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CEREBRAL BLOOD FLOW, BLOOD VOLUME AND OXYGEN UTILIZATION: NORMAL VALUES AND EFFECT OF AGE
Abstract
Regional cerebral blood flow (CBF), oxygen extraction ratio (OER), oxygen utilization (CMRO2) and blood volume (CBV) were measured in a group of 34 healthy volunteers (age range 22-82 yrs) using the 15O steady-state inhalation method and positron emission tomography. Between subjects CBF correlated positively with CMRO2, although the interindividual variability of the measured values was large. OER was not dependent on CMRO2, but highly negatively correlated with CBF. CBV correlated positively with CBF. When considering the values of all the regions of interest within a single subject, a strict coupling between CMRO2 and CBF, and between CBF and CBV was found, while OER was constant and independent of CBF and CMRO2. In 'pure' grey and white matter regions CMRO2, CBF and CBV decreased with age approximately 0.50% per year. In other regions the decline was less evident, most likely due to partial volume effects. OER did not change or showed a slight increase with age (maximum in the grey matter region 0.35%/yr). The results suggest diminished neuronal firing or decreased dendritic synaptic density with age.
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“…Tyrrell, 1992; Chugani et al., 1987; Frewen et al., 1991; Himwich et al., 1959; Kennedy and Sokoloff, 1957; Kinnala et al., 1996; Kuhl et al., 1982; Leenders et al., 1990; Pantano et al., 1984; Perlmutter et al., 1987; Petit-Taboué et al., 1998; 50 Cell Metabolism 19, 49–57, January 7, 2014 ª2014…”
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Mehagnoul‐Schipper, D. J., van der Kallen, B. F. W., Colier, W. N. J. M., van der Sluijs, M. C., van Erning, L. J. T. O., Thijssen, H. O. M., Oeseburg, B., Hoefnagels, W. H. L., & Jansen, R. W. M. M. (2002) · Human Brain Mapping · doi:10.1002/hbm.10026 · Cited by 264
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ten Dam, V. H., van den Heuvel, D. M. J., de Craen, A. J. M., Bollen, E. L. E. M., Murray, H. M., Westendorp, R. G. J., Blauw, G. J., & van Buchem, M. A. (2007) · Radiology · doi:10.1148/radiol.2431052111 · Cited by 116
Barnes, S. R., Ng, T. S. C., Montagne, A., Law, M., Zlokovic, B. V., & Jacobs, R. E. (2016) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25793 · Cited by 115
Christen, T., Pannetier, N. A., Ni, W. W., Qiu, D., Moseley, M. E., Schuff, N., & Zaharchuk, G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.052 · Cited by 97
Luckhaus, C., Flüß, M. O., Wittsack, H.-J., Grass-Kapanke, B., Jänner, M., Khalili-Amiri, R., Friedrich, W., Supprian, T., Gaebel, W., Mödder, U., et al. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.11.053 · Cited by 88
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Metadata
- Authors
- LEENDERS, K. L., PERANI, D., LAMMERTSMA, A. A., HEATHER, J. D., BUCKINGHAM, P., JONES, T., HEALY, M. J. R., GIBBS, J. M., WISE, R. J. S., HATAZAWA, J., HEROLD, S., BEANEY, R. P., BROOKS, D. J., SPINKS, T., RHODES, C., & FRACKOWIAK, R. S. J.
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 1990
- Volume
- 113
- Issue
- 1
- Pages
- 27-47
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2470594653
- Cited by
- 1,093
- References
- 0
- FWCI
- 11.99
- Type
- journal article
- Language
- en
- Open access
- closed
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