The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography
Open accessAbstract
See Rabinovici (doi: 10.1093/brain/aww025 ) for a scientific commentary on this article. Cerebral accumulation of amyloid-β is thought to be the starting mechanism in Alzheimer’s disease. Amyloid-β can be detected by analysis of cerebrospinal fluid amyloid-β 42 or amyloid positron emission tomography, but it is unknown if any of the methods can identify an abnormal amyloid accumulation prior to the other. Our aim was to determine whether cerebrospinal fluid amyloid-β 42 change before amyloid PET during preclinical stages of Alzheimer’s disease. We included 437 non-demented subjects from the prospective, longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. All underwent 18 F-florbetapir positron emission tomography and cerebrospinal fluid amyloid-β 42 analysis at baseline and at least one additional positron emission tomography after a mean follow-up of 2.1 years (range 1.1–4.4 years). Group classifications were based on normal and abnormal cerebrospinal fluid and positron emission tomography results at baseline. We found that cases with isolated abnormal cerebrospinal fluid amyloid-β and normal positron emission tomography at baseline accumulated amyloid with a mean rate of 1.2%/year, which was similar to the rate in cases with both abnormal cerebrospinal fluid and positron emission tomography (1.2%/year, P = 0.86). The mean accumulation rate of those with isolated abnormal cerebrospinal fluid was more than three times that of those with both normal cerebrospinal fluid and positron emission tomography (0.35%/year, P = 0.018). The group differences were similar when analysing yearly change in standardized uptake value ratio of florbetapir instead of percentage change. Those with both abnormal cerebrospinal fluid and positron emission tomography deteriorated more in memory and hippocampal volume compared with the other groups ( P < 0.001), indicating that they were closer to Alzheimer’s disease dementia. The results were replicated after adjustments of different factors and when using different cut-offs for amyloid-β abnormality including a positron emission tomography classification based on the florbetapir uptake in regions where the initial amyloid-β accumulation occurs in Alzheimer’s disease. This is the first study to show that individuals who have abnormal cerebrospinal amyloid-β 42 but normal amyloid-β positron emission tomography have an increased cortical amyloid-β accumulation rate similar to those with both abnormal cerebrospinal fluid and positron emission tomography and higher rate than subjects where both modalities are normal. The results indicate that cerebrospinal fluid amyloid-β 42 becomes abnormal in the earliest stages of Alzheimer’s disease, before amyloid positron emission tomography and before neurodegeneration starts. See Rabinovici (doi: 10.1093/brain/aww025 ) for a scientific commentary on this article. The success of disease-modifying therapies in Alzheimer’s disease may depend on detecting the earliest signs of abnormal amyloid-β load. Palmqvist et al . show that CSF amyloid-β 42 becomes abnormal before amyloid PET and before neurodegeneration onset in preclinical Alzheimer’s disease. This may have implications for risk factor interventions and future therapies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“This may be due to a temporal offset between these two measures [121–123].”
From NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease (2018) · cites this paper as [122]
In the citing paper
“This indicates that individuals with AD may first be defined as being CSF+/PET−, and then progress to CSF+/PET+.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [12]
In the citing paper
“those with abnormally low CSF levels of Aβ42, but with still normal Aβ levels detected by PET (CSF+/PET−), accumulated Aβ fibrils longitudinally at a similar rate as those who had both abnormal CSF levels of Aβ42 and abnormal levels of Aβ detected by PET (CSF+/PET+) and four times higher than those with normal biomarker results on both parameters (CSF−/PET−)12.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [12]
In the citing paper
“In a recent study, we also showed that CSF+/PET− subjects accumulated Aβ at a similar rate as CSF+/PET+ subjects and about four times higher than CSF−/PET− subjects12.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [12]
In the citing paper
“Mixture modeling statistics provide an unbiased threshold for abnormality (i.e., the diagnosis or status is not used in analysis) and it is a well-established method for defining Aβ PET cut-offs12, 17, 60, 61.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [12]
In the citing paper
“reduced concentrations of Aβ42 in CSF and increased retention of Aβ tracers in the brain have been considered as early biomarkers of AD.255–258”
From Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future (2023) · cites this paper as [258]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,012
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
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,852
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,007
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 5×
“The results were robust using four different types of group classifications for CSF+/ and PET+/ and when classifying PET +/– based on amyloid-b deposition in regions that are affected in the initial phase of amyloid-b accumulation (Braak and Braak, 1991; Goedert, 2015).”
BackgroundMethodResultInfluentialAlbert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.008 · Cited by 10,152 · cited 2×
“As the disease progresses, affected individuals show neurodegeneration and early clinical symptoms and may be diagnosed with mild cognitive impairment (MCI) (Albert et al., 2011).”
BackgroundThe 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
Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., Iwatsubo, T., Jack, C. R., Kaye, J., Montine, T. J., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.003 · Cited by 7,155
Flagged first, then by citations
Research works printed but not linked: 1
- Biogen. A Randomized, Blinded, Placebo-Controlled Single Ascending Dose Study of the Safety, Tolerability, and Pharmacokinetics of BIIB037 in Subjects with Mild to Moderate Alzheimer's Disease. In: AD/PD International Conference, Nice, France, 2015.
Cited by
Sorted by citations
Items 301–400 of 400
Clinical value of Alzheimer's disease biomarker testing
Patel, K. J., Yang, D., Best, J. R., Chambers, C., Lee, P. E., Henri‐Bhargava, A., Funnell, C. R., Foti, D. J., Pettersen, J. A., Feldman, H., et al. (2024) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12464 · Cited by 3
Tavares-Júnior, J. W. L., Ciurleo, G. C. V., Feitosa, E. D. A. A. F., Oriá, R. B., & Braga‐Neto, P. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231368 · Cited by 3
Method to discriminate amyloids using fluorescent probes
Teppang, K. L., Ehrlich, R. S., & Yang, J. (2020) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2020.04.010 · Cited by 3
Nir, T. M., Haddad, E., Thompson, P. M., & Jahanshad, N. (2021) · Current topics in behavioral neurosciences · doi:10.1007/7854_2021_221 · Cited by 3
Kim, K. H., Seo, J. D., Kim, E. S., Kim, H.-S., Jeon, S., Pak, K., Lee, M. J., Lee, J., Lee, Y. M., Lee, K., et al. (2020) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000396 · Cited by 3
Intraperitoneal hyperthermia in the management of pseudomyxoma peritonei.
Hsu, K.-H., Chou, C.-Y., & Chang, Y.-C. (2007) · PubMed · Cited by 3
Chethana, H. P., Hemachandra, G., & Sidhu, A. (2022) · Springer eBooks · doi:10.1007/978-981-16-6703-9_12 · Cited by 3
Fluid Markers of Alzheimer Disease
Sutphen, C. L., Volluz, K., & Tarawneh, R. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_8 · Cited by 2
Abbate, C., & Gallucci, A. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251374319 · Cited by 2
Cognitive Trajectories from Preclinical Alzheimer's Disease to Dementia
Öhman, F., Rakêt, L. L., Schöll, M., & Initiative, A. D. N. (2026) · Advanced Science · doi:10.1002/advs.202518124 · Cited by 2
Biomarkers for Neurodegenerative Diseases
Abdelmoaty, M. M., Lu, E., & Foster, E. G. (2024) · Springer eBooks · doi:10.1007/978-3-031-68237-7_44 · Cited by 2
Fluid Biomarkers in Alzheimer’s Disease and Frontotemporal Dementia
Mattsson, N., Grigoriou, S., & Zetterberg, H. (2018) · Springer eBooks · doi:10.1007/978-3-319-72938-1_11 · Cited by 2
Self-citationBiomarqueurs du liquide cérébrospinal dans la maladie d’Alzheimer
Hugon, J., Dumurgier, J., Cognat, E., & Paquet, C. (2018) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/s0001-4079(19)30359-0 · Cited by 2
Doecke, J. D., François, C., Fowler, C. J., Stoops, E., Bourgeat, P., Rainey‐Smith, S. R., Li, Q., Masters, C. L., Martins, R. N., Villemagne, V. L., et al. (2021) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00812-9 · Cited by 2
A Robust Blood-based Signature of Cerebrospinal Fluid Aβ42 Status
Eke, C. S., Sakr, F. A., Jammeh, E., Zhao, P., & Ifeachor, E. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175158 · Cited by 2
Biel, D., Suárez‐Calvet, M., Hager, P., Rubinski, A., Dewenter, A., Steward, A., Roemer, S. N., Ewers, M., Haass, C., Brendel, M., et al. (2022) · medRxiv · doi:10.1101/2022.08.26.22279269 · Cited by 2
Liu, L., Huang, X., Xu, J., Wang, X., Liu, Y., Ren, X., Zhang, C., Wang, Y., Wang, Y., Zhou, S., et al. (2025) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2025.109964 · Cited by 1
Cerebrospinal Fluid Biomarkers of Alzheimer Disease
Васильевна, Н. К., Shpilyukova, Y. A., Шабалина, А. А., Fedotova, E. Y., & Illarioshkin, S. N. (2025) · Annals of Clinical and Experimental Neurology · doi:10.17816/acen.1185 · Cited by 1
Park, S., Kim, D., Choi, J. H., Hong, C. H., Son, S. J., Roh, H. W., Shin, H., & Woo, H. G. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf366 · Cited by 1
Plasma biomarkers in neuropsychiatric syndromes: A narrative review
Ismail, Z., Guan, D. X., Babulal, G. M., Bateman, J. R., Cantillon, M., Creese, B., D’Antonio, F., Fischer, C. E., Gatchel, J. R., Ghahremani, M., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251392185 · Cited by 3
Rani, N., Moghekar, A., Callow, D. D., Alm, K. H., Pettigrew, C. A., Soldan, A., Miller, M. H., Wang, J., Lodge, M. A., Albert, M., et al. (2026) · Neurology · doi:10.1212/wnl.0000000000214620 · Cited by 1
Incidence of altered proteins in the aging brain: Implications for biological diagnostic markers
Alafuzoff, I., & Libard, S. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877251414978 · Cited by 1
Amyloid spatial extent with florbetapir-PET for early detection of preclinical Alzheimer’s disease
Thibault, E. G., Montenegro, G. A. D. C., Becker, J. A., Price, J. C., Healy, B. C., Hanseeuw, B. J., Buckley, R. F., Jacobs, H. I. L., Properzi, M. J., Sperling, R. A., et al. (2026) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2026.100529 · Cited by 1
Nocito, G., Scuderi, V., Turnaturi, R., Sabatino, G., Filice, S., Crispi, S., Trapani, M., Scala, A., Pappalardo, G., Scalese, S., et al. (2026) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2026.153002 · Cited by 1
Neurotheranostics in Alzheimer's Disease: Current concepts and experimental strategies
Yanagisawa, D., Ohgita, T., Kawashima, H., Yoshikawa, H., Saito, H., Tooyama, I., & Takata, K. (2026) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2026.109084 · Cited by 1
Real‐world implementation of lecanemab in Alzheimer's disease: A Japanese cohort
Shimohama, S., Bun, S., Shikimoto, R., Tomioka, H., Takayama, T., Mimura, Y., 笹栗弘貴, Sekiguchi, K., Nishimoto, Y., Iwabuchi, Y., et al. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70492 · Cited by 1
Allegri, R., Méndez, P. C., Russo, M. J., Cohen, G., Calandri, I. L., Campos, J., Nahas, F., Surace, E. I., Vázquez, S., & Sevlever, G. E. (2019) · Neurología (English Edition) · doi:10.1016/j.nrleng.2017.12.012 · Cited by 2
Brain MRI signatures across sex and CSF Alzheimer’s disease biomarkers
Cheng, Y., He, Y., Gopinath, K. K., Billot, B., Iglesias, J. E., Wu, C., Dodge, H. H., Wills, A.-M. A., Carlyle, B. C., Kivisäkk, P., et al. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf210 · Cited by 1
A “Stress Test” of the 2018 NIA AA Research Criteria for Alzheimer’s Disease
Hildebrandt, H., Kastrup, A., & Eling, P. (2020) · Zeitschrift für Neuropsychologie · doi:10.1024/1016-264x/a000281 · Cited by 1
A blood-based signature of cerebrospinal fluid A β 1–42 statusPreprint
Goudey, B., Fung, B. J., Schieber, C., Initiative, A. D. N., & Faux, N. G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/190207 · Cited by 1
Neural Network Training with Highly Incomplete DatasetsPreprint
Chang, Y., Natali, L., Jamialahmadi, O., Romeo, S., Pereira, J. B., & Volpe, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00429 · Cited by 1
Ferrari‐Souza, J. P., Lussier, F. Z., Leffa, D. T., Therriault, J., Tissot, C., Bellaver, B., Ferreira, P. C. L., Malpetti, M., Wang, Y.-T., Povala, G., et al. (2022) · medRxiv · doi:10.1101/2022.07.12.22277556 · Cited by 1
Mieling, M., Göttlich, M., Yousuf, M., Bunzeck, N., & Initative, T. A. D. N. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.28.534523 · Cited by 1
Brădeanu, A. V., Pascu, L. S., Sârbu, N., Ciubară, B. A., Munteniță, C., Nechita, A., Sârbu, M. I., & Ciubară, A. (2023) · Preprints.org · doi:10.20944/preprints202305.0052.v1
Lecy, E. E., Min, H.-K. P., Apgar, C. J., Maltais, D. D., Lundt, E. S., Albertson, S. M., Senjem, M. L., Schwarz, C. G., Botha, H., Graff‐Radford, J., et al. (2023) · medRxiv · doi:10.1101/2023.05.14.23289948 · Cited by 1
Azaraein, M. H., Zarrazvand, E., Heydari, F., Asimi, N., Ramezannezhad, E., Rahimipour, D., Janeshin, K., Mahdian, A., Sichani, M. F., Farhadi, S., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7327653/v1 · Cited by 1
Where Are Amyloid Changes Seen First in Alzheimer Disease
Molano, J. R. V. (2016) · Journal watch · doi:10.1056/nejm-jw.na41192
Elevated Brain Amyloid in Cognitively Normal Individuals
Gao, R., Chen, C., & Zhu, T. (2017) · JAMA · doi:10.1001/jama.2017.12951
Imaging of Brain Amyloid Load in Early Alzheimer’s Disease
Grimmer, T., & Perryman, J. G. (2018) · Neuromethods · doi:10.1007/978-1-4939-7674-4_9
Nobili, F. M., Cagnin, A., Calcagni, M. L., Chincarini, A., Guerra, U. P., Morbelli, S. D., Padovani, A., Paghera, B., Pappatà, S., Parnetti, L., et al. (2019) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.18.03069-8
Reimand, J., de Wilde, A., Teunissen, C. E., Zwan, M. D., Windhorst, A. D., Boellaard, R., Barkhof, F., van der Flier, W. M., Scheltens, P., van Berckel, B. N. M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/673467
Khatun, A., Paterson, R. W., & Schöll, M. (2020) · Alzheimer s Research & Therapy · doi:10.1186/s13195-020-0583-z
Venkataraman, I., & Naides, S. J. (2020) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2020.06.001
Introduction: neuropsychology and the assessment of patients with dementia
Hildebrandt, H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821072-7.00001-1
Alzheimer’s disease: neuropathology, neuropsychological differential diagnosis, and treatment
Hildebrandt, H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821072-7.00002-3
Major, C. K., & Okhravi, H. R. (2021) · Cureus · doi:10.7759/cureus.13481
Impact de l’imagerie TEP métabolique et moléculaire dans les maladies neurodégénératives cognitives
Lagarde, J., Olivieri, P., Bottlaender, M., & Sarazin, M. (2021) · Pratique Neurologique - FMC · doi:10.1016/j.praneu.2021.03.011 · Cited by 2
Spatial Extent of Amyloid-β Levels and Associations with Alzheimer’s Disease BiomarkersPreprint
Ozlen, H., Binette, A. P., Köbe, T., Meyer, P., Breitner, J. C. S., Poirier, J., & Villeneuve, S. (2021) · Research Square · doi:10.21203/rs.3.rs-711524/v1
Piersson, A. D., Mohamad, M., Suppiah, S., & Rajab, N. F. (2021) · Clinical and Translational Imaging · doi:10.1007/s40336-021-00440-1
Diagnosi clinicolaboratoristica della malattia di Alzheimer
Lagarde, J., Olivieri, P., Bottlaender, M., & Sarazin, M. (2021) · EMC - Neurologia · doi:10.1016/s1634-7072(21)45320-1 · Cited by 1
Яхьяевич, Я. Н., & Vagabov, N. M. (2009) · VESTNIK OF ASTRAKHAN STATE TECHNICAL UNIVERSITY SERIES MARINE ENGINEERING AND TECHNOLOGIES
Development of Fluid Biomarkers for Alzheimer’s Disease
Blennow, K. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108975759.033
Predicting MCI and Alzheimer’s disease on structural brain integrity with machine learningPreprint
Mieling, M., Yousuf, M., & Bunzeck, N. (2023) · doi:10.31219/osf.io/8dtcm
Gerhand, S. (2024) · Cambridge University Press eBooks · doi:10.1017/9781009025911.014
Krishnan, A. R. (2024) · Qapsule Queen s Undergraduate Health Sciences Journal · doi:10.24908/qap.v1i2.17339
Öztürk, G., Harı, E., Yıldırım, K., Bayram, A., Yıldırım, Z., Demıralp, T., & Gurvit, H. I. (2025) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2025.109082
Accrual of Alzheimer's disease pathology as a function of proximity to parental dementia onset
Ziukelis, E. T., Mak, E., Ritchie, C. W., O'Brien, J. T., & Aarsland, D. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70092
Fluid-based biomarkers in neurodegeneration: Current findings and future directions
Blagojević, S., Jeremić, M., & Marković, I. (2024) · Medicinski podmladak · doi:10.5937/mp75-54180
Clinical investigation cases studies and critical thinking problem approaches
ZEIDAN, H. M., & ZEIDAN, I. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-21632-9.00010-8
An overview of current diagnostic strategies
Cappa, S. F., & Cerami, C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-822240-9.00007-7
Advances in Early Biological Diagnostic Markers for Alzheimer's Disease
Duan, J. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2025.22919
Imaging Markers of Amyloid and Tau in Alzheimer Disease
Tarawneh, R. M. (2025) · Springer eBooks · doi:10.1007/978-3-031-80849-4_20
Chong, J. R., Hye, A., Boada, M., Svenningsson, P., Fladby, T., & Aarsland, D. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251360404
Ma, F., Özbay, P. S., Bilgiç, B., Hedden, T., Delman, B. N., Balchandani, P., & Alipour, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.31.673334
P-TAU205 IS A BIOMARKER LINKED TO TAU-PET ABNORMALITY: A CROSS-SECTIONAL AND LONGITUDINAL STUDYPreprint
Lantero‐Rodriguez, J., Janelidze, S., Palmqvist, S., Braun-Wohlfahrt, L. S., Vávra, J., Bali, D., Dolado, A. O., Mattsson, N., Stomrud, E., Zetterberg, H., et al. (2025) · medRxiv · doi:10.1101/2025.10.21.25338437
Self-citationSeo, E. H., Lim, H. J., Yoon, H.-J., Choi, K. Y., Lee, J. J., Park, J. Y., Choi, S. H., Kim, H., Kim, B. C., & Lee, K. H. (2021) · Figshare · doi:10.6084/m9.figshare.16779366
Mouton‐Liger, F., Dumurgier, J., Cognat, E., Hourrègue, C., Zetterberg, H., Vanderstichele, H., Vanmechelen, E., Bouaziz-Amar, É., Blennow, K., Hugon, J., et al. (2020) · Figshare · doi:10.6084/m9.figshare.12679732
Guo, Y., Huang, Y.-Y., Shen, X.-N., Chen, S.-D., Hu, H., Wang, Z.-T., Tan, L., & Yu, J.-T. (2021) · Figshare · doi:10.6084/m9.figshare.14538932
Milà-Alomà, M., Shekari, M., Salvadó, G., Gispert, J. D., Arenaza‐Urquijo, E. M., Operto, G., Falcón, C., Vilor‐Tejedor, N., Grau‐Rivera, O., Sala‐Vila, A., et al. (2021) · Figshare · doi:10.6084/m9.figshare.15064593
Doecke, J. D., François, C., Fowler, C. J., Stoops, E., Bourgeat, P., Rainey-Smith, S. R., Li, Q.-X., Masters, C. L., Martins, R. N., Villemagne, V. L., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14442834
Brand, A. L., Lawler, P. E., Bollinger, J. G., Li, Y., Schindler, S. E., Li, M., Lopez, S., Ovod, V., Nakamura, A., Shaw, L. M., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22603541
Self-citationAndersson, E., Schultz, N., Saito, T., Saido, T. C., Blennow, K., Gouras, G. K., Zetterberg, H., & Hansson, O. (2023) · Figshare · doi:10.6084/m9.figshare.22620482
Self-citationNakano, M., Kondo, K., Ishiki, K., Moniruzzaman, M., Mitsuishi, Y., KADOTA, A., Watanabe, S., Yamashita, K., Miura, M., Iwanaga, S., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251409337
ApoE and COVID-19 Risk and Long-Term Burden
Tavares-Júnior, J. W. L., Ciurleo, G. C. V., Oriá, R. B., & Braga‐Neto, P. (2025) · Springer eBooks · doi:10.1007/978-3-031-52641-1_56-1
Zhang, Y., Sun, L., Zhao, Y., Zhang, Z., Chen, H., Zhao, M., Miao, Z., Wang, W., Xu, X., & Initiative, A. D. N. (2026) · BMC Medicine · doi:10.1186/s12916-026-04623-3
Vellone, D. A., Leon, R., Goodarzi, Z., Forkert, N. D., Smith, E. E., & Ismail, Z. (2026) · medRxiv · doi:10.64898/2026.02.25.26347102
Patel, H., Choi, A., Weng, P. J., Karl, J. J., Joseph, S., Johnson, K. G., Berger, M., Whitson, H. E., Grewal, D. S., & Fekrat, S. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261423968
Optimal control of Alzheimer’s disease model via multi-target combination therapy
Dong, M., Guo, Z., Zhang, Y., Luo, J., & Zhang, J. (2026) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2026.112425
Ma, F., Özbay, P. S., Bilgiç, B., Hedden, T., Delman, B. N., Balchandani, P., & Alipour, A. (2026) · NMR in Biomedicine · doi:10.1002/nbm.70268
Abdelouahab, O. B. (2021) · theses.fr (ABES) · doi:10.70675/97f4885fz3f5cz4234zb8e2ze3fa7c4e3d41
Dautricourt, S. (2021) · theses.fr (ABES) · doi:10.70675/63472a25z31eaz4ebczaebdzd8d2e061b4b8
The fluidity of fluid biomarker evidence in the clinic
Vandenberghe, R. R. (2026) · Brain · doi:10.1093/brain/awag053
Role of T-cell immunity in the pathophysiology of Alzheimer’s disease and other Tauopathies
Matta, K. (2024) · theses.fr (ABES) · doi:10.70675/cea4fe5ez67c3z4445z92f7z39af62ba059b
Liu, K.-Z., Zhou, X., Dong, Y.-F., Luo, X., Tan, L., Tan, C., & Xu, W. (2026) · Journal of Advanced Research · doi:10.1016/j.jare.2026.04.030
ApoE and COVID-19 Risk and Long-Term Burden
Tavares-Júnior, J. W. L., Ciurleo, G. C. V., Oriá, R. B., & Braga‐Neto, P. (2026) · Springer eBooks · doi:10.1007/978-3-031-96089-5_56
Ma, F., Akolkar, H., Benosman, R. B., & Herrup, K. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71493
Rizzo, M., Oomens, J. E., Jansen, W. J., Visser, P. J., & Vos, S. J. B. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261453099 · Cited by 1
Jordan, D. M., Kritzer, E., Thompson, R. C., Lund, A. N., Patel, T., Grice, A. B., John, D., Initiative, A. D. N., Initiative, A. D. N., Goate, A., et al. (2026) · medRxiv · doi:10.64898/2026.07.07.26356710
Mild behavioral impairment-apathy and Alzheimer's disease plasma phosphorylated tau biomarker levels
Vellone, D. A., Leon, R., Goodarzi, Z., Forkert, N. D., Smith, E. E., & Ismail, Z. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261470257
Amyloid-PET pipeline choice influences classification of preclinical Alzheimer’s diseasePreprint
Coath, W., Bollack, A., Scott, C. J., Keshavan, A., Malone, I. B., Murray‐Smith, H., Markiewicz, P. J., Erlandsson, K., Thomas, B. A., Barkhof, F., et al. (2026) · medRxiv · doi:10.64898/2026.08.14.26360465
Four-year evolution of the retinal microvasculature in an APOE ε4/ε4 cognitively normal individual
Patel, H., Corpuz, E., Liu, A. J., Whitson, H. E., Grewal, D. S., & Fekrat, S. (2026) · BMJ Case Reports · doi:10.1136/bcr-2026-274211
Cortical thickness changes precede high levels of amyloid by at least 7 years
Roe, J. M., Jagust, W. J., Landau, S. M., Harrison, T. M., Grydeland, H., Slivka, M., Warren, J. L. A., Garrido, P. F., Sørensen, Ø., Grødem, E., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02363-4 · Cited by 1
Cerebrospinal fluid biomarkers for cerebral amyloid angiopathy with non‐hemorrhagic imaging marker
Sekita, A., Sprügel, M. I., Balk, S., Haupenthal, D., Engelhorn, T., Schmidt, M. A., Doerfler, A., Oberstein, T. J., Maler, J. M., Kornhuber, J., et al. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70431
CSF p-tau205: a biomarker of Alzheimer’s disease progression and biological staging
Lantero‐Rodriguez, J., Janelidze, S., Palmqvist, S., Braun-Wohlfahrt, L. S., Vávra, J., Bali, D., Dolado, A. O., Mattsson, N., Stomrud, E., Zetterberg, H., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03838-3
Self-citationShimasaki, R., Kurihara, M., Sato, K., Bannai, T., Hatano, K., Ishii, K., Ihara, R., Ogawa, S., & Iwata, A. (2026) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70497
Ashrafzadeh‐Kian, S., Figdore, D. J., Bornhorst, J. A., Vanderboom, P. M., Callison, S., Day, G. S., Lachner, C., Graff‐Radford, N. R., Shir, D., Jack, C. R., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71887
Xiong, S.-Y., ZHAO, Y.-L., Fu, Y., Li, Q., Zhang, D. L., Qiu, S.-D., Wang, Y.-C., Yan, Z., Wei, Z., & Tan, L. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261489152
Zhang, S., Roemer, S. N., Chong, J. R., Fang, T., Li, Y., Wang, Z., Zeng, T., Li, X., Deco, G., Venketasubramanian, N., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71891
Super-Cross-Reactive Recognition Enables Preclinical Diagnosis of Alzheimer’s Disease
Li, W., Li, J., Ning, X., Tian, J., Chen, F., Zhang, S.-X., Wang, Z.-H., Pan, Y., Zhang, W., & Guo, D. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c14717
Metadata
- Authors
- Palmqvist, S., Mattsson, N., Hansson, O., & Initiative, F. T. A. D. N.
- Affiliations
- Lund University (SE)
- Corresponding author
- Palmqvist, Sebastian
- Hansson, Oskar
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-03-02
- Volume
- 139
- Issue
- 4
- Pages
- 1226-1236
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC4806222
- OpenAlex
- W2302528231
- Cited by
- 400
- References
- 59
- Citations by year
- 2026: 32 · 2025: 41 · 2024: 33 · 2023: 31 · 2022: 36 · 2021: 58 · 2020: 50 · 2019: 43 · 2018: 38 · 2017: 25 · 2016: 11
- FWCI
- 34.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Brain (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Lunds Universitet
- National Institutes of Health (5u01ag024904-10)
- Canadian Institutes of Health Research
- National Institute on Aging (U01-AG-024904)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Clinical Neurology › Cerebrospinal fluid and hydrocephalus
- Keywords
- cerebrospinal fluid amyloid-beta 42, amyloid PET, florbetapir, preclinical Alzheimer's disease, standardized uptake value ratio, memory decline, hippocampal volume, Alzheimer's dementia, biomarker comparison, ADNI cohort, longitudinal follow-up
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Cerebral Cortex, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Peptide Fragments, Prospective Studies, Amyloid beta-Peptides, Early Diagnosis, Positron-Emission Tomography
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review