The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
Larger temporal volume in elderly with high versus low beta-amyloid deposition
Abstract
β-Amyloid deposition is one of the main hallmarks of Alzheimer's disease thought to eventually cause neuronal death. Post-mortem and neuroimaging studies have consistently reported cases with documented normal cognition despite high β-amyloid burden. It is of great interest to understand what differentiates these particular subjects from those without β-amyloid deposition or with both β-amyloid deposition and cognitive deficits, i.e. what allows these subjects to resist the damage of the pathological lesions. [¹¹C]Pittsburgh compound B positron emission tomography and magnetic resonance brain scans were obtained in 149 participants including healthy controls and patients with subjective cognitive impairment, mild cognitive impairment and Alzheimer's disease. Magnetic resonance data were compared between high versus low-[11C]Pittsburgh compound B cases, and between high-[¹¹C]Pittsburgh compound B cases with versus those without cognitive deficits. Larger temporal (including hippocampal) grey matter volume, associated with better episodic memory performance, was found in high- versus low-[¹¹C]Pittsburgh compound B healthy controls. The same finding was obtained using different [¹¹C]Pittsburgh compound B thresholds, correcting [¹¹C]Pittsburgh compound B data for partial averaging, using age, education, Mini-Mental State Examination, apolipoprotein E4 and sex-matched subsamples, and using manual hippocampal delineation instead of voxel-based analysis. By contrast, in participants with subjective cognitive impairment, significant grey matter atrophy was found in high-[¹¹C]Pittsburgh compound B cases compared to low-[¹¹C]Pittsburgh compound B cases, as well as in high-[¹¹C]Pittsburgh compound B cases with subjective cognitive impairment, mild cognitive impairment and Alzheimer's disease compared to high-[¹¹C]Pittsburgh compound B healthy controls. Larger grey matter volume in high-[¹¹C]Pittsburgh compound B healthy controls may reflect either a tissue reactive response to β-amyloid or a combination of higher 'brain reserve' and under-representation of subjects with standard/low temporal volume in the high-[¹¹C]Pittsburgh compound B healthy controls. Our complementary analyses tend to support the latter hypotheses. Overall, our findings suggest that the deleterious effects of β-amyloid on cognition may be delayed in those subjects with larger brain (temporal) volume.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“A between-group analysis performed by Chetelat et al. indicated that, in participants with SCD, individuals with a higher level of amyloid deposition showed significant gray matter atrophy compared with individuals with a low level of amyloid deposition [180].”
From Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease (2020) · cites this paper as [180]
Citations
Cites
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676
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
Katzman, R., Terry, R., DeTeresa, R., Brown, T., Davies, P., Fuld, P., Renbing, X., & Peck, A. (1988) · Annals of Neurology · doi:10.1002/ana.410230206 · Cited by 1,225
Dickerson, B. C., Bakkour, A., Salat, D. H., Feczko, E., Pacheco, J., Greve, D. N., Grodstein, F., Wright, C. I., Blacker, D., Rosas, H. D., et al. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn113 · Cited by 1,153
[ 11 C]PIB in a nondemented population: Potential antecedent marker of Alzheimer disease
Mintun, M. A., LaRossa, G. N., Sheline, Y. I., Dence, C. S., Lee, S. Y., Mach, R. H., Klunk, W. E., Mathis, C. A., DeKosky, S. T., & Morris, J. C. (2006) · Neurology · doi:10.1212/01.wnl.0000228230.26044.a4 · Cited by 1,036
Ellis, K. A., Bush, A. I., Darby, D., Fazio, D. D., Foster, J. K., Hudson, P. J., Lautenschlager, N. T., Lenzo, N. P., Martins, R. N., Maruff, P. T., et al. (2009) · International Psychogeriatrics · doi:10.1017/s1041610209009405 · Cited by 1,005
Self-citationRapid appearance and local toxicity of amyloid-β plaques in a mouse model of Alzheimer’s disease
Meyer-Luehmann, M., Spires-Jones, T. L., Prada, C., Garcia-Alloza, M., de Calignon, A., Rozkalne, A., Koenigsknecht-Talboo, J., Holtzman, D. M., Bacskai, B. J., & Hyman, B. T. (2008) · Nature · doi:10.1038/nature06616 · Cited by 987
Jack, C. R., Lowe, V. J., Senjem, M. L., Weigand, S. D., Kemp, B. J., Shiung, M. M., Knopman, D. S., Boeve, B. F., Klunk, W. E., Mathis, C. A., et al. (2008) · Brain · doi:10.1093/brain/awm336 · Cited by 958
Flagged first, then by citations
1 of the 41 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 101–159 of 159
Decline of fiber tract integrity over the adult age range: A diffusion spectrum imaging study
Teipel, S. J., Lerche, M., Kilimann, I., O’Brien, K., Grothe, M. J., Meyer, P., Li, X., Sänger, P., & Hauenstein, K. (2013) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.24420 · Cited by 14
The characteristics of brain atrophy prior to the onset of Alzheimer’s disease: a longitudinal study
Hu, Y., Zhu, T., & Zhang, W. (2024) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2024.1344920 · Cited by 16
Amyloid β influences the relationship between cortical thickness and vascular load
Parker, T. D., Cash, D. M., Lane, C., Lu, K., Malone, I. B., Nicholas, J. M., James, S., Keshavan, A., Murray‐Smith, H., Wong, A., et al. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12022 · Cited by 15
Contribution of iron and Aβ to age differences in entorhinal and hippocampal subfield volume
Foster, C. M., Kennedy, K. M., Daugherty, A. M., & Rodrigue, K. M. (2020) · Neurology · doi:10.1212/wnl.0000000000010868 · Cited by 13
Rogojin, A., Gorbet, D. J., Hawkins, K. M., & Sergio, L. E. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1054516 · Cited by 12
Subcortical amyloid load is associated with shape and volume in cognitively normal individuals
Rahayel, S., Bocti, C., Sévigny-Dupont, P., Joannette, M., Lavallée, M. M., Nikelski, J., Chertkow, H. M., & Joubert, S. (2019) · Human Brain Mapping · doi:10.1002/hbm.24680 · Cited by 11
Cacciaglia, R., Falcón, C., Sánchez‐Benavides, G., Brugulat‐Serrat, A., Alomà, M. M., Suárez‐Calvet, M., Molinuevo, J. L., Fauria, K., Minguillón, C., Kollmorgen, G., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.14415 · Cited by 14
“13 Discrepancies may be due to methodological factors or the difference in mean participants’ age across studies, as well as the biomarkers used for the definition of AD.”
Pérez‐Cordón, A., Monté, G. C., Sanabria, Á., Rodríguez‐Gómez, O., Valero, S., Ruiz, C. A., Marquié, M., Espinosa, A., Ortega, G., Hernandez, I., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-74704-7 · Cited by 14
Apolipoprotein E e4 Allele Is Associated with Subjective Cognitive Decline: A Meta-Analysis
Zhang, T., Liu, S., Zhang, Y., Guan, Y., Wang, X., Zhao, L., Shi, Z., Yue, W., Zhang, Y., Liu, S., et al. (2017) · Neuroepidemiology · doi:10.1159/000482018 · Cited by 11
Lindberg, O. E., Kern, S., Skoog, J., Machado, A., Pereira, J. B., Sacuiu, S. F., Wahlund, L., Blennow, K., Zetterberg, H., Zettergren, A., et al. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.10.033 · Cited by 10
Cantero, J. L., Atienza, M., Sánchez‐Juan, P., Rodríguez‐Rodríguez, E., Vázquez‐Higuera, J. L., Pozueta, A., González‐Suárez, A., Vilaplana, E., Pegueroles, J., Montal, V., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.12.010 · Cited by 11
Association of CSF biomarkers with MRI brain changes in Alzheimer's disease
Seidu, N. M., Kern, S., Sacuiu, S. F., Sterner, T. R., Blennow, K., Zetterberg, H., Lindberg, O. E., Ferreira, D., Westman, E., Zettergren, A., et al. (2024) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12556 · Cited by 9
“6 19 Whenitcomestothehip-pocampus and amygdala, the most common finding reported was the associationbetweenbiomarkerslevels(i.e.,lowerA β 42,highert-tau,p-tau, and NfL in CSF; higher A β accumulation at PET brain imaging) and smaller volume, 13,18,20–26 while previous studies including the basal…”
ResultCacciaglia, R., Shekari, M., Salvadó, G., Milà‐Alomà, M., Falcón, C., Sánchez‐Benavides, G., Minguillón, C., Fauria, K., Grau‐Rivera, O., Molinuevo, J. L., et al. (2024) · Brain Communications · doi:10.1093/braincomms/fcae451 · Cited by 13
Initiative, A. D. N., Wolf, D. G. F., Fischer, F. U., & Fellgiebel, A. (2018) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9870-8 · Cited by 12
“These findings are in line with previous studies indicating that the hippocampus might not only be target of AD-related pathology but also serve as a potential resilience factor (Chételat et al. 2010; Erten-Lyons et al. 2009).”
Brain structural indicators of β-amyloid neuropathology
Jang, I., Li, B., Rashid, B., Jacoby, J. R., Huang, S. Y., Dickerson, B. C., & Salat, D. H. (2024) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2024.01.005 · Cited by 8
“…that misclassified A β + individuals may represent a unique group of individuals, potentially exhibiting some form of cognitive resilience in the face of aging and AD pathology and possibly related to preserved brain structure (Bocancea et al., 2021; Ch ´ etelat et al., 2010; Stern et al., 2020).”
BackgroundMRI-to-PET synthesis via deep learning for amyloid-β quantification in Alzheimer’s disease
Chen, Z., Bi, S., Shan, Y., Wang, F., Wang, Y., Qi, Z., Wang, T., Li, X., Li, S., Xiao, H., et al. (2026) · European Radiology · doi:10.1007/s00330-025-12251-3 · Cited by 8
“When A β deposition exceeds the compensatory capacity of the aging brain, it leads to accelerated cortical atrophy [20, 21].”
Valech, N., Sánchez‐Benavides, G., Tort‐Merino, A., Coll‐Padrós, N., Olives, J., León, M., Falcón, C., Molinuevo, J. L., & Rami, L. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190624 · Cited by 9
Fox‐Fuller, J. T., Torrico‐Teave, H., Uquillas, F. D., Chen, K., Su, Y., Chen, Y., Brickhouse, M. J., Sanchez, J. S., Agüero, C., Jacobs, H. I. L., et al. (2021) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12233 · Cited by 8
Chang, Y., Huang, C., Chen, N.-C., Lin, K., Huang, S., Chang, Y., Hsu, S., Chang, W., Lui, C., Hsu, C.-W., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149056 · Cited by 8
“Another study suggested that a larger temporal lobe volume provides BR that shows resistance to fibrillar β-amyloid impact within the gray matter (GM) [13].”
BackgroundResultInfluentialAmyloid positron emission tomography and cognitive reserve
Bauckneht, M., Picco, A., Nobili, F. M., & Morbelli, S. D. (2015) · World Journal of Radiology · doi:10.4329/wjr.v7.i12.475 · Cited by 8
Subcortical amyloid relates to cortical morphology in cognitively normal individuals
Rahayel, S., Bocti, C., Sévigny-Dupont, P., Joannette, M., Lavallée, M. M., Nikelski, J., Chertkow, H. M., & Joubert, S. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04446-w · Cited by 7
“First, three standardized uptake value ratio (SUVR) values were computed: 1) one value representing the average of the values of all grey matter voxels (“global Aβ burden”); 2) one value representing the average of voxels of the cortical surface only (“cortical Aβ burden”) [15]; and 3) one value representing the area-…”
BackgroundMethodYang, H. J., Dong, C., Cai, Y., Zhao, M., Liu, J., Bian, S., & Ding, X. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1596459 · Cited by 6
“Between-group analyses revealed that SCD subjects with higher levels of amyloid deposition presented with significantly greater gray matter atrophy than those with lower levels of amyloid deposition (Chételat et al., 2010).”
Li, C., Initiative, A. F. T. A. D. N., Duara, R., Loewenstein, D., Izquierdo, W., Cabrerizo, M., Barker, W., & Adjouadi, M. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-180231 · Cited by 6
Lifespan brain activity, b-amyloid, and Alzheimer's disease
Jagust, W. J., & Mormino, E. C. (2011) · Cited by 6
Morita, A., Fujiwara, T., Murayama, H., Machida, M., Inoue, S., & Shobugawa, Y. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad269 · Cited by 5
Novak, G., & Einstein, S. G. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-386945-6.00004-4 · Cited by 4
Pais, M. V., Kuo, C., Ances, B. M., Wetherell, J. L., Lenze, E. J., & Diniz, B. S. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12487 · Cited by 5
Aβ Imaging in Aging, Alzheimer’s Disease and Other Neurodegenerative Conditions
Villemagne, V. L., Fodero‐Tavoletti, M. T., Yates, P. A., Masters, C. L., & Rowe, C. C. (2014) · Springer eBooks · doi:10.1007/978-3-642-54307-4_10 · Cited by 4
Self-citationRisk and resilience: A new perspective on Alzheimer's disease
Teipel, S. J. (2013) · Geriatric Mental Health Care · doi:10.1016/j.gmhc.2013.04.003 · Cited by 6
Setiadi, T. M., Marsman, J.-B. C., Martens, S., Tumati, S., Opmeer, E. M., Reesink, F. E., Deyn, P. P. D., Atienza, M., Aleman, A., & Cantero, J. L. (2024) · Journal of Alzheimer s Disease · doi:10.3233/jad-231196 · Cited by 3
Dartigues, J., Foubert‐Samier, A., & Helmer, C. (2013) · Revue d Épidémiologie et de Santé Publique · doi:10.1016/j.respe.2013.04.004 · Cited by 4
Wang, S., Kang, D. W., Um, Y. H., Kim, S., Lee, C. U., Scheltens, P., & Lim, H. K. (2023) · The Journal of Prevention of Alzheimer s Disease · doi:10.14283/jpad.2023.87 · Cited by 3
“Some studies have shown that A-PET-positive CN have decreased grey matter volume, especially in the hippocampus, compared with A-PET-negative CN (45), but other studies did not find these differences (46).”
ResultRodríguez‐Fernández, B., González‐Escalante, A., Genius, P., Evans, T. E., Ortiz-Romero, P. L., Minguillón, C., Kollmorgen, G., Ashton, N. J., Zetterberg, H., Blennow, K., et al. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105886 · Cited by 2
Kang, D. W., Wang, S., Um, Y. H., Kim, N. Y., Lee, C. U., & Lim, H. K. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220427 · Cited by 2
Septal hypertrophy and cell cycle re-entry in AD
Butler, T., Bowen, R. L., & Atwood, C. (2019) · Aging · doi:10.18632/aging.101777 · Cited by 2
Cortical thickness changes precede high levels of amyloid by at least seven yearsPreprint
Roe, J. M., Jagust, W., Landau, S. M., Harrison, T. M., Grydeland, H., Slivka, M., Warren, J. L. A., Garrido, P. F., Sørensen, Ø., Grødem, E., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.14.670398 · Cited by 2
“Findings from these consistently suggest that distinct cortical changes may occur early relative to later in the disease process 12–17 .”
InfluentialAβ Imaging in Aging, Alzheimer’s Disease, and Other Neurodegenerative Conditions
Villemagne, V. L., Doré, V., Burnham, S. C., & Rowe, C. C. (2020) · Springer eBooks · doi:10.1007/978-3-030-53168-3_10 · Cited by 2
Self-citationWhite matter hyperintensities contribute to early cortical thinning in addition to tau in aging
Leone, R., & Kobeleva, X. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.07.007 · Cited by 1
Pieperhoff, L., Lorenzini, L., Mastenbroek, S. E., Tranfa, M., Shekari, M., Wink, A. M., Wolz, R., Grootoonk, S., Ritchie, C. W., Boada, M., et al. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103912 · Cited by 4
Pieperhoff, L., Lorenzini, L., Almeida, F., Tranfa, M., Bollack, A., Arunachalam, P., Masserini, F., Wolz, R., Grootoonk, S., Ritchie, C. W., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71609 · Cited by 1
“…MRI assessments in Alzheimer’s disease (AD), 2 yet they capture relatively late-stage neurodegeneration and may exhibit early biphasic trajectories, with some studies reporting earlytransientincreasesinvolume,thicknessordensity, 3–8 potentially reflecting early neuroinflammatory cellular processes.”
Augmentation du volume temporal chez les sujets asymptomatiques ayant des dépôts amyloïdes
Chételat, G. (2011) · médecine/sciences · doi:10.1051/medsci/20112712014 · Cited by 1
Self-citationCognitive Aging and the Transition to Early Alzheimer’s Disease
Mormino, E. C., & Jagust, W. J. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190917982.013.59 · Cited by 1
Piersson, A. D., Mohamad, M., Suppiah, S., & Rajab, N. F. (2021) · medRxiv · doi:10.1101/2021.01.20.21250005 · Cited by 1
Localization of Network-Level Atrophy in Preclinical Alzheimer's DiseasePreprint
Lee, S., Baratono, S. R., Burt, G., Palm, S. T., Drew, W., Zide, B. S., Chiulli, N., Larivière, S., Fox, M., Sperling, R. A., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-5977523/v1 · Cited by 1
Qiu, T., Liu, Z.-Q., Gallego-Rudolf, J., Edde, M., Caron, A. V., Zhang, Y., Chakravarty, M. M., Soucy, J., Collins, D. L., Poirier, J., et al. (2025) · medRxiv · doi:10.1101/2025.09.25.25336640 · Cited by 3
Estudi de l'atròfia cerebral en la malaltia de l'Alzheimer
Pegueroles, J. (2014)
Capacités de réserve, vieillissement cérébral et maladie d'Alzheimer
Foubert-Samier, P. A. (2013) · theses.fr (ABES)
Ong, K. T. (2018) · OBM Geriatrics · doi:10.21926/obm.geriatr.1803011
Multimodal Imaging for Enhanced Diagnosis and for Assessing Progression of Alzheimer’s Disease
Li, C. (2018) · doi:10.25148/etd.fidc006548
“parahippocampal in CN stage had been reported in a previous study [136] that informed larger hippocampal and parahippocampal volume in CN participants with high Aβ load”
Sommer, B. R. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.718376635.793560491
Pelkmans, W., Cacciaglia, R., Kassinopoulos, M., Grau‐Rivera, O., Minguillón, C., Molinuevo, J. L., Kollmorgen, G., Quijano‐Rubio, C., Wilson, R. E., Jonaitis, E. M., et al. (2026) · Molecular Neurodegeneration · doi:10.1186/s13024-026-00987-5
Reseco, L., Atienza, M., Fernandez-Alvarez, M., Carro, E., & Cantero, J. L. (2021) · Figshare · doi:10.6084/m9.figshare.16575443
Rodríguez‐Fernández, B., Escalante, A. G., Genius, P., Evans, T. E., Ortiz‐Romero, P., Minguillón, C., Kollmorgen, G., Ashton, N. J., Zetterberg, H., Blennow, K., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz70856_098828
Tomadesso, C. (2018) · theses.fr (ABES) · doi:10.70675/7fad6418zff81z49c7z984dz63aa2e44d7fc
TACR3 variant confers resilience to aging and Alzheimer’s diseasePreprint
Ruffini, N., Fischer, F. U., Slotos, R. S., Goschke, J., Scholz, L., Knaepen, K., Hüttelmaier, S., Morrison, H., Steffan, T., Pabst, A.-S., et al. (2026) · medRxiv · doi:10.64898/2026.06.06.26355071
Atrophy in preclinical Alzheimer’s disease maps to a network that predicts longitudinal decline
Lee, S., Baratono, S. R., Ha, J.-H., Burt, G., Palm, S. T., Drew, W., Zide, B. S., Chiulli, N., Larivière, S., Zhang, S., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03706-0 · Cited by 2
“Some studies support that AD-speci fi c cortical thinning occurs in preclinical AD [3, 4], while others have found no cross-sectional difference [5] or even increased cortical thickness [6].”
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
“Findings from these consistently suggest that distinct cortical changes may occur early relative to later in the disease process 12–17 .”
Influential
Metadata
- Authors
- Chételat, G., Villemagne, V. L., Pike, K. E., Baron, J.-C., Bourgeat, P., Jones, G., Faux, N. G., Ellis, K. A., Salvado, O., Szoeke, C., Martins, R. N., Ames, D., Masters, C. L., & Rowe, C. C.
- Affiliations
- Inserm (FR)
- Mental Health Research Institute (AU)
- University of Cambridge (GB)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Austin Health (AU)
- The University of Melbourne (AU)
- Edith Cowan University (AU)
- National Ageing Research Institute (AU)
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-25
- Volume
- 133
- Issue
- 11
- Pages
- 3349-3358
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2075668621
- Cited by
- 159
- References
- 41
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 8 · 2023: 6 · 2022: 5 · 2021: 10 · 2020: 10 · 2019: 11 · 2018: 13 · 2017: 13 · 2016: 8 · 2015: 8 · 2014: 15 · 2013: 17 · 2012: 8
- FWCI
- 8.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Brain (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Commonwealth Scientific and Industrial Research Organisation
- 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 › Oncology › Cancer-related cognitive impairment studies
- Keywords
- amyloid-beta deposition, Alzheimer's disease, hippocampal volume, brain reserve, gray matter atrophy, mild cognitive impairment, subjective cognitive impairment, episodic memory, 11C-Pittsburgh compound B, amyloid PET
- MeSH terms
- Age Factors, Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Female, Heterozygote, Humans, Male, Organ Size, Temporal Lobe, Cohort Studies, Amyloid beta-Peptides
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
