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Pattern of Cerebral Hypoperfusion in Alzheimer Disease and Mild Cognitive Impairment Measured with Arterial Spin-labeling MR Imaging: Initial Experience
Open accessAbstract
PURPOSE: To prospectively determine if pulsed arterial spin-labeling perfusion magnetic resonance (MR) imaging depicts regional cerebral hypoperfusion in subjects with Alzheimer disease (AD) and mild cognitive impairment (MCI), compared with perfusion in cognitively normal (CN) subjects, that is consistent with results of fluorodeoxyglucose (FDG) positron emission tomography (PET) and hexamethylpropyleneamine oxime (HMPAO) single photon emission computed tomography (SPECT) studies of similar populations. MATERIALS AND METHODS: Institutional review board approval and informed consent were obtained. Twenty subjects with AD (13 men, seven women; mean age, 72.9 years), 18 with MCI (nine men, nine women; mean age, 73.3 years), and 23 CN subjects (10 men, 13 women; mean age, 72.9 years) underwent arterial spin-labeling and volumetric T1-weighted structural MR imaging. Perfusion images were coregistered to structural images, corrected for partial volume effects (PVEs) with information from the structural image to determine tissue content of perfusion voxels, and normalized to a study-specific template. Analyses of perfusion differences between groups, with and without corrections for PVEs, were performed on a voxel-by-voxel basis with a one-tailed fixed-effects analysis of covariance model adjusted for age. In addition, tests were performed with and without accounting for global perfusion. RESULTS: The AD group showed significant regional hypoperfusion, compared with the CN group, in the right inferior parietal cortex extending into the bilateral posterior cingulate gyri (P <.001), bilateral superior and middle frontal gyri (P <.001), and left inferior parietal lobe (P=.007). When PVEs from underlying cortical gray matter atrophy were accounted for, the AD group still showed hypoperfusion in the right inferior parietal lobe extending into the bilateral posterior cingulate gyri (P <.001) and left (P=.003) and right (P=.012) middle frontal gyri. With a more liberal voxel-level threshold of P <.01, the MCI group showed significant regional hypoperfusion relative to the CN group in the inferior right parietal lobe (P=.046), similar to the region of greatest significance in the AD group. CONCLUSION: Arterial spin-labeling MR imaging showed regional hypoperfusion with AD, in brain regions similar to those seen in FDG PET and HMPAO SPECT studies of similar populations; this hypoperfusion persists after accounting for underlying cortical gray matter atrophy.
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Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N. A., Friston, K. J., & Frackowiak, R. S. J. (2001) · NeuroImage · doi:10.1006/nimg.2001.0786 · Cited by 4,561
Reiman, E. M., Caselli, R. J., Yun, L. S., Chen, K., Bandy, D., Minoshima, S., Thibodeau, S. N., & Osborne, D. (1996) · New England Journal of Medicine · doi:10.1056/nejm199603213341202 · Cited by 1,366
Preclinical prediction of Alzheimer's disease using SPECT
Johnson, K. A., Jones, K., Holman, B. L., Becker, J. A., Spiers, P. A., Satlin, A., & Albert, M. S. (1998) · Neurology · doi:10.1212/wnl.50.6.1563 · Cited by 471
Neuroimaging and Early Diagnosis of Alzheimer Disease: A Look to the Future
Petrella, J. R., Coleman, R. E., & Doraiswamy, P. M. (2003) · Radiology · doi:10.1148/radiol.2262011600 · Cited by 427
Dissociating atrophy and hypometabolism impact on episodic memory in mild cognitive impairment
Chetelat, G. (2003) · Brain · doi:10.1093/brain/awg196 · Cited by 264
Cited by
Glial and neuronal control of brain blood flow
Attwell, D., Buchan, A. M., Charpak, S., Lauritzen, M., MacVicar, B. A., & Newman, E. A. (2010) · Nature · doi:10.1038/nature09613 · Cited by 2,511
Ross, R., Blair, S. N., Arena, R., Church, T. S., Després, J.-P., Franklin, B. A., Haskell, W. L., Kaminsky, L. A., Levine, B. D., Lavie, C. J., et al. (2016) · Circulation · doi:10.1161/cir.0000000000000461 · Cited by 2,486
The clinical use of structural MRI in Alzheimer disease
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P., & Thompson, P. M. (2010) · Nature Reviews Neurology · doi:10.1038/nrneurol.2009.215 · Cited by 2,113
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2005.06.003 · Cited by 1,233
Self-citationSperling, R. A., LaViolette, P. S., O'Keefe, K., O'Brien, J., Rentz, D. M., Pihlajamaki, M., Marshall, G., Hyman, B. T., Selkoe, D. J., Hedden, T., et al. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.003 · Cited by 1,027
Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis
Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968
“…et al., 2007; Ewers et al., 2012; Lee et al., 2006; Mosconi, 2005; Visser et al., 2002; Walhovd et al., 2010), superior/medial frontal gyrus (Johnson et al., 2005), precentral/postcentral gyrus (Belleville et al., 2011), precuneus (Bokde et al., 2006; Davatzikos et al., 2011; Singh et al.,…”
Review: Cerebral microvascular pathology in ageing and neurodegeneration
Brown, W. R., & Thore, C. R. (2011) · Neuropathology and Applied Neurobiology · doi:10.1111/j.1365-2990.2010.01139.x · Cited by 749
Cerebral amyloid angiopathy and its relationship to Alzheimer’s disease
Thal, D. R., Griffin, W. S. T., de Vos, R. A. I., & Ghebremedhin, E. (2008) · Acta Neuropathologica · doi:10.1007/s00401-008-0366-2 · Cited by 372
Cortes-Canteli, M., Paul, J., Norris, E. H., Bronstein, R., Ahn, H. J., Zamolodchikov, D., Bhuvanendran, S., Fenz, K. M., & Strickland, S. (2010) · Neuron · doi:10.1016/j.neuron.2010.05.014 · Cited by 339
Microglia–blood vessel interactions: a double-edged sword in brain pathologies
Dudvarski Stankovic, N., Teodorczyk, M., Ploen, R., Zipp, F., & Schmidt, M. H. H. (2016) · Acta Neuropathologica · doi:10.1007/s00401-015-1524-y · Cited by 301
Positron emission tomography imaging in dementia
HERHOLZ, K., CARTER, S. F., & JONES, M. (2007) · The British Journal of Radiology · doi:10.1259/bjr/97295129 · Cited by 287
Mechanisms of mitochondrial dysfunction and energy deficiency in Alzheimer’s disease
Atamna, H., & Frey, W. H. (2007) · Mitochondrion · doi:10.1016/j.mito.2007.06.001 · Cited by 278
Najjar, S., Pearlman, D. M., Devinsky, O., Najjar, A., & Zagzag, D. (2013) · Journal of Neuroinflammation · doi:10.1186/1742-2094-10-142 · Cited by 248
Brain imaging of neurovascular dysfunction in Alzheimer’s disease
Montagne, A., Nation, D. A., Pa, J., Sweeney, M. D., Toga, A. W., & Zlokovic, B. V. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1570-0 · Cited by 239
Cerebral Hemodynamics and Vascular Risk Factors: Setting the Stage for Alzheimer's Disease
de la Torre, J. C. (2012) · Journal of Alzheimer's Disease · doi:10.3233/jad-2012-120793 · Cited by 219
The Utility of Cerebral Blood Flow as a Biomarker of Preclinical Alzheimer’s Disease
Hays, C. C., Zlatar, Z. Z., & Wierenga, C. E. (2016) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-015-0261-z · Cited by 213
Asllani, I., Habeck, C., Scarmeas, N., Borogovac, A., Brown, T. R., & Stern, Y. (2008) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/sj.jcbfm.9600570 · Cited by 172
Effects of working memory training on functional connectivity and cerebral blood flow during rest
Takeuchi, H., Taki, Y., Nouchi, R., Hashizume, H., Sekiguchi, A., Kotozaki, Y., Nakagawa, S., Miyauchi, C. M., Sassa, Y., & Kawashima, R. (2013) · Cortex · doi:10.1016/j.cortex.2012.09.007 · Cited by 162
Wolk (2012) · doi:10.1097/wco.0b013e328354ff0a · Cited by 147
Low cerebral blood flow is associated with lower memory function in metabolic syndrome
Birdsill, A. C., Carlsson, C. M., Willette, A. A., Okonkwo, O. C., Johnson, S. C., Xu, G., Oh, J. M., Gallagher, C. L., Koscik, R. L., Jonaitis, E. M., et al. (2013) · Obesity · doi:10.1002/oby.20170 · Cited by 139
Thal, D. R., Capetillo-Zarate, E., Larionov, S., Staufenbiel, M., Zurbruegg, S., & Beckmann, N. (2009) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2008.01.017 · Cited by 138
Thal, D. R., Griffin, W. S. T., & Braak, H. (2008) · Journal of Cellular and Molecular Medicine · doi:10.1111/j.1582-4934.2008.00411.x · Cited by 134
Tartaglia, M. C., Rosen, H. J., & Miller, B. L. (2011) · Neurotherapeutics · doi:10.1007/s13311-010-0012-2 · Cited by 124
Self-citationCerebrospinal Fluid Biomarkers of Neurovascular Dysfunction in Mild Dementia and Alzheimer'S Disease
Sweeney, M. D., Sagare, A. P., & Zlokovic, B. V. (2015) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2015.76 · Cited by 122
Nielsen, R. B., Egefjord, L., Angleys, H., Mouridsen, K., Gejl, M., Møller, A., Brock, B., Brændgaard, H., Gottrup, H., Rungby, J., et al. (2017) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2017.02.007 · Cited by 119
Phosphorylation of Tau at S422 is enhanced by Aβ in TauPS2APP triple transgenic mice
Grueninger, F., Bohrmann, B., Czech, C., Ballard, T. M., Frey, J. R., Weidensteiner, C., von Kienlin, M., & Ozmen, L. (2010) · Neurobiology of Disease · doi:10.1016/j.nbd.2009.09.004 · Cited by 117
Faure, A., Verret, L., Bozon, B., El Tannir El Tayara, N., Ly, M., Kober, F., Dhenain, M., Rampon, C., & Delatour, B. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2009.03.009 · Cited by 112
Type 2 diabetes mellitus, dyslipidemia, and Alzheimer’s disease
Carlsson, C. M. (2010) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-100012 · Cited by 109
Reduction of the cerebrovascular volume in a transgenic mouse model of Alzheimer's disease
Bourasset, F., Ouellet, M., Tremblay, C., Julien, C., Do, T. M., Oddo, S., LaFerla, F., & Calon, F. (2009) · Neuropharmacology · doi:10.1016/j.neuropharm.2009.01.006 · Cited by 106
Toward precision medicine in Alzheimer’s disease
Reitz, C. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.03.05 · Cited by 100
Csiszar, A., Tarantini, S., Fülöp, G. A., Kiss, T., Valcarcel-Ares, M. N., Galvan, V., Ungvari, Z., & Yabluchanskiy, A. (2017) · GeroScience · doi:10.1007/s11357-017-9991-9 · Cited by 93
Samuraki, M., Matsunari, I., Chen, W.-P., Yajima, K., Yanase, D., Fujikawa, A., Takeda, N., Nishimura, S., Matsuda, H., & Yamada, M. (2007) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-007-0454-x · Cited by 92
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
Imaging of cerebrovascular pathology in animal models of Alzheimer's disease
Klohs, J., Rudin, M., Shimshek, D. R., & Beckmann, N. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00032 · Cited by 82
Role of mitochondrial-mediated signaling pathways in Alzheimer disease and hypoxia
Carvalho, C., Correia, S. C., Santos, R. X., Cardoso, S., Moreira, P. I., Clark, T. A., Zhu, X., Smith, M. A., & Perry, G. (2009) · Journal of Bioenergetics and Biomembranes · doi:10.1007/s10863-009-9247-1 · Cited by 72
The Role of Cholesterol Metabolism in Alzheimer’s Disease
Sun, J.-H., Yu, J.-T., & Tan, L. (2015) · Molecular Neurobiology · doi:10.1007/s12035-014-8749-y · Cited by 70
Roher, A. E., Garami, Z., Alexandrov, A. V., Kokjohn, T. A., Esh, C. L., Kalback, W. M., Vedders, L. J., Wilson, J. R., Sabbagh, M. N., & Beach, T. G. (2006) · Neurological Research · doi:10.1179/016164106x130470 · Cited by 56
Weidensteiner, C., Metzger, F., Bohrmann, A. B. B., Kuennecke, B., & von Kienlin, M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21985 · Cited by 56
A human brain vascular atlas reveals diverse cell mediators of Alzheimer’s disease risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Maat, C. A., Losada, P. M., Chen, M. B., Agam, M., Schaum, N., Khoury, N., et al. (2021) · doi:10.1101/2021.04.26.441262 · Cited by 48
Renke, M. B., Marcinkowska, A. B., Kujach, S., & Winklewski, P. J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.803332 · Cited by 50
Dolphin, H., Dukelow, T., Finucane, C., Commins, S., McElwaine, P., & Kennelly, S. P. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.897303 · Cited by 38
Hays, C. C., Zlatar, Z. Z., Campbell, L., Meloy, M. J., & Wierenga, C. E. (2018) · Journal of the International Neuropsychological Society · doi:10.1017/s135561771700087x · Cited by 34
Alzheimer's disease biomarkers: correspondence between human studies and animal models
Sabbagh, J. J., Kinney, J. W., & Cummings, J. L. (2013) · Neurobiology of Disease · doi:10.1016/j.nbd.2013.04.010 · Cited by 27
Metadata
- Authors
- Johnson, N. A., Jahng, G.-H., Weiner, M. W., Miller, B. L., Chui, H. C., Jagust, W. J., Gorno-Tempini, M. L., & Schuff, N.
- Venue
- Radiology
- Published
- 2005-03
- Volume
- 234
- Issue
- 3
- Pages
- 851-859
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2111240150
- Cited by
- 587
- References
- 9
- FWCI
- 14.30
- Type
- journal article
- Language
- en
- Open access
- green
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