Korean Journal of Radiology

  • 제22권7호
  • /
  • Pages.1194-1202
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  • 2021
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Arterial Spin Labeling Magnetic Resonance Imaging in Healthy Adults: Mathematical Model Fitting to Assess Age-Related Perfusion Pattern

  • Ying Hu (Department of Radiology, West China Hospital, Sichuan University) ;
  • Rongbo Liu (Department of Radiology, West China Hospital, Sichuan University) ;
  • Fabao Gao (Department of Radiology, West China Hospital, Sichuan University)
  • 투고 : 2020.04.08
  • 심사 : 2021.01.08
  • 발행 : 2021.07.01
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초록

Objective: To investigate the age-dependent changes in regional cerebral blood flow (CBF) in healthy adults by fitting mathematical models to imaging data. Materials and Methods: In this prospective study, 90 healthy adults underwent pseudo-continuous arterial spin labeling imaging of the brain. Regional CBF values were extracted from the arterial spin labeling images of each subject. Multivariable regression with the Akaike information criterion, link test, and F test (Ramsey's regression equation specification error test) was performed for 7 models in every brain region to determine the best mathematical model for fitting the relationship between CBF and age. Results: Of all 87 brain regions, 68 brain regions were best fitted by cubic models, 9 brain regions were best fitted by quadratic models, and 10 brain regions were best fitted by linear models. In most brain regions (global gray matter and the other 65 brain regions), CBF decreased nonlinearly with aging, and the rate of CBF reduction decreased with aging, gradually approaching 0 after approximately 60. CBF in some regions of the frontal, parietal, and occipital lobes increased nonlinearly with aging before age 30, approximately, and decreased nonlinearly with aging for the rest of life. Conclusion: In adults, the age-related perfusion patterns in most brain regions were best fitted by the cubic models, and age-dependent CBF changes were nonlinear.

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과제정보

We would like to acknowledge the work of our doctoral students in our department who worked many days and nights performing MR imaging scanning on the volunteers. We would also like to thank the volunteers who traveled long distances to our hospital and actively cooperate with our MR scanning.

참고문헌

  1. Biagi L, Abbruzzese A, Bianchi MC, Alsop DC, Del Guerra A, Tosetti M. Age dependence of cerebral perfusion assessed by magnetic resonance continuous arterial spin labeling. J Magn Reson Imaging 2007;25:696-702
  2. 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
  3. Preibisch C, Sorg C, Forschler A, Grimmer T, Sax I, Wohlschlager AM, et al. Age-related cerebral perfusion changes in the parietal and temporal lobes measured by pulsed arterial spin labeling. J Magn Reson Imaging 2011;34:1295-1302
  4. Campbell AM, Beaulieu C. Pulsed arterial spin labeling parameter optimization for an elderly population. J Magn Reson Imaging 2006;23:398-403
  5. Wintermark M, Sesay M, Barbier E, Borbely K, Dillon WP, Eastwood JD, et al. Comparative overview of brain perfusion imaging techniques. Stroke 2005;36:e83-e99
  6. Wang J, Licht DJ, Jahng GH, Liu CS, Rubin JT, Haselgrove J, et al. Pediatric perfusion imaging using pulsed arterial spin labeling. J Magn Reson Imaging 2003;18:404-413
  7. Elias MF, D'Agostino RB, Elias PK, Wolf PA. Neuropsychological test performance, cognitive functioning, blood pressure, and age: the Framingham Heart Study. Exp Aging Res 1995;21:369-391
  8. Farmer ME, Kittner SJ, Abbott RD, Wolz MM, Wolf PA, White LR. Longitudinally measured blood pressure, antihypertensive medication use, and cognitive performance: the Framingham Study. J Clin Epidemiol 1990;43:475-480
  9. Zhang N, Gordon ML, Goldberg TE. Cerebral blood flow measured by arterial spin labeling MRI at resting state in normal aging and Alzheimer's disease. Neurosci Biobehav Rev 2017;72:168-175
  10. Pantano P, Baron JC, Lebrun-Grandie P, Duquesnoy N, Bousser MG, Comar D. Regional cerebral blood flow and oxygen consumption in human aging. Stroke 1984;15:635-641
  11. Martin AJ, Friston KJ, Colebatch JG, Frackowiak RS. Decreases in regional cerebral blood flow with normal aging. J Cereb Blood Flow Metab 1991;11:684-689
  12. 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 Mol Imaging 2014;29:67-75
  13. Devous MD Sr, Stokely EM, Chehabi HH, Bonte FJ. Normal distribution of regional cerebral blood flow measured by dynamic single-photon emission tomography. J Cereb Blood Flow Metab 1986;6:95-104
  14. Kety SS. Human cerebral blood flow and oxygen consumption as related to aging. J Chronic Dis 1956;3:478-486
  15. Parkes LM, Rashid W, Chard DT, Tofts PS. Normal cerebral perfusion measurements using arterial spin labeling: reproducibility, stability, and age and gender effects. Magn Reson Med 2004;51:736-743
  16. Chen JJ, Rosas HD, Salat DH. Age-associated reductions in cerebral blood flow are independent from regional atrophy. Neuroimage 2011;55:468-478
  17. Liu W, Lou X, Ma L. Use of 3D pseudo-continuous arterial spin labeling to characterize sex and age differences in cerebral blood flow. Neuroradiology 2016;58:943-948
  18. Hasan KM, Ali H, Shad MU. Atlas-based and DTI-guided quantification of human brain cerebral blood flow: feasibility, quality assurance, spatial heterogeneity and age effects. Magn Reson Imaging 2013;31:1445-1452
  19. Hurvich CM, Tsai CL. Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika 1991;78:499-509
  20. Wong AM, Liu HL, Tsai ML, Schwartz ES, Yeh CH, Wang HS, et al. Arterial spin-labeling magnetic resonance imaging of brain maturation in early childhood: mathematical model fitting to assess age-dependent change of cerebral blood flow. Magn Reson Imaging 2019;59:114-120
  21. Cavanaugh JE. Unifying the derivations for the Akaike and corrected Akaike information criteria. Stat Probab Lett 1997;33:201-208
  22. Akaike H. A new look at the statistical model identification. IEEE T Automat Contr 1974;19:716-723
  23. Tukey JW. One degree of freedom for non-additivity. Biometrics 1949;5:232-242
  24. Pregibon D. Goodness of link tests for generalized linear models. J R Stat Soc: Series C (Applied Statistics) 1980;29:15-24
  25. Ramsey JB. Tests for specification errors in classical linear least-squares regression analysis. J R Stat Soc: Series B (Methodological) 1969;31:350-371
  26. Ramsey JB, Zarembka P. Specification error tests and alternative functional forms of the aggregate production function. J Am Stat Assoc 1971;66:471-477
  27. Fan AP, Jahanian H, Holdsworth SJ, Zaharchuk G. Comparison of cerebral blood flow measurement with [15O]-water positron emission tomography and arterial spin labeling magnetic resonance imaging: a systematic review. J Cereb Blood Flow Metab 2016;36:842-861
  28. Leenders KL, Perani D, Lammertsma AA, Heather JD, Buckingham P, Healy MJ, et al. Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age. Brain 1990;113:27-47
  29. Yamaguchi T, Kanno I, Uemura K, Shishido F, Inugami A, Ogawa T, et al. Reduction in regional cerebral metabolic rate of oxygen during human aging. Stroke 1986;17:1220-1228
  30. Petersen ET, Mouridsen K, Golay X. The QUASAR reproducibility study, part II: results from a multi-center Arterial Spin Labeling test-retest study. Neuroimage 2010;49:104-113
  31. Liu Y, Zhu X, Feinberg D, Guenther M, Gregori J, Weiner MW, et al. Arterial spin labeling MRI study of age and gender effects on brain perfusion hemodynamics. Magn Reson Med 2012;68:912-922
  32. Buijs PC, Krabbe-Hartkamp MJ, Bakker CJ, de Lange EE, Ramos LM, Breteler MM, et al. Effect of age on cerebral blood flow: measurement with ungated two-dimensional phase-contrast MR angiography in 250 adults. Radiology 1998;209:667-674
  33. Heo S, Prakash RS, Voss MW, Erickson KI, Ouyang C, Sutton BP, et al. Resting hippocampal blood flow, spatial memory and aging. Brain Res 2010;1315:119-127
  34. Shefer VF. Absolute number of neurons and thickness of the cerebral cortex during aging, senile and vascular dementia, and Pick's and Alzheimer's diseases. Neurosci Behav Physiol 1973;6:319-324
  35. Henderson G, Tomlinson BE, Gibson PH. Cell counts in human cerebral cortex in normal adults throughout life using an image analysing computer. J Neurol Sci 1980;46:113-136
  36. Brody H. Organization of the cerebral cortex. III. A study of aging in the human cerebral cortex. J Comp Neurol 1955;102:511-516
  37. 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 2010;21:1426-1434
  38. Alsop DC, Detre JA, Golay X, Gunther M, Hendrikse J, Hernandez-Garcia L, et al. Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magn Reson Med 2015;73:102-116