Quantification of amyloid beta and tau PET without a structural MRI
dc.contributor.author | Landau, Susan M. | |
dc.contributor.author | Ward, Tyler J. | |
dc.contributor.author | Murphy, Alice | |
dc.contributor.author | Iaccarino, Leonardo | |
dc.contributor.author | Harrison, Theresa M. | |
dc.contributor.author | La Joie, Renaud | |
dc.contributor.author | Baker, Suzanne | |
dc.contributor.author | Koeppe, Robert A. | |
dc.contributor.author | Jagust, William J. | |
dc.date.accessioned | 2023-03-03T21:07:55Z | |
dc.date.available | 2024-03-03 16:07:54 | en |
dc.date.available | 2023-03-03T21:07:55Z | |
dc.date.issued | 2023-02 | |
dc.identifier.citation | Landau, Susan M.; Ward, Tyler J.; Murphy, Alice; Iaccarino, Leonardo; Harrison, Theresa M.; La Joie, Renaud; Baker, Suzanne; Koeppe, Robert A.; Jagust, William J. (2023). "Quantification of amyloid beta and tau PET without a structural MRI." Alzheimer’s & Dementia 19(2): 444-455. | |
dc.identifier.issn | 1552-5260 | |
dc.identifier.issn | 1552-5279 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/175874 | |
dc.description.abstract | IntroductionRelying on magnetic resonance imaging (MRI) for quantification of positron emission tomography (PET) images may limit generalizability of the results. We evaluated several MRI-free approaches for amyloid beta (Aβ) and tau PET quantification relative to MRI-dependent quantification cross-sectionally and longitudinally.MethodsWe compared baseline MRI-free and MRI-dependent measurements of Aβ PET ([18F]florbetapir [FBP], N = 1290, [18F]florbetaben [FBB], N = 290) and tau PET ([18F]flortaucipir [FTP], N = 768) images with respect to continuous and dichotomous agreement, effect sizes of Aβ+ impaired versus Aβ– unimpaired groups, and longitudinal standardized uptake value ratio (SUVR) slopes in a subset of individuals.ResultsThe best-performing MRI-free approaches had high continuous and dichotomous agreement with MRI-dependent SUVRs for Aβ PET and temporal flortaucipir (R2 ≥0.95; ± agreement ≥92%) and for Alzheimer’s disease–related effect sizes; agreement was slightly lower for entorhinal flortaucipir and longitudinal slopes.DiscussionThere is no consistent loss of baseline or longitudinal AD-related signal with MRI-free Aβ and tau PET image quantification. | |
dc.publisher | Wiley Periodicals, Inc. | |
dc.subject.other | Alzheimer’s disease | |
dc.subject.other | florbetaben | |
dc.subject.other | florbetapir | |
dc.subject.other | flortaucipir | |
dc.subject.other | amyloid positron emission tomogrraphy | |
dc.title | Quantification of amyloid beta and tau PET without a structural MRI | |
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 | http://deepblue.lib.umich.edu/bitstream/2027.42/175874/1/alz12668.pdf | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/175874/2/alz12668_am.pdf | |
dc.identifier.doi | 10.1002/alz.12668 | |
dc.identifier.source | Alzheimer’s & Dementia | |
dc.identifier.citedreference | Landau SM, Fero A, Baker SL, et al. Measurement of longitudinal beta-amyloid change with 18F-florbetapir PET and standardized uptake value ratios. J Nucl Med. 2015; 56: 567 - 574. | |
dc.identifier.citedreference | Farrell ME, Jiang S, Schultz AP, et al. Defining the lowest threshold for amyloid-PET to predict future cognitive decline and amyloid accumulation. Neurology. 2021; 96: e619 - e631. | |
dc.identifier.citedreference | Mintun MA, Lo AC, Duggan Evans C, et al. Donanemab in early Alzheimer’s disease. N Engl J Med. 2021; 384: 1691 - 1704. | |
dc.identifier.citedreference | Iaccarino L, La Joie R, Koeppe R, et al. rPOP: robust PET-only processing of community acquired heterogeneous amyloid-PET data. Neuroimage. 2022; 246: 118775. | |
dc.identifier.citedreference | Pegueroles J, Montal V, Bejanin A, et al. AMYQ: an index to standardize quantitative amyloid load across PET tracers. Alzheimers Dement. 2021; 17 ( 9 ): 1499 - 1508. | |
dc.identifier.citedreference | Bourgeat P, Villemagne VL, Dore V, et al. Comparison of MR-less PiB SUVR quantification methods. Neurobiol Aging. 2015; 36 (Suppl 1): S159 - S166. | |
dc.identifier.citedreference | Edison P, Carter SF, Rinne JO, et al. Comparison of MRI based and PET template based approaches in the quantitative analysis of amyloid imaging with PIB-PET. Neuroimage. 2013; 70: 423 - 433. | |
dc.identifier.citedreference | Dore V, Bullich S, Rowe CC, et al. Comparison of (18)F-florbetaben quantification results using the standard Centiloid, MR-based, and MR-less CapAIBL((R)) approaches: validation against histopathology. Alzheimers Dement. 2019; 15: 807 - 816. | |
dc.identifier.citedreference | Fleisher AS, Pontecorvo MJ, Devous MD, Sr., et al. Positron emission tomography imaging with [18F]flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes. JAMA Neurol. 2020; 77: 829 - 839. | |
dc.identifier.citedreference | Mormino EC, Smiljic A, Hayenga AO, et al. Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb Cortex. 2011; 21: 2399 - 2407. | |
dc.identifier.citedreference | Scholl M, Lockhart SN, Schonhaut DR, et al. PET imaging of Tau deposition in the aging human brain. Neuron. 2016; 89: 971 - 982. | |
dc.identifier.citedreference | Maass A, Landau S, Baker SL, et al. Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer’s disease. Neuroimage. 2017; 157: 448 - 463. | |
dc.identifier.citedreference | Weiner MW, Harvey D, Hayes J, et al. Effects of traumatic brain injury and posttraumatic stress disorder on development of Alzheimer’s disease in Vietnam Veterans using the Alzheimer’s disease neuroimaging initiative: preliminary report. Alzheimers Dement (N Y). 2017; 3: 177 - 188. | |
dc.identifier.citedreference | Landau SM, Mintun MA, Joshi AD, et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol. 2012; 72: 578 - 586. | |
dc.identifier.citedreference | ADNI database. ida.loni.usc.edu | |
dc.identifier.citedreference | Chen K, Roontiva A, Thiyyagura P, et al. Improved power for characterizing longitudinal amyloid-beta PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. J Nucl Med. 2015; 56: 560 - 566. | |
dc.identifier.citedreference | Desikan RS, Segonne F, Fischl B, 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 - 980. | |
dc.identifier.citedreference | Landau SM, Mintun M, Joshi A, et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol. 2012; 72 ( 4 ): 578 - 586. | |
dc.identifier.citedreference | Royse SK, Minhas DS, Lopresti BJ, et al. Validation of amyloid PET positivity thresholds in centiloids: a multisite PET study approach. Alzheimers Res Ther. 2021; 13: 99. | |
dc.identifier.citedreference | Fleisher AS, Joshi AD, Sundell KL, et al. Use of white matter reference regions for detection of change in florbetapir positron emission tomography from completed phase 3 solanezumab trials. Alzheimers Dement. 2017; 13: 1117 - 1124. | |
dc.identifier.citedreference | Jack CR, Jr., Wiste HJ, Weigand SD, et al. Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement. 2017; 13: 205 - 216. | |
dc.identifier.citedreference | Diedrichsen J. A spatially unbiased atlas template of the human cerebellum. Neuroimage. 2006; 33: 127 - 138. | |
dc.identifier.citedreference | Baker SL, Maass A, Jagust WJ. Considerations and code for partial volume correcting [(18)F]-AV-1451 tau PET data. Data Brief. 2017; 15: 648 - 657. | |
dc.identifier.citedreference | Harrison TM, La Joie R, Maass A, et al. Longitudinal tau accumulation and atrophy in aging and Alzheimer disease. Ann Neurol. 2019; 85: 229 - 240. | |
dc.identifier.citedreference | Schwarz CG, Therneau TM, Weigand SD, et al. Selecting software pipelines for change in flortaucipir SUVR: balancing repeatability and group separation. Neuroimage. 2021; 238: 118259. | |
dc.identifier.citedreference | Klunk WE, Koeppe RA, Price JC, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015; 11: 1 - 15 e1-4. | |
dc.identifier.citedreference | Ishii K, Willoch F, Minoshima S, et al. Statistical brain mapping of 18F-FDG PET in Alzheimer’s disease: validation of anatomic standardization for atrophied brains. J Nucl Med. 2001; 42: 548 - 557. | |
dc.identifier.citedreference | Lilja J, Leuzy A, Chiotis K, Savitcheva I, Sorensen J, Nordberg A. Spatial normalization of (18)F-Flutemetamol PET images using an adaptive principal-component template. J Nucl Med. 2019; 60: 285 - 291. | |
dc.identifier.citedreference | Brendel M, Hogenauer M, Delker A, et al. Improved longitudinal [(18)F]-AV45 amyloid PET by white matter reference and VOI-based partial volume effect correction. Neuroimage. 2015; 108: 450 - 459. | |
dc.working.doi | NO | en |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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