Utility of perfusion PET measures to assess neuronal injury in Alzheimer’s disease
dc.contributor.author | Joseph‐mathurin, Nelly | |
dc.contributor.author | Su, Yi | |
dc.contributor.author | Blazey, Tyler M. | |
dc.contributor.author | Jasielec, Mateusz | |
dc.contributor.author | Vlassenko, Andrei | |
dc.contributor.author | Friedrichsen, Karl | |
dc.contributor.author | Gordon, Brian A. | |
dc.contributor.author | Hornbeck, Russ C. | |
dc.contributor.author | Cash, Lisa | |
dc.contributor.author | Ances, Beau M. | |
dc.contributor.author | Veale, Thomas | |
dc.contributor.author | Cash, David M. | |
dc.contributor.author | Brickman, Adam M. | |
dc.contributor.author | Buckles, Virginia | |
dc.contributor.author | Cairns, Nigel J. | |
dc.contributor.author | Cruchaga, Carlos | |
dc.contributor.author | Goate, Alison | |
dc.contributor.author | Jack, Clifford R. | |
dc.contributor.author | Karch, Celeste | |
dc.contributor.author | Klunk, William | |
dc.contributor.author | Koeppe, Robert A. | |
dc.contributor.author | Marcus, Daniel S. | |
dc.contributor.author | Mayeux, Richard | |
dc.contributor.author | McDade, Eric | |
dc.contributor.author | Noble, James M. | |
dc.contributor.author | Ringman, John | |
dc.contributor.author | Saykin, Andrew J. | |
dc.contributor.author | Thompson, Paul M. | |
dc.contributor.author | Xiong, Chengjie | |
dc.contributor.author | Morris, John C. | |
dc.contributor.author | Bateman, Randall J. | |
dc.contributor.author | Benzinger, Tammie L.S. | |
dc.date.accessioned | 2020-01-13T15:21:49Z | |
dc.date.available | 2020-01-13T15:21:49Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Joseph‐mathurin, Nelly ; Su, Yi; Blazey, Tyler M.; Jasielec, Mateusz; Vlassenko, Andrei; Friedrichsen, Karl; Gordon, Brian A.; Hornbeck, Russ C.; Cash, Lisa; Ances, Beau M.; Veale, Thomas; Cash, David M.; Brickman, Adam M.; Buckles, Virginia; Cairns, Nigel J.; Cruchaga, Carlos; Goate, Alison; Jack, Clifford R.; Karch, Celeste; Klunk, William; Koeppe, Robert A.; Marcus, Daniel S.; Mayeux, Richard; McDade, Eric; Noble, James M.; Ringman, John; Saykin, Andrew J.; Thompson, Paul M.; Xiong, Chengjie; Morris, John C.; Bateman, Randall J.; Benzinger, Tammie L.S. (2018). "Utility of perfusion PET measures to assess neuronal injury in Alzheimer’s disease." Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring 10(C): 669-677. | |
dc.identifier.issn | 2352-8729 | |
dc.identifier.issn | 2352-8729 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/153272 | |
dc.description.abstract | Introduction18Fâ fluorodeoxyglucose (FDG) positron emission tomography (PET) is commonly used to estimate neuronal injury in Alzheimer’s disease (AD). Here, we evaluate the utility of dynamic PET measures of perfusion using 11Câ Pittsburgh compound B (PiB) to estimate neuronal injury in comparison to FDG PET.MethodsFDG, early frames of PiB images, and relative PiB delivery rate constants (PiBâ R1) were obtained from 110 participants from the Dominantly Inherited Alzheimer Network. Voxelwise, regional crossâ sectional, and longitudinal analyses were done to evaluate the correlation between images and estimate the relationship of the imaging biomarkers with estimated time to disease progression based on family history.ResultsMetabolism and perfusion images were spatially correlated. Regional PiBâ R1 values and FDG, but not early frames of PiB images, significantly decreased in the mutation carriers with estimated year to onset and with increasing dementia severity.DiscussionHypometabolism estimated by PiBâ R1 may provide a measure of brain perfusion without increasing radiation exposure. | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | PiB | |
dc.subject.other | Neuronal injury | |
dc.subject.other | Alzheimer’s disease | |
dc.subject.other | FDG | |
dc.subject.other | Perfusion | |
dc.title | Utility of perfusion PET measures to assess neuronal injury in Alzheimer’s disease | |
dc.type | Article | |
dc.rights.robots | IndexNoFollow | |
dc.subject.hlbsecondlevel | Neurology and Neurosciences | |
dc.subject.hlbtoplevel | Health Sciences | |
dc.description.peerreviewed | Peer Reviewed | |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/153272/1/dad2jdadm201808012.pdf | |
dc.identifier.doi | 10.1016/j.dadm.2018.08.012 | |
dc.identifier.source | Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring | |
dc.identifier.citedreference | R.S. Frackowiak, G.L. Lenzi, T. Jones, J.D. Heather. Quantitative measurement of regional cerebral blood flow and oxygen metabolism in man using 15O and positron emission tomography: Theory, procedure, and normal values. J Comput Assist Tomogr. 4: 1980; 727 â 736 | |
dc.identifier.citedreference | J.C. Morris, P.S. Aisen, R.J. Bateman, T.L. Benzinger, N.J. Cairns, A.M. Fagan, et al. Developing an international network for Alzheimer research: The Dominantly Inherited Alzheimer Network. Clin Investig. 2: 2012; 975 â 984 | |
dc.identifier.citedreference | K.J. Lin, I.T. Hsiao, J.L. Hsu, C.C. Huang, K.L. Huang, C.J. Hsieh, et al. Imaging characteristic of dualâ phase (18)Fâ florbetapir (AVâ 45/Amyvid) PET for the concomitant detection of perfusion deficits and betaâ amyloid deposition in Alzheimer’s disease and mild cognitive impairment. Eur J Nucl Med Mol Imaging. 43: 2016; 1304 â 1314 | |
dc.identifier.citedreference | C.R. Jack Jr., D.S. Knopman, W.J. Jagust, R.C. Petersen, M.W. Weiner, P.S. Aisen, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12: 2013; 207 â 216 | |
dc.identifier.citedreference | R.J. Bateman, C. Xiong, T.L. Benzinger, A.M. Fagan, A. Goate, N.C. Fox, et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 367: 2012; 795 â 804 | |
dc.identifier.citedreference | A.M. Fagan, D.M. Holtzman. Cerebrospinal fluid biomarkers of Alzheimer’s disease. Biomark Med. 4: 2010; 51 â 63 | |
dc.identifier.citedreference | W.E. Klunk, H. Engler, A. Nordberg, Y. Wang, G. Blomqvist, D.P. Holt, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compoundâ B. Ann Neurol. 55: 2004; 306 â 319 | |
dc.identifier.citedreference | C.C. Rowe, S. Ng, U. Ackermann, S.J. Gong, K. Pike, G. Savage, et al. Imaging betaâ amyloid burden in aging and dementia. Neurology. 68: 2007; 1718 â 1725 | |
dc.identifier.citedreference | L. Mosconi, V. Berti, L. Glodzik, A. Pupi, S. De Santi, M.J. de Leon. Preâ clinical detection of Alzheimer’s disease using FDGâ PET, with or without amyloid imaging. J Alzheimers Dis. 20: 2010; 843 â 854 | |
dc.identifier.citedreference | C.R. Jack Jr., D.S. Knopman, W.J. Jagust, L.M. Shaw, P.S. Aisen, M.W. Weiner, et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9: 2010; 119 â 128 | |
dc.identifier.citedreference | A. Forsberg, H. Engler, G. Blomquist, B. Langstrom, A. Nordberg. The use of PIBâ PET as a dual pathological and functional biomarker in AD. Biochim Biophys Acta. 1822: 2012; 380 â 385 | |
dc.identifier.citedreference | P.T. Meyer, S. Hellwig, F. Amtage, C. Rottenburger, U. Sahm, P. Reuland, et al. Dualâ biomarker imaging of regional cerebral amyloid load and neuronal activity in dementia with PET and 11Câ labeled Pittsburgh compound B. J Nucl Med. 52: 2011; 393 â 400 | |
dc.identifier.citedreference | A.H. Rostomian, C. Madison, G.D. Rabinovici, W.J. Jagust. Early 11Câ PIB frames and 18Fâ FDG PET measures are comparable: A study validated in a cohort of AD and FTLD patients. J Nucl Med. 52: 2011; 173 â 179 | |
dc.identifier.citedreference | S. Tiepolt, S. Hesse, M. Patt, J. Luthardt, M.L. Schroeter, K.T. Hoffmann, et al. Early [(18)F]florbetaben and [(11)C]PiB PET images are a surrogate biomarker of neuronal injury in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 43: 2016; 1700 â 1709 | |
dc.identifier.citedreference | L. Fu, L. Liu, J. Zhang, B. Xu, Y. Fan, J. Tian. Comparison of dualâ biomarker PIBâ PET and dualâ tracer PET in AD diagnosis. Eur Radiol. 24: 2014; 2800 â 2809 | |
dc.identifier.citedreference | M.E. Raichle, W.R. Martin, P. Herscovitch, M.A. Mintun, J. Markham. Brain blood flow measured with intravenous H2(15)O. II. Implementation and validation. J Nucl Med. 24: 1983; 790 â 798 | |
dc.identifier.citedreference | T.L. Benzinger, T. Blazey, C.R. Jack Jr., R.A. Koeppe, Y. Su, C. Xiong, et al. Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc Natl Acad Sci U S A. 110: 2013; E4502 â E4509 | |
dc.identifier.citedreference | Y.J. Chen, B.L. Rosario, W. Mowrey, C.M. Laymon, X. Lu, O.L. Lopez, et al. Relative 11Câ PiB Delivery as a Proxy of Relative CBF: Quantitative Evaluation Using Singleâ Session 15Oâ Water and 11Câ PiB PET. J Nucl Med. 56: 2015; 1199 â 1205 | |
dc.identifier.citedreference | G. Blomquist, H. Engler, A. Nordberg, A. Ringheim, A. Wall, A. Forsberg, et al. Unidirectional Influx and Net Accumulation of PIB. Open Neuroimag J. 2: 2008; 114 â 125 | |
dc.identifier.citedreference | E. Rodriguezâ Vieitez, A. Leuzy, K. Chiotis, L. Saintâ Aubert, A. Wall, A. Nordberg. Comparability of [18F]THK5317 and [11C]PIB blood flow proxy images with [18F]FDG positron emission tomography in Alzheimer’s disease. J Cereb Blood flow Metab. 37: 2016; 740 â 749 | |
dc.identifier.citedreference | L. Liu, L. Fu, X. Zhang, J. Zhang, X. Zhang, B. Xu, et al. Combination of dynamic (11)Câ PIB PET and structural MRI improves diagnosis of Alzheimer’s disease. Psychiatry Res. 233: 2015; 131 â 140 | |
dc.identifier.citedreference | K. Farid, Y.T. Hong, F.I. Aigbirhio, T.D. Fryer, D.K. Menon, E.A. Warburton, et al. Earlyâ Phase 11Câ PiB PET in amyloid angiopathyâ related symptomatic cerebral hemorrhage: Potential diagnostic value?. PLoS One. 10: 2015; e0139926 | |
dc.identifier.citedreference | A.F. Gietl, G. Warnock, F. Riese, A.M. Kalin, A. Saake, E. Gruber, et al. Regional cerebral blood flow estimated by early PiB uptake is reduced in mild cognitive impairment and associated with age in an amyloidâ dependent manner. Neurobiol Aging. 36: 2015; 1619 â 1628 | |
dc.identifier.citedreference | R.J. Bateman, P.S. Aisen, B. De Strooper, N.C. Fox, C.A. Lemere, J.M. Ringman, et al. Autosomalâ dominant Alzheimer’s disease: A review and proposal for the prevention of Alzheimer’s disease. Alzheimers Res Ther. 3: 2011; 1 | |
dc.identifier.citedreference | H.C. Kuo, I.T. Hsiao, C.J. Hsieh, C.Y. Huang, K.L. Huang, Y.Y. Wai, et al. Dualâ phase 18Fâ florbetapir positron emission tomography in patients with primary progressive aphasia, Alzheimer’s disease, and healthy controls: A preliminary study. J Formos Med Assoc. 116: 2017; 964 â 972 | |
dc.identifier.citedreference | S. Daerr, M. Brendel, C. Zach, E. Mille, D. Schilling, M.J. Zacherl, et al. Evaluation of earlyâ phase [18F]â florbetaben PET acquisition in clinical routine cases. NeuroImage Clin. 14: 2017; 77 â 86 | |
dc.identifier.citedreference | I.T. Hsiao, C.C. Huang, C.J. Hsieh, W.C. Hsu, S.P. Wey, T.C. Yen, et al. Correlation of earlyâ phase 18Fâ florbetapir (AVâ 45/Amyvid) PET images to FDG images: preliminary studies. Eur J Nucl Med Mol Imaging. 39: 2012; 613 â 620 | |
dc.identifier.citedreference | R.C. Gur, J.D. Ragland, M. Reivich, J.H. Greenberg, A. Alavi, R.E. Gur. Regional differences in the coupling between resting cerebral blood flow and metabolism may indicate action preparedness as a default state. Cereb Cortex. 19: 2009; 375 â 382 | |
dc.identifier.citedreference | O.B. Paulson, S.G. Hasselbalch, E. Rostrup, G.M. Knudsen, D. Pelligrino. Cerebral blood flow response to functional activation. J Cereb Blood flow Metab. 30: 2010; 2 â 14 | |
dc.identifier.citedreference | D.N. Greve, D.H. Salat, S.L. Bowen, D. Izquierdoâ Garcia, A.P. Schultz, C. Catana, et al. Different partial volume correction methods lead to different conclusions: An (18)Fâ FDGâ PET study of aging. NeuroImage. 132: 2016; 334 â 343 | |
dc.identifier.citedreference | B.A. Thomas, K. Erlandsson, M. Modat, L. Thurfjell, R. Vandenberghe, S. Ourselin, et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 38: 2011; 1104 â 1119 | |
dc.identifier.citedreference | M. Brendel, M. Hogenauer, A. Delker, J. Sauerbeck, P. Bartenstein, J. Seibyl, et al. Improved longitudinal [(18)F]â AV45 amyloid PET by white matter reference and VOIâ based partial volume effect correction. NeuroImage. 108: 2015; 450 â 459 | |
dc.identifier.citedreference | D.M. Cash, G.R. Ridgway, Y. Liang, N.S. Ryan, K.M. Kinnunen, T. Yeatman, et al. The pattern of atrophy in familial Alzheimer disease: Volumetric MRI results from the DIAN study. Neurology. 81: 2013; 1425 â 1433 | |
dc.identifier.citedreference | C.C. Meltzer, J.K. Zubieta, J. Brandt, L.E. Tune, H.S. Mayberg, J.J. Frost. Regional hypometabolism in Alzheimer’s disease as measured by positron emission tomography after correction for effects of partial volume averaging. Neurology. 47: 1996; 454 â 461 | |
dc.identifier.citedreference | V. Ibanez, P. Pietrini, G.E. Alexander, M.L. Furey, D. Teichberg, J.C. Rajapakse, et al. Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer’s disease. Neurology. 50: 1998; 1585 â 1593 | |
dc.identifier.citedreference | F.E. Satterthwaite. An approximate distribution of estimates of variance components. Biometrics. 2: 1946; 110 â 114 | |
dc.identifier.citedreference | D. Rindskopf. An introduction to the bootstrap. Efron B, Tibshirani RJ, eds. J Educ Behav Stat.. 22: 1997; 245 | |
dc.identifier.citedreference | J. Ashburner, K.J. Friston. Diffeomorphic registration using geodesic shooting and Gaussâ Newton optimisation. NeuroImage. 55: 2011; 954 â 967 | |
dc.identifier.citedreference | J. Ashburner, K.J. Friston. Unified segmentation. NeuroImage. 26: 2005; 839 â 851 | |
dc.identifier.citedreference | B.J. Lopresti, W.E. Klunk, C.A. Mathis, J.A. Hoge, S.K. Ziolko, X. Lu, et al. Simplified quantification of Pittsburgh Compound B amyloid imaging PET studies: A comparative analysis. J Nucl Med. 46: 2005; 1959 â 1972 | |
dc.identifier.citedreference | A.A. Lammertsma, S.P. Hume. Simplified reference tissue model for PET receptor studies. NeuroImage. 4: 1996; 153 â 158 | |
dc.identifier.citedreference | S.N. Vaishnavi, A.G. Vlassenko, M.M. Rundle, A.Z. Snyder, M.A. Mintun, M.E. Raichle. Regional aerobic glycolysis in the human brain. Proc Natl Acad Sci U S A. 107: 2010; 17757 â 17762 | |
dc.identifier.citedreference | J.C. Morris, C.M. Roe, C. Xiong, A.M. Fagan, A.M. Goate, D.M. Holtzman, et al. APOE predicts amyloidâ beta but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol. 67: 2010; 122 â 131 | |
dc.identifier.citedreference | Y. Su, T.M. Blazey, A.Z. Snyder, M.E. Raichle, D.S. Marcus, B.M. Ances, et al. Partial volume correction in quantitative amyloid imaging. NeuroImage. 107: 2015; 55 â 64 | |
dc.identifier.citedreference | J.C. Morris. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology. 43: 1993; 2412 â 2414 | |
dc.identifier.citedreference | L. Berg, J.P. Miller, M. Storandt, J. Duchek, J.C. Morris, E.H. Rubin, et al. Mild senile dementia of the Alzheimer type: 2. Longitudinal assessment. Ann Neurol. 23: 1988; 477 â 484 | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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