2008;67:1205–12.
Scheff SW, Price DA, Sparks DL. Quantitative assessment of possible age-related change in synaptic numbers in the human frontal cortex. Neurobiol Aging. 2001;22:355–65.
Itoh Y, Yamada M, Suematsu N, Matsushita M, Otomo E. An immunohistochemical study of centenarian brains: a comparison. J Neurol Sci. 1998;157:73–81.
Michiels L, Delva A, van Aalst J, Ceccarini J, Vandenberghe W, Vandenbulcke M, et al. Synaptic density in healthy human aging is not influenced by age or sex: a 11C-UCB-J PET study. Neuroimage. 2021;232:117877.
Fjell AM, Westlye LT, Espeseth T, Reinvang I, Walhovd KB, Dale AM. Cortical gray matter atrophy in healthy aging cannot be explained by undetected incipient cognitive disorders: a comment on Burgmans et al. (2009). Neuropsychology. 2010;24:258–66.
Bakkour A, Morris JC, Wolk DA, Dickerson BC. The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: specificity and differential relationships with cognition. Neuroimage. 2013;76:332–44.
Smith CD, Chebrolu H, Wekstein DR, Schmitt FA, Markesbery WR. Age and gender effects on human brain anatomy: a voxel-based morphometric study in healthy elderly. Neurobiol Aging. 2007;28:1075–87.
Oschwald J, Guye S, Liem F, Rast P, Willis S, Röcke C, et al. Brain structure and cognitive ability in healthy aging: a review on longitudinal correlated change. Rev Neurosci. 2019;31:1–57.
Hedman AM, van Haren NEM, Schnack HG, Kahn RS, Hulshoff Pol HE. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp. 2012;33:1987–2002.
Herholz K, Salmon E, Perani D, Baron JC, Holthoff V, Frölich L, et al. Discrimination between Alzheimer dementia and controls by automated analysis of multicenter FDG PET. Neuroimage. 2002;17:302–16.
Pardo JV, Lee JT, Sheikh SA, Surerus-Johnson C, Shah H, Munch KR, et al. Where the brain grows old: decline in anterior cingulate and medial prefrontal function with normal aging. Neuroimage. 2007;35:1231–7.
Ibáñez V, Pietrini P, Furey ML, Alexander GE, Millet P, Bokde ALW, et al. Resting state brain glucose metabolism is not reduced in normotensive healthy men during aging, after correction for brain atrophy. Brain Res Bull. 2004;63:147–54.
Curiati PK, Tamashiro-Duran JH, Duran FLS, Buchpiguel CA, Squarzoni P, Romano DC, et al. Age-related metabolic profiles in cognitively healthy elders: results from a voxel-based [18F]fluorodeoxyglucose-positron-emission tomography study with partial volume effects correction. Am J Neuroradiol. 2011;32:560–5.
Nugent S, Tremblay S, Chen KW, Ayutyanont N, Roontiva A, Castellano CA, et al. Brain glucose and acetoacetate metabolism: a comparison of young and older adults. Neurobiol Aging. 2014;35:1386–95.
Rathee R, Rallabandi VPS, Roy PK. Age-related differences in white matter integrity in healthy human brain: evidence from structural MRI and diffusion tensor imaging. Magn Reson Insights. 2016;9:9–20.
Catani M, Mesulam M. The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex. 2008;44:953–61.
Teubner-Rhodes S, Vaden KI, Cute SL, Yeatman JD, Dougherty RF, Eckert MA. Aging-resilient associations between the arcuate fasciculus and vocabulary knowledge: microstructure or morphology? J Neurosci. 2016;36:7210–22.
Ikuta T, Gollnick HM, Rutledge AN. Age associated decline in the arcuate fasciculus and IQ. Brain Imaging Behav. 2020;14:362–7.
Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: anatomy, function, and dysfunction. Neurosci Biobehav Rev. 2018;92:104–27.
Zahr NM, Rohlfing T, Pfefferbaum A, Sullivan EV. Problem solving, working memory, and motor correlates of association and commissural fiber bundles in normal aging: a quantitative fiber tracking study. Neuroimage. 2009;44:1050–62.
Yuan R, Di X, Taylor PA, Gohel S, Tsai YH, Biswal BB. Functional topography of the thalamocortical system in human. Brain Struct Funct. 2016;221:1971–84.
Bartzokis G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging. 2004;25:5–18.
Suetens K, Nuttin B, Gabriëls L, Van Laere K. Differences in metabolic network modulation between capsulotomy and deep-brain stimulation for refractory obsessive-compulsive disorder. J Nucl Med. 2014;55:951–9.
Burgmans S, Van Boxtel MPJ, Vuurman EFPM, Smeets F, Gronenschild EHBM, Uylings HBM, et al. The prevalence of cortical gray matter atrophy may be overestimated in the healthy aging brain. Neuropsychology 2009;23:541–50.
Tseng BY, Gundapuneedi T, Khan MA, Diaz-Arrastia R, Levine BD, Lu H, et al. White matter integrity in physically fit older adults. Neuroimage. 2013;82:510–6.
Tournier J-D, Mori S, Leemans A, Morgan RH, Reson M, Author M. Diffusion tensor imaging and beyond NIH public access author manuscript. Magn Reson Med. 2011;65:1532–56.
Raffelt DA, Smith RE, Ridgway GR, Tournier JD, Vaughan DN, Rose S, et al. Connectivity-based fixel enhancement: whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres. Neuroimage. 2015;117:40–55.
Schramm G, Koole M, Willekens SMA, Rezaei A, Van Weehaeghe D, Delso G, et al. Regional accuracy of ZTE-based attenuation correction in static 18F-FDG and dynamic 18F-PE2I brain PET/MR. Front Phys. 2019. https://doi.org/10.3389/fphy.2019.00211.
Acknowledgements
The authors acknowledge the contributions by Stefanie Willekens, PhD, on data acquisition, Kim Serdons, Pharm PhD and the radiopharmacy team for tracer production, Kwinten Porters, Jef Van Loock, Guido Putzeys, Kris Byloos and Stefan Ghysels for data acquisition, and Ronald Peeters, Nathalie Mertens from the PET/MR physics team.
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van Aalst, J., Devrome, M., Van Weehaeghe, D. et al. Regional glucose metabolic decreases with ageing are associated with microstructural white matter changes: a simultaneous PET/MR study. Eur J Nucl Med Mol Imaging 49, 664–680 (2022). https://doi.org/10.1007/s00259-021-05518-6
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DOI: https://doi.org/10.1007/s00259-021-05518-6