amyloid; molecular connectome; positron emission tomography; tau; transcriptome; tau Proteins; Amyloid beta-Peptides; Humans; Positron-Emission Tomography; Male; Female; Aged; tau Proteins/metabolism; Amyloid beta-Peptides/metabolism; Disease Progression; Aged, 80 and over; Longitudinal Studies; Alzheimer Disease/diagnostic imaging; Alzheimer Disease/metabolism; Alzheimer Disease/genetics; Alzheimer Disease/pathology; Connectome/methods; Brain/diagnostic imaging; Brain/metabolism; Alzheimer Disease; Brain; Connectome; Epidemiology; Health Policy; Developmental Neuroscience; Neurology (clinical); Geriatrics and Gerontology; Cellular and Molecular Neuroscience; Psychiatry and Mental Health
Abstract :
[en] [en] INTRODUCTION: Mapping individual differences is crucial to improve personalized medicine approaches in Alzheimer's disease (AD), which is characterized by strong inter-individual variability in the accumulation patterns of tau and amyloid beta pathology.
METHODS: We assess the progression of AD across the disease continuum by building individual molecular connectomes using longitudinal positron emission tomography (PET) data.
RESULTS: We demonstrate that these connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects. Alterations in the connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional PET measures. We introduce a novel gene-specific transcription network analysis that linked individual tau and amyloid connectomes to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.
DISCUSSION: Individual molecular connectome mapping provides a novel and sensitive framework to monitor AD progression.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Xu, Zhilei ; Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Solna, Sweden
Mijalkov, Mite; Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Solna, Sweden
Sun, Jiawei; Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Solna, Sweden
Chang, Yu-Wei; Department of Physics, University of Gothenburg, Gothenburg, Sweden
Sala, Arianna ; Université de Liège - ULiège > Département des sciences cliniques
Volpe, Giovanni; Department of Physics, University of Gothenburg, Gothenburg, Sweden
Severino, Mario; Department of Information Engineering, University of Padua, Padua, Italy
Veronese, Mattia; Department of Information Engineering, University of Padua, Padua, Italy ; Department of Neuroimaging, King's College London, Strand, London, UK
Garcia-Ptacek, Sara; Department of Neurobiology, Division of Clinical Geriatrics, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden ; Theme Inflammation and Aging, Karolinska University Hospital, Solna, Sweden
Pereira, Joana B; Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Solna, Sweden
Alzheimer's Disease Neuroimaging Initiative
Language :
English
Title :
Mapping individual molecular connectomes in Alzheimer's disease.
Publication date :
March 2026
Journal title :
Alzheimer's and Dementia: the Journal of the Alzheimer's Association
USDOD - United States Department of Defense Gun och Bertil Stohnes Stiftelse Sverige Vetenskapsrådet Hjärnfonden Stiftelsen Lars Hiertas Minne
Funding text :
Data collection and sharing for this project was funded by ADNI and DOD ADNI. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The HABS study was launched in 2010, funded by the National Institute on Aging, and is led by principal investigators Reisa A. Sperling MD and Keith A. Johnson MD at Massachusetts General Hospital/Harvard Medical School in Boston, MA. The computations of this article were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) in projects sens2023024 and NAISS 2023/22-769. This research was supported by several funding bodies, including the KI Research Incubator, Swedish Research Council (grant no. 2022-01108, 2025-03210), the Alzheimer Foundation (grant no. AF-1032782), Blomqvist Foundation (grant no. 2-3980/2025), the Swedish Brain Foundation (grant no. FO2025-0059), and the Romanian Government through Romania's National Recovery and Resilience Plan (contract no. 760250/28.12.2023, code PNRR-C9-I8-CF109/31.07.2023), administered by the Romanian Ministry of Research, Innovation and Digitalization under Component 9, Investment 8. Further funding was received from StratNeuro, KI Consolidator Grant, KID funding, the King Gustaf V and Queen Victoria's Foundation, Gamla Tj\u00E4narinnor, Gun och Bertil Stohnes Stiftelse, Dementia Foundation and the Lars Hierta Memorial Foundation.Data collection and sharing for this project was funded by ADNI and DOD ADNI. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie; Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol\u2010Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann\u2010La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC; Johnson & Johnson Pharmaceutical Research & Development LLC; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The HABS study was launched in 2010, funded by the National Institute on Aging, and is led by principal investigators Reisa A. Sperling MD and Keith A. Johnson MD at Massachusetts General Hospital/Harvard Medical School in Boston, MA. The computations of this article were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) in projects sens2023024 and NAISS 2023/22\u2010769. This research was supported by several funding bodies, including the KI Research Incubator, Swedish Research Council (grant no. 2022\u201001108, 2025\u201003210), the Alzheimer Foundation (grant no. AF\u20101032782), Blomqvist Foundation (grant no. 2\u20103980/2025), the Swedish Brain Foundation (grant no. FO2025\u20100059), and the Romanian Government through Romania's National Recovery and Resilience Plan (contract no. 760250/28.12.2023, code PNRR\u2010C9\u2010I8\u2010CF109/31.07.2023), administered by the Romanian Ministry of Research, Innovation and Digitalization under Component 9, Investment 8. Further funding was received from StratNeuro, KI Consolidator Grant, KID funding, the King Gustaf V and Queen Victoria's Foundation, Gamla Tj\u00E4narinnor, Gun och Bertil Stohnes Stiftelse, Dementia Foundation and the Lars Hierta Memorial Foundation.
Elahi FM, Miller BL. A clinicopathological approach to the diagnosis of dementia. Nat Rev Neurol. 2017;13:457-476. doi:10.1038/nrneurol.2017.96
Braak H, Braak E. Staging of alzheimer's disease-related neurofibrillary changes. Neurobiol Aging. 1995;16:271-278. doi:10.1016/0197-4580(95)00021-6
Wong DF, Rosenberg PB, Zhou Y, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand18 F-AV-45 (Flobetapir F 18). J Nucl Med. 2010;51:913-920. doi:10.2967/jnumed.109.069088
Xia C, Arteaga J, Chen G, et al. [18 F]T807, a novel tau positron emission tomography imaging agent for Alzheimer's disease. Alzheimers Dement. 2013;9:666-676. doi:10.1016/j.jalz.2012.11.008
Clark CM, Pontecorvo MJ, Beach TG, et al. Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study. Lancet Neurol. 2012;11:669-678. doi:10.1016/S1474-4422(12)70142-4
Murray ME, Lowe VJ, Graff-Radford NR, et al. Clinicopathologic and11 C-Pittsburgh compound B implications of Thal amyloid phase across the Alzheimer's disease spectrum. Brain. 2015;138:1370-1381. doi:10.1093/brain/awv050
Fleisher AS, Pontecorvo MJ, Devous MD, et al. Positron emission tomography imaging with [18 F]flortaucipir and postmortem assessment of Alzheimer disease neuropathologic changes. JAMA Neurol. 2020;77:829. doi:10.1001/jamaneurol.2020.0528
Cho H, Choi JY, Hwang MS, et al. In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Ann Neurol. 2016;80:247-258. doi:10.1002/ana.24711
Leuzy A, Binette AP, Vogel JW, et al. Comparison of group-level and individualized brain regions for measuring change in longitudinal tau positron emission tomography in Alzheimer disease. JAMA Neurol. 2023;80:614. doi:10.1001/jamaneurol.2023.1067
Grothe MJ, Barthel H, Sepulcre J, et al. In vivo staging of regional amyloid deposition. Neurology. 2017;89:2031-2038. doi:10.1212/WNL.0000000000004643
Chiotis K, Saint-Aubert L, Boccardi M, et al. Clinical validity of increased cortical uptake of amyloid ligands on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase development framework. Neurobiol Aging. 2017;52:214-227. doi:10.1016/j.neurobiolaging.2016.07.012
Wolters EE, Dodich A, Boccardi M, et al. Clinical validity of increased cortical uptake of [18F]flortaucipir on PET as a biomarker for Alzheimer's disease in the context of a structured 5-phase biomarker development framework. Eur J Nucl Med Mol Imaging. 2021;48:2097-2109. doi:10.1007/s00259-020-05118-w
Chételat G, Arbizu J, Barthel H, et al. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer's disease and other dementias. Lancet Neurol. 2020;19:951-962. doi:10.1016/S1474-4422(20)30314-8
Ossenkoppele R, Van Der Kant R, Hansson O. Tau biomarkers in Alzheimer's disease: towards implementation in clinical practice and trials. Lancet Neurol. 2022;21:726-734. doi:10.1016/S1474-4422(22)00168-5
Jellinger KA. Recent update on the heterogeneity of the Alzheimer's disease spectrum. J Neural Transm. 2022;129:1-24. doi:10.1007/s00702-021-02449-2
Sepulcre J, Sabuncu MR, Becker A, Sperling R, Johnson KA. In vivo characterization of the early states of the amyloid-beta network. Brain. 2013;136:2239-2252. doi:10.1093/brain/awt146
Diez I, Ortiz-Terán L, Ng TSC, et al. Tau propagation in the brain olfactory circuits is associated with smell perception changes in aging. Nat Commun. 2024;15:4809. doi:10.1038/s41467-024-48462-3
Therriault J, Zimmer ER, Benedet AL, Pascoal TA, Gauthier S, Rosa-Neto P. Staging of Alzheimer's disease: past, present, and future perspectives. Trends Mol Med. 2022;28:726-741. doi:10.1016/j.molmed.2022.05.008
Whitwell JL, Graff-Radford J, Tosakulwong N, et al. [18 F]AV-1451 clustering of entorhinal and cortical uptake in Alzheimer's disease. Ann Neurol. 2018;83:248-257. doi:10.1002/ana.25142
the Alzheimer's Disease Neuroimaging Initiative, Vogel JW, Young AL, Oxtoby NP, et al. Four distinct trajectories of tau deposition identified in Alzheimer's disease. Nat Med. 2021;27:871-881. doi:10.1038/s41591-021-01309-6
Vogel JW, Hansson O. Subtypes of Alzheimer's disease: questions, controversy, and meaning. Trends Neurosci. 2022;45:342-345. doi:10.1016/j.tins.2022.02.001
Armstrong RA. β-amyloid (Aβ) deposition in cognitively normal brain, dementia with Lewy bodies, and Alzheimer's disease: a study using principal components analysis. Folia Neuropathol. 2012;50(2):130-139. n.d.
Franzmeier N, Dewenter A, Frontzkowski L, et al. Patient-centered connectivity-based prediction of tau pathology spread in Alzheimer's disease. Sci Adv. 2020;6:eabd1327. doi:10.1126/sciadv.abd1327
Thompson E, Schroder A, He T, et al. Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease. Imaging Neurosci. 2024;2:imag-2-00089. doi:10.1162/imag_a_00089
Sala A, Lizarraga A, Caminiti SP, et al. Brain connectomics: time for a molecular imaging perspective? Trends Cogn Sci. 2023;27:353-366. doi:10.1016/j.tics.2022.11.015
Vogel JW, Corriveau-Lecavalier N, Franzmeier N, et al. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci. 2023;24:620-639. doi:10.1038/s41583-023-00731-8
Desikan RS, Ségonne 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. doi:10.1016/j.neuroimage.2006.01.021
Landau SM, Fero A, Baker SL, et al. Measurement of longitudinal β-amyloid change with18 F-florbetapir PET and standardized uptake value ratios. J Nucl Med. 2015;56:567-574. doi:10.2967/jnumed.114.148981
Landau SM, Horng A, Fero A, et al. Amyloid negativity in patients with clinically diagnosed Alzheimer disease and MCI. Neurology. 2016;86:1377-1385. doi:10.1212/WNL.0000000000002576
Dagley A, LaPoint M, Huijbers W, et al. Harvard aging brain study: dataset and accessibility. NeuroImage. 2017;144:255-258. doi:10.1016/j.neuroimage.2015.03.069
Liu X, Wang Y, Ji H, Aihara K, Chen L. Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res. 2016;44:e164. doi:10.1093/nar/gkw772
Ding J, Shen C, Wang Z, Yang Y, El Fakhri G, Lu J, et al. Tau-PET abnormality as a biomarker for Alzheimer's disease staging and early detection: a topological perspective. Cereb Cortex N Y N 1991 2023;33:10649-10659. doi:10.1093/cercor/bhad312
Huang S-Y, Hsu J-L, Lin K-J, Hsiao I-T. A novel individual metabolic brain network for 18F-FDG PET imaging. Front Neurosci. 2020;14:344. doi:10.3389/fnins.2020.00344
Finn ES, Shen X, Scheinost D, et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18:1664-1671. doi:10.1038/nn.4135
Van De Ville D, Farouj Y, Preti MG, Liégeois R, Amico E. When makes you unique: temporality of the human brain fingerprint. Sci Adv. 2021;7:eabj0751. doi:10.1126/sciadv.abj0751
Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489:391-399. doi:10.1038/nature11405
Arnatkevic̆iūtė A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage. 2019;189:353-367. doi:10.1016/j.neuroimage.2019.01.011
Reimand J, Isserlin R, Voisin V, et al. Pathway enrichment analysis and visualization of omics data using g:profiler, GSEA, cytoscape and enrichmentmap. Nat Protoc. 2019;14:482-517. doi:10.1038/s41596-018-0103-9
Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min, ACM; 2016:785-794. doi:10.1145/2939672.2939785
Xu Z, Xia M, Wang X, Liao X, Zhao T, He Y. Meta-connectomic analysis maps consistent, reproducible, and transcriptionally relevant functional connectome hubs in the human brain. Commun Biol. 2022;5:1-17. doi:10.1038/s42003-022-04028-x
Mijalkov M, Kakaei E, Pereira JB, Westman E, Volpe G, Initiative for the ADN. BRAPH: a graph theory software for the analysis of brain connectivity. PLOS ONE. 2017;12:e0178798. doi:10.1371/journal.pone.0178798
Chang Y-W, Zufiria-Gerbolés B, Gómez-Ruiz E, et al. BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences 2025:2025.04.11.648455. doi:10.1101/2025.04.11.648455
Jack CR, Wiste HJ, Lesnick TG, et al. Brain β-amyloid load approaches a plateau. Neurology. 2013;80:890-896. doi:10.1212/WNL.0b013e3182840bbe
Cho H, Choi JY, Lee HS, et al. Progressive tau accumulation in alzheimer disease: 2-year follow-up study. J Nucl Med Off Publ Soc Nucl Med. 2019;60:1611-1621. doi:10.2967/jnumed.118.221697
Jack Jr. CR, Andrews JS, Beach TG, et al. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's association workgroup. Alzheimers Dement. 2024;20:5143-5169. doi:10.1002/alz.13859
Hammers A, Allom R, Koepp MJ, et al. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp. 2003;19:224-247. doi:10.1002/hbm.10123
Rolls ET, Joliot M, Tzourio-Mazoyer N. Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. NeuroImage. 2015;122:1-5. doi:10.1016/j.neuroimage.2015.07.075
Gatz M, Reynolds CA, Fratiglioni L, et al. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry. 2006;63:168. doi:10.1001/archpsyc.63.2.168
Xie T, He Y. Mapping the Alzheimer's brain with connectomics. Front Psychiatry. 2012;2:77. doi:10.3389/fpsyt.2011.00077
Grothe MJ, Teipel SJ, for the Alzheimer's Disease Neuroimaging Initiative. Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks: network-Specificity of AD Pathology. Hum Brain Mapp. 2016;37:35-53. doi:10.1002/hbm.23018
Sepulcre J, Schultz AP, Sabuncu M, et al. In vivo tau, amyloid, and gray matter profiles in the aging brain. J Neurosci. 2016;36:7364-7374. doi:10.1523/JNEUROSCI.0639-16.2016
Yang F, Chowdhury SR, Jacobs HIL, et al. Longitudinal predictive modeling of tau progression along the structural connectome. NeuroImage. 2021;237:118126. doi:10.1016/j.neuroimage.2021.118126
Salloway S, Sperling R, Fox NC, et al. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease. N Engl J Med. 2014;370:322-333. doi:10.1056/NEJMoa1304839
Bateman RJ, Smith J, Donohue MC, et al. Two phase 3 trials of gantenerumab in early Alzheimer's disease. N Engl J Med. 2023;389:1862-1876. doi:10.1056/NEJMoa2304430
Fornito A, Arnatkevičiūtė A, Fulcher BD. Bridging the gap between connectome and transcriptome. Trends Cogn Sci. 2019;23:34-50. doi:10.1016/j.tics.2018.10.005
Richiardi J, Altmann A, Milazzo A-C, et al. Correlated gene expression supports synchronous activity in brain networks. Science. 2015;348:1241-1244. doi:10.1126/science.1255905
Shimohama S. Apoptosis in Alzheimer's disease – an update n.d.
Sharma VK, Singh TG, Singh S, Garg N, Dhiman S. Apoptotic pathways and Alzheimer's disease: probing therapeutic potential. Neurochem Res. 2021;46:3103-3122. doi:10.1007/s11064-021-03418-7
Feng M, Hou T, Zhou M, et al. Gut microbiota may be involved in Alzheimer's disease pathology by dysregulating pyrimidine metabolism in APP/PS1 mice. Front Aging Neurosci. 2022;14:967747. doi:10.3389/fnagi.2022.967747
Schueller E, Paiva I, Blanc F, et al. Dysregulation of histone acetylation pathways in hippocampus and frontal cortex of Alzheimer's disease patients. Eur Neuropsychopharmacol. 2020;33:101-116. doi:10.1016/j.euroneuro.2020.01.015
Garavito MF, Narváez-Ortiz HY, Zimmermann BH. Pyrimidine metabolism: dynamic and versatile pathways in pathogens and cellular development. J Genet Genomics. 2015;42:195-205. doi:10.1016/j.jgg.2015.04.004
Mao P, Reddy PH. Aging and amyloid beta-induced oxidative DNA damage and mitochondrial dysfunction in Alzheimer's disease: implications for early intervention and therapeutics. Biochim Biophys Acta BBA—Mol Basis Dis. 2011;1812:1359-1370. doi:10.1016/j.bbadis.2011.08.005
Swerdlow RH, Burns JM, Khan SM. The Alzheimer's disease mitochondrial cascade hypothesis: progress and perspectives. Biochim Biophys Acta BBA—Mol Basis Dis. 2014;1842:1219-1231. doi:10.1016/j.bbadis.2013.09.010
Pesini A, Iglesias E, Garrido N, Bayona-Bafaluy MP, Montoya J, Ruiz-Pesini E. OXPHOS, pyrimidine nucleotides, and alzheimer's disease: a pharmacogenomics approach. J Alzheimers Dis. 2014;42:87-96. doi:10.3233/JAD-140384
Lu X, Wang L, Yu C, Yu D, Yu G. Histone acetylation modifiers in the pathogenesis of Alzheimer's disease. Front Cell Neurosci. 2015;9:226. doi:10.3389/fncel.2015.00226
Pirooznia SK, Sarthi J, Johnson AA, et al. Tip60 HAT activity mediates APP induced lethality and apoptotic cell death in the CNS of a drosophila Alzheimer's disease model. PLoS ONE. 2012;7:e41776. doi:10.1371/journal.pone.0041776
De Simone A, Milelli A. Histone deacetylase inhibitors as multitarget ligands: new players in Alzheimer's disease drug discovery? ChemMedChem. 2019;14:1067-1073. doi:10.1002/cmdc.201900174
Jennings CG, Landman R, Zhou Y, et al. Opportunities and challenges in modeling human brain disorders in transgenic primates. Nat Neurosci. 2016;19:1123-1130. doi:10.1038/nn.4362
Sepulcre J, Grothe MJ, d'Oleire Uquillas F, et al. Neurogenetic contributions to amyloid beta and tau spreading in the human cortex. Nat Med. 2018;24:1910-1918. doi:10.1038/s41591-018-0206-4
Yu M, Risacher SL, Nho KT, et al. Spatial transcriptomic patterns underlying amyloid-β and tau pathology are associated with cognitive dysfunction in Alzheimer's disease. Cell Rep. 2024;43:113691. doi:10.1016/j.celrep.2024.113691
Rullmann M, McLeod A, Grothe MJ, Sabri O, Barthel H. Reshaping the amyloid buildup curve in Alzheimer disease? Partial-volume effect correction of longitudinal amyloid PET data. J Nucl Med. 2020;61:1820-1824. doi:10.2967/jnumed.119.238477
Villemagne VL, Pike KE, Chételat G, et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Ann Neurol. 2011;69:181-192. doi:10.1002/ana.22248
Thomas BA, Erlandsson K, Modat M, et al. The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease. Eur J Nucl Med Mol Imaging. 2011;38:1104-1119. doi:10.1007/s00259-011-1745-9
Schwarz CG, Jones DT, Gunter JL, et al. Contributions of imprecision in PET-MRI rigid registration to imprecision in amyloid PET SUVR measurements. Hum Brain Mapp. 2017;38:3323-3336. doi:10.1002/hbm.23622
