Cited by 827Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCancer-related cognitive impairment studies

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Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD

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Jack, C. R., Shiung, M. M., Gunter, J. L., O’Brien, P. C., Weigand, S. D., Knopman, D. S., Boeve, B. F., Ivnik, R. J., Smith, G. E., Cha, R. H., Tangalos, E. G., Petersen, R. C.·2004·Neurology

DOI 10.1212/01.wnl.0000110315.26026.efPMC PMC2730165

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Abstract

OBJECTIVE: To correlate different methods of measuring rates of brain atrophy from serial MRI with corresponding clinical change in normal elderly subjects, patients with mild cognitive impairment (MCI), and patients with probable Alzheimer disease (AD). METHODS: One hundred sixty subjects were recruited from the Mayo Clinic Alzheimer's Disease Research Center and Alzheimer's Disease Patient Registry Studies. At baseline, 55 subjects were cognitively normal, 41 met criteria for MCI, and 64 met criteria for AD. Each subject underwent an MRI examination of the brain at the time of the baseline clinical assessment and then again at the time of a follow-up clinical assessment, 1 to 5 years later. The annualized changes in volume of four structures were measured from the serial MRI studies: hippocampus, entorhinal cortex, whole brain, and ventricle. Rates of change on several cognitive tests/rating scales were also assessed. Subjects who were classified as normal or MCI at baseline could either remain stable or convert to a lower-functioning group. AD subjects were dichotomized into slow vs fast progressors. RESULTS: All four atrophy rates were greater among normal subjects who converted to MCI or AD than among those who remained stable, greater among MCI subjects who converted to AD than among those who remained stable, and greater among fast than slow AD progressors. In general, atrophy on MRI was detected more consistently than decline on specific cognitive tests/rating scales. With one exception, no differences were found among the four MRI rate measures in the strength of the correlation with clinical deterioration at different stages of the disease. CONCLUSIONS: These data support the use of rates of change from serial MRI studies in addition to standard clinical/psychometric measures as surrogate markers of disease progression in AD. Estimated sample sizes required to power a therapeutic trial in MCI were an order of magnitude less for MRI than for change measures based on cognitive tests/rating scales.

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Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,845
Clinical diagnosis of Alzheimer's disease: Report of the NINCDS‐ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease
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 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
Mild Cognitive Impairment: Clinical Characterization and Outcome
Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980
Self-citation
Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis
Smith, S. M., Zhang, Y., Jenkinson, M., Chen, J. M., Matthews, P. M., Federico, A., & Stefano, N. D. (2002) · NeuroImage · doi:10.1006/nimg.2002.1040 · Cited by 2,013
Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment
Jack, C. R., Petersen, R. C., Xu, Y. C., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Waring, S. C., Tangalos, E. G., & Kokmen, E. (1999) · Neurology · doi:10.1212/wnl.52.7.1397 · Cited by 1,404
Self-citation
Medial temporal atrophy on MRI in normal aging and very mild Alzheimer's disease
Jack, C. R., Petersen, R. C., Xu, Y. C., Waring, S. C., O'Brien, P. C., Tangalos, E. G., Smith, G. E., Ivnik, R. J., & Kokmen, E. (1997) · Neurology · doi:10.1212/wnl.49.3.786 · Cited by 1,128
Self-citation
Rates of hippocampal atrophy correlate with change in clinical status in aging and AD
Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., Boeve, B. F., Tangalos, E. G., & Kokmen, E. (2000) · Neurology · doi:10.1212/wnl.55.4.484 · Cited by 937
Self-citation
Aging, Memory, and Mild Cognitive Impairment
Petersen, R. C., Kokmen, E., Waring, S. C., Smith, G. E., Ivnik, R. J., & Tangelos, E. G. (1997) · International Psychogeriatrics · doi:10.1017/s1041610297004717 · Cited by 895
Self-citation
Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer’s disease
Small, G. W., Ercoli, L. M., Silverman, D. H. S., Huang, S.-C., Komo, S., Bookheimer, S. Y., Lavretsky, H., Miller, K., Siddarth, P., Rasgon, N. L., et al. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.090106797 · Cited by 790

Show all 41 references

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K-Bayes Reconstruction for Perfusion MRI I: Concepts and Application
Kornak, J., Young, K., Schuff, N. W., Du, A., Maudsley, A. A., & Weiner, M. W. (2009) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-009-9183-y · Cited by 6
First magenetic resonance imaging studies on aluminium maltolate‐treated aged New Zealand rabbits: an Alzheimer's animal model
Obulesu, M., Dowlathabad, M. R., Raichurkar, K. P., & Shamasundar, N. M. (2015) · Psychogeriatrics · doi:10.1111/psyg.12158 · Cited by 6
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Woods, C., Xing, X., Khanal, S., & Lin, A. (2024) · Bioengineering · doi:10.3390/bioengineering11090943 · Cited by 5
“These factors contribute to more pronounced atrophy in specific brain regions for APOE4 carriers and manifest differently between the sexes [3,4,10–12].”
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Crespo-Cuevas, A. M., Elena López-Cancio, M., Cáceres, C. M., González, A., Ispierto, L., Hernández‐Pérez, M., Mataró, M., Planas, A. M., Canento, T., Martín-Aguilar, L., et al. (2019) · Journal of Alzheimer s Disease · doi:10.3233/jad-190949 · Cited by 5
“Previous studies with MRI have demonstrated that serial measurement of ventricular volume and its enlargement over the time correlated with cognitive decline [44–47].”
Preserved brain youthfulness: longitudinal evidence of slower brain aging in superagers
Park, C.-H., Kim, B. R., Lim, S. M., Kim, E., Jeong, J. H., & Kim, G. H. (2025) · GeroScience · doi:10.1007/s11357-025-01531-x · Cited by 5
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Nobili, F. M., Arnaldi, D., Roccatagliata, L., Chincarini, A., Accardo, J., Picco, A., Ferrara, M., Buschiazzo, A., & Morbelli, S. D. (2014) · Springer eBooks · doi:10.1007/978-3-642-54307-4_12 · Cited by 5
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Aksman, L., Lythgoe, D. J., Williams, S., Jokisch, M., Mönninghoff, C., Streffer, J. R., Jöckel, K., Weimar, C., & Marquand, A. F. (2016) · Human Brain Mapping · doi:10.1002/hbm.23317 · Cited by 5
“Overall, these patterns of coefficients are highly consistent with the literature on whole brain and ventricular volume change in dementia subjects relative to controls [Freeborough and Fox, 1997; Jack et al., 2004; Nestor et al., 2008].”
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Visual short-term memory binding deficits in Alzheimer's disease: a reply to Parra's commentary.
Liang, Y., Pertzov, Y., Nicholas, J. M., Henley, S. M. D., Crutch, S. J., Woodward, F., & Masud Husain, B. (2016) · Cortex · doi:10.1016/j.cortex.2016.11.003 · Cited by 5
Cytoarchitectonic Mapping of MRI Detects Rapid Changes in Alzheimer's Disease
Blair, J. C., Lasiecka, Z. M., Patrie, J. T., Barrett, M. J., & Druzgal, T. J. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00241 · Cited by 6
An off-the-grid approach to multi-compartment magnetic resonance fingerprinting
Golbabaee, M., & Poon, C. (2022) · Inverse Problems · doi:10.1088/1361-6420/ac70da · Cited by 5
“for studies related to several brain disorders such as Alzheimer’s disease, multiple sclerosis, or Schizophrenia [6]–[8].”
Background
Clinical and radiological characteristics of early versus late mild cognitive impairment in patients with comorbid depressive disorder
Motter, J. N., Pelton, G. H., D’Antonio, K., Rushia, S. N., Pimontel, M. A., Petrella, J. R., Garcon, E., Ciovacco, M., Sneed, J. R., Doraiswamy, P. M., et al. (2018) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.4955 · Cited by 5
“In patients with MCI, those who convert to AD have greater rate of atrophy in the hippocampus and entorhinal cortex than those who remain cognitively stable.(20) Further, those with DEP‐CI who transition to a clinical diagnosis of AD do so at a younger age and with greater hippocampal volume loss than patients with MC…”
Background
Bedeutung der zerebralen Perfusionsmessung am Mehrzeilenspiral-CT in der Demenzdiagnostik
Streitparth, F., Wieners, G., Kämena, A., Schröder, R. J., Stiepani, H., Kokocinski, T., Röttgen, R., Steinhagen‐Thiessen, E., Lenzen-Großimlinghaus, R., & Hidajat, N. (2006) · Der Radiologe · doi:10.1007/s00117-006-1443-y · Cited by 5
“Eine Korrelation zwischen Atrophierate und klinischem Demenzgrad konnte in seriellen Studien für bestimmte Hirnareale wie dem entorhinalen und temporalen Kortex oder Hippokampus gezeigt werden [9, 18, 28, 34].”
Background
Maladie d’Alzheimer et maladies apparentées : vers un diagnostic plus précis et précoce
Fortin, M.-P., & Krolak‐Salmon, P. (2010) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2010.08.006 · Cited by 9
Magnetic resonance imaging of Alzheimer’s disease: from diagnosis to therapeutic evaluation
Gu, X., Jiang, Z.-F., & Huang, H. (2010) · Chinese Journal of Integrative Medicine · doi:10.1007/s11655-010-0276-8 · Cited by 5
Neuroimaging of Alzheimer’s Disease, Mild Cognitive Impairment, and Other Dementias
Risacher, S. L., & Saykin, A. J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-6373-4_19 · Cited by 5
Alzheimer’s Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe AtrophyPreprint
Petkus, A. J., Salminen, L. E., Wang, X., Driscoll, I. F., Millstein, J., Beavers, D. P., Espeland, M. A., Braskie, M. N., Thompson, P. M., Casanova, R., et al. (2023) · medRxiv · doi:10.1101/2023.11.29.23299144 · Cited by 5
“59 The ERC is also one of the earliest brain regions to exhibit pathological tau according to the original Braak & Braak model of Alzheimer’s disease neuropathology.”
Background
Adherence to the MIND diet and longitudinal brain structural changes over a decade: evidence from the Framingham heart study offspring cohort
Chen, H., Hailili, G., Tong, L., Fei, L., Cao, Y., Xu, X., Li, X., van Lent, D. M., & Yuan, C. (2026) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2025-336957 · Cited by 5
“Brain MRI offers a precise method for assessing brain structure and atrophy, 16–18 which could occur years to decades before clinical symptoms emerge, a period when dietary factors could play a key preventive role.”
Single‐session reproducibility of MR spectroscopy measures of glutathione in the mesial temporal lobe with MEGA‐PRESS
Vidyasagar, R., Fazollahi, A., Desmond, P. M., Moffat, B. A., Bush, A. I., & Ayton, S. (2024) · Journal of Neuroimaging · doi:10.1111/jon.13179 · Cited by 4
Baseline and longitudinal changes in cortical thickness and hippocampal volume predict cognitive decline
Chen, G., McKay, N. S., Gordon, B. A., Joseph‐Mathurin, N., Liu, J., Schindler, S. E., Hassenstab, J. J., Doering, S., Aschenbrenner, A. J., Wang, Q., et al. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251352202 · Cited by 4
“22 While we do replicate these results, we were also interested in whether longitudinal changes in hippocampal volume would have additional value for predicting cognitive decline.”
Better Utilization of Mouse Models of Neurodegenerative Diseases in Preclinical Studies: From the Bench to the Clinic
Janus, C., Hernandez, C., deLelys, V., Roder, H. M., & Welzl, H. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3661-8_18 · Cited by 4
Hippocampal neurobiology and function in an aged mouse model of TDP-43 proteinopathy in an APP/PSEN1 background
Arezoumandan, S., Cai, X., Kalkarni, P., Davis, S. A., Wilson, K. I., Ferris, C. F., Cairns, N. J., & Gitcho, M. A. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.136010 · Cited by 4
Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment
Sanabria-Díaz, G., Démonet, J., Rodríguez‐Herreros, B., Draganski, B., Kherif, F., & Melie‐García, L. (2021) · NeuroImage Clinical · doi:10.1016/j.nicl.2021.102799 · Cited by 5
Prediction of Expanded Disability Status Scale in patients with MS using deep learning
Kabir, V. H., Khayati, R. M., Nasrabadi, A. M., & Nabavi, S. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109143 · Cited by 5
Ex vivo brain MRI to assess conventional and FLASH brain irradiation effects
Jansen, J., Kimbler, A., Drayson, O. G. G., Lanz, B., Mosso, J., Grilj, V., Petit, B., Franco‐Pérez, J., Simon, A. B., Limoli, C. L., et al. (2025) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2025.110894 · Cited by 4
Metabolomics in the Study of Alzheimer's Disease
Ibáñez, C., Valdés, A., García‐Cañas, V., & Simó, C. (2014) · Comprehensive analytical chemistry · doi:10.1016/b978-0-444-62650-9.00010-5 · Cited by 4
Vers un diagnostic biologique de la maladie d’Alzheimer et des syndromes apparentés
Krolak‐Salmon, P., Seguin, J., Perret‐Liaudet, A., Desestret, V., Vighetto, A., & Bonnefoy, M. (2008) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2008.01.029 · Cited by 8
A versatile algorithm for the automatic segmentation of hippocampus based on level set
Rajeesh, J., Moni, R. S., Palanikumar, S., & Gopalakrishnan, T. (2011) · International Journal of Biomedical Engineering and Technology · doi:10.1504/ijbet.2011.043295 · Cited by 4
Precentral and cerebellar atrophic changes in moyamoya disease using 7-T magnetic resonance imaging
Moon, H. C., Oh, B. H., Cheong, C., Kim, W. S., Min, K. S., Kim, Y. G., & Park, Y. S. (2019) · Acta Radiologica · doi:10.1177/0284185119866808 · Cited by 4
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Background
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Cummins, T., Xia, Y., Elias, A., Lamb, F., Pannek, K., Doré, V., Bourgeat, P., Salvado, O., Fripp, J., Hopwood, M., et al. (2021) · GLOBAL PSYCHIATRY ARCHIVES · doi:10.52095/gp.2021.8112 · Cited by 4
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Li, W., Sun, L., Yue, L., & Xiao, S. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.11.032 · Cited by 4
Use of Magnetic Resonance Imaging to Identify Mild Cognitive Impairment: Who Should Be Imaged?
Apostolova, L. G. (2008) · CNS Spectrums · doi:10.1017/s1092852900026997 · Cited by 4
Neuroimaging Findings in Mild Cognitive Impairment
Massa, F., Bauckneht, M., Peira, E., Lapucci, C., Picco, A., Capitanio, S., Arnaldi, D., Roccatagliata, L., Chincarini, A., & Nobili, F. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-53168-3_12 · Cited by 4
'Generation Next' in Alzheimer disease genetic studies
Apostolova, L. G. (2013) · Nature Reviews Neurology · doi:10.1038/nrneurol.2013.133 · Cited by 4
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Lee, J.-D., Su, S.-C., Huang, C., Xu, W.-C., & Wei, Y.-Y. (2009) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2009.373 · Cited by 4
“As a result, by measuring the ventricular enlargement, hemispheric atrophy rate shows higher correlation than medial temporal lobe atrophy rates on cognitive tests [6], and reveals significant variation between normal individuals and the subjects with mild cognitive impairment and AD [3].”
Background
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Wang, L., Thompson, P. M., & Zhu, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20351-1_51 · Cited by 4
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Imms, P. E., Chaudhari, N. N., Chowdhury, N. F., Wang, H., Yu, X., Amgalan, A., & Andrei Irimia, A. (2024) · GeroScience · doi:10.1007/s11357-024-01310-0 · Cited by 3
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Lim, S.-H. (2021) · Journal of the Korean Chemical Society · doi:10.15207/jkcs.2021.12.2.327 · Cited by 3
Dementias
Apostolova, L. G., DeKosky, S. T., & Cummings, J. L. (2012) · Elsevier eBooks · doi:10.1016/b978-1-4377-0434-1.00091-8 · Cited by 3
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Liu, Y., Tsai, T.-S., Li, Y., Peng, J.-H., Chang, H., Peng, H., Lee, Y.-C., Lee, T.-Y., Liou, C.-H., Lin, T.-F., et al. (2024) · European Radiology Experimental · doi:10.1186/s41747-023-00414-0 · Cited by 3
“…imaging (MRI) has been recognized to provide quantitative imaging biomarkers [1] by measuring tissue characteristics and function, including but not limited to brain volume [2], tumor volume [3], functional network connectivity [4], fat content (FC) [5], and diffusivity of tissue water [6].”
Background
Joint and Long Short-Term Memory Regression of Clinical Scores for Alzheimer’s Disease Using Longitudinal Data
Yang, M., Elazab, A., Yang, P., Xia, Z., Wang, T., & Lei, B. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857827 · Cited by 3
“who studied the changing of hippocampus, entorhinal cortex, whole brain, and ventricles for AD diagnosis [8].”
Background
Neuroimaging in Alzheimer Disease
Kantarci, K., & Jack, C. R. (2006) · Springer eBooks · doi:10.1007/0-387-31216-1_8 · Cited by 3
Self-citation
Functional MRI Studies of Memory in Aging, Mild Cognitive Impairment, and Alzheimer’s Disease
Risacher, S. L., Wishart, H. A., & Saykin, A. J. (2011) · Springer eBooks · doi:10.1007/978-1-4419-0345-7_24 · Cited by 3
Currently Available Neuroimaging Approaches in Alzheimer Disease (AD) Early Diagnosis
Ortiz-Terán, L., Juan, M. R., Martin, M. D. L. N. C., & Ortiz, T. (2011) · InTech eBooks · doi:10.5772/17012 · Cited by 3
“Moreover, other studies have shown that not only mesial temporal lobe regions show greater atrophy linked with progression towards AD, but also some cortical structures, such as posterior cingulum, precunneus or temporal medial lobe (Jack et al, 2004; Jack et al, 2005; Chetelat et al, 2005).”
Background
Combination of Multiple Features in Support Vector Machine with Principal Component Analysis in Application for Alzheimer’s Disease Diagnosis
Lee, J.-D., Su, S.-C., Huang, C., Wang, J., Xu, W.-C., Wei, Y.-Y., & Lee, S. T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-10684-2_57 · Cited by 3
Issues for Clinical Drug Development in Neurodegenerative Diseases
Dib, M. (2005) · Drugs · doi:10.2165/00003495-200565170-00004 · Cited by 3
“disease progression, through serial MRI measures.[31,32] In theory, this could be used to assess the”
Method
Biochemical and Radiological Markers of Alzheimer’s Disease Progression
Dziedzic, T., Pera, J., Klimkowicz‐Mrowiec, A., Mroczko, B., & Słowik, A. (2016) · Journal of Alzheimer s Disease · doi:10.3233/ifs-150578 · Cited by 3
“Four studies demonstrated that whole brain atrophy rate was associated out of 6 studies, ventricular expansion rate correlated with cognitive decline [80, 82, 84–86], whereas one study failed to ﬁnd such a relationship [87].”
Alzheimer's disease or A lzheimer's syndrome?: a longitudinal computed tomography neuroradiological follow‐up study of 56 cases diagnosed clinically as A lzheimer's disease
Fujisawa, K., Tsunoda, S., Hino, H., Shibuya, K., Takeda, A., & Aoki, N. (2015) · Psychogeriatrics · doi:10.1111/psyg.12162 · Cited by 3
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Whitwell, J. L., & Vemuri, P. (2010) · Methods in molecular biology · doi:10.1007/978-1-61737-992-5_27 · Cited by 3
A fuzzy membership based comparison of the grey matter (GM) in cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) using brain images
Hazarika, R. A., Maji, A. K., Sur, S. N., Olariu, I., & Kandar, D. (2022) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-219279 · Cited by 3
A Clinical Perspective of Mild Cognitive Impairment: What Radiologists Should Know
Duara, R., Loewenstein, D., Barker, W., Isaacson, R., & Greig‐Custo, M. T. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.010 · Cited by 3
A putative role for PCSK9 in synaptic remodelling and plasticity in response to brain injury: implications for Alzheimer's disease
Jasmin, S. B. (2011) · eScholarship@McGill (McGill) · doi:10.82308/52384 · Cited by 3
MRI-Derived AD Signature of Cortical Thinning and Plasma P-Tau217 for Predicting Alzheimer Dementia Among Community-Dwelling Older Adults
Yu, L., Wang, T., Hansson, O., Janelidze, S., Lamar, M., Arfanakis, K., Bennett, D. A., Schneider, J. A., & Boyle, P. A. (2024) · Neurology Clinical Practice · doi:10.1212/cpj.0000000000200291 · Cited by 2
Neuromodulation Interventions for Language Deficits in Alzheimer’s Disease: Update on Current Practice and Future Developments
Chen, F., Nie, Y., & Chen, K. (2025) · Brain Sciences · doi:10.3390/brainsci15070754 · Cited by 4
“AD patients often experience vascular comorbidities [81,82] or brain atrophy [83,84], which can alter the safety profile of neuromodulation.”
Hippocampal Atrophy in Alzheimer’s Disease
Soininen, H. S., Liu, Y., Rueckert, D., & Lötjönen, J. M. P. (2012) · Neurodegenerative Disease Management · doi:10.2217/nmt.12.13 · Cited by 2
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Li, L., Wu, Y., Wu, J., Li, B., Hua, R., Shi, F., Chen, L., & Wu, Y. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1555054 · Cited by 2
“Atrophy of the medial temporal lobe or hippocampus, as observed on MRI, is the most well-established neurodegenerative biomarker of AD (Jack et al., 2004).”
Chapter 3 Mild Cognitive Impairment
Jicha, G. A., & Petersen, R. C. (2007) · Blue books of neurology · doi:10.1016/s1877-184x(09)70054-1 · Cited by 3
Self-citation
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Chou, Y., Leporé, N., Saharan, P., Madsen, S. K., Hua, X., Jack, C. R., Shaw, L. M. J., Trojanowski, J. Q., Weiner, M. W., Toga, A. W., et al. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490368 · Cited by 2
Self-citation
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Wolz, R., Heckemann, R. A., Aljabar, P., Hajnal, J. V., Hammers, A., Lötjönen, J. M. P., & Rueckert, D. (2010) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2010.5490147 · Cited by 2
“Index Terms— structural MR images, Alzheimer’s disease, hippocampal atrophy, graph-cuts”
Background
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Prodromou, E., Leandrou, S., Schiza, E. C., Neocleous, K., Matsangidou, M., & Pattichis, C. S. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00135 · Cited by 2
“Atrophy initially affects Medial Temporal Lobe (MTL) [15], [16] a region which includes anatomically related structures that are essential for declarative memory [17].”
Background
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Seo, H., Brand, L., Wang, H., & Nie, F. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16130 · Cited by 2
“For example, studies (Carmichael et al. 2007; Jack et al. 2004) have shown that ventricular volume, and its rate of change, is related to the vulnerability to cognitive decline and dementia.”
Result
NORMAL PRESSURE HYDROCEPHALUS
Graff‐Radford, N. R. (2007) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/01.con.0000267239.92250.d8 · Cited by 2
“In the Katzman and Hussey (1970) test, fluid was infused through an LP needle into the CSF at a known rate until a steady state pressure was reached.”
Method
Physiological changes and clinical effects of aging
Dikmeer, A., & Cankurtaran, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-98804-9.00006-2 · Cited by 2
Functional MRI Studies of Memory in Aging, Mild Cognitive Impairment, and Alzheimer’s Disease
Zhu, J. K., Risacher, S. L., Wishart, H. A., & Saykin, A. J. (2023) · Springer eBooks · doi:10.1007/978-3-031-10909-6_30 · Cited by 2
Structural and Functional Magnetic Resonance Imaging
Lockau, H., Jessen, F., Fellgiebel, A., & Drzezga, A. E. (2013) · PET Clinics · doi:10.1016/j.cpet.2013.08.004 · Cited by 2
MRI in Dementia
Schmidt, R., Havas, D., Ropele, S., Enzinger, C., & Fazekas, F. (2009) · Magnetic Resonance Imaging Clinics of North America · doi:10.1016/j.mric.2009.09.009 · Cited by 2
19 MRI as a Surrogate Marker in Clinical Trials in Alzheimer’s Disease
Jessen, F., & Hampel, H. J. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195328875.003.0019 · Cited by 2
The Effect of Spinal Cord Injury on Beta-Amyloid Plaque Pathology in TgCRND8 Mouse Model of Alzheimer’s Disease
Yuan, Q., Yang, J., Xian, Y., Liu, R., Chan, C. W., Wu, W., & Lin, Z. (2020) · Current Alzheimer Research · doi:10.2174/1567205017666200807191447 · Cited by 2
Correlation Between Cerebral Atrophy and Texture Features in Alzheimer-type Dementia Brains: A 3-Year Follow-up MRI Study
Kodama, N., & Takeuchi, H. (2005) · IEEJ Transactions on Electronics Information and Systems · doi:10.1541/ieejeiss.125.1382 · Cited by 2
Pathobiology of Alzheimer’s disease and biomarker development.
Arai, H., Okamura, N., Waragai, M., Furukawa, K., Yanai, K., & Kudo, Y. (2010) · Folia Pharmacologica Japonica · doi:10.1254/fpj.135.3 · Cited by 2
Thin skin
Rottenberg, D. A. (2008) · Neurology · doi:10.1212/01.wnl.0000341312.49465.f9 · Cited by 2
La disminución volumétrica del núcleo caudado derecho como fenotipo neuroanatómico del trastorno por déficit de atención con hiperactividad pediátrico. Un análisis morfométrico fronto-caudado por resonancia magnética estructural
Vila, S., & Carlos, J. (2007) · TDX (Tesis Doctorals en Xarxa) · Cited by 2
Cerebrovascular Challenges in Diabetic Patients
Barnes, J. N., & Joyner, M. J. (2011) · Hypertension · doi:10.1161/hypertensionaha.110.168070 · Cited by 2
Cognitive test completion time as an indicator of early brain atrophy: Findings from the Cognitive Assessment for Dementia, iPad version 2 (CADi2) in healthy adults
Iwasa, K., Takamura, M., Ishida, M., Kuroda, Y., Abe, S., & Nagai, A. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251400746 · Cited by 1
“10 These modalities therefore provide complementary yet distinct information.”
Cardiovascular Risk Indices Differentially Linked to Hippocampal Subfield Volumes: A Comparison Between Younger and Older Adults
Homayouni, R., Saifullah, S., Sutton, B. P., & Daugherty, A. M. (2026) · Hippocampus · doi:10.1002/hipo.70082 · Cited by 1
“Hippocampal volume significantly declines during typical and pathological aging, often preceding memory impairment in the ensuing years (Jack et al. 1998, 2004; Schuff et al. 2009; Smith et al. 2007; Tondelli et al. 2012).”
Canine Cognitive Dysfunction and Alzheimer’s Disease: Pathophysiological Relationships and the Impact of Glymphatic System Impairment on Neurodegeneration
Dondi, M., Bianchi, E., Borghetti, P., Lecce, R. D., Gnudi, G., Guarnieri, C., Buffagni, V., Ravanetti, F., Saleri, R., & Corradi, A. (2026) · Veterinary Sciences · doi:10.3390/vetsci13030298 · Cited by 1
Progression of Cerebral Atrophy in Alzheimer's Disease and Its Consequences on Traumatic Brain Injury: Insights From Longitudinal MRI Studies
Abdi, H., Sánchez‐Molina, D., Vilana, S. G., Azizi, S., & Rahimi-Movaghar, V. (2026) · International Journal for Numerical Methods in Biomedical Engineering · doi:10.1002/cnm.70180 · Cited by 1
Place cell physiology in a transgenic mouse model of Alzheimer's disease
Yi, M. (2009) · Cited by 1
Chapter 15 Advances in Neuroimaging
Scheltens, P. (2007) · Blue books of neurology · doi:10.1016/s1877-184x(09)70066-8 · Cited by 1
Automated Hippocampal Segmentation and Mapping Reveals Genetically Accelerated Tissue Loss in 1-year Repeat MRI data from 490 Alzheimer's Disease, MCI, and Control Subjects
Morra, J. H., Zhuowen Tu, A., Apostolova, L. G., Green, A., Avedissian, C., Madsen, S., Parikshak, N., Toga, A. W., Jack, C. R., Schuff, N. W., et al. (2009) · NeuroImage · doi:10.1016/s1053-8119(09)71153-x · Cited by 1
Self-citation
Sleep Pattern: Preventing Factors for Alzheimer Disease
Khorgami, Z., & Sugaya, K. (2015) · Journal of Advances in Medicine and Medical Research · doi:10.9734/bjmmr/2015/19333 · Cited by 1
Translational MRI in CNS Drug Discovery
von Kienlin, M., & Risterucci, C. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0134-7_14 · Cited by 1
Ausgewählte altersbedingte Erkrankungen
Behl, C., & Ziegler, C. (2015) · Springer eBooks · doi:10.1007/978-3-662-48250-6_4 · Cited by 1
Advances in Alzheimer’s Disease Management
Gauthier, S. G., & Rosa‐Neto, P. (2012) · doi:10.2217/9781780840840 · Cited by 1
Neurodegenerative disorders
Pietropaolo, S., Sluyter, F., & Crusio, W. E. (2014) · Cambridge University Press eBooks · doi:10.1017/cbo9781107360556.030 · Cited by 1
Using fMRI to study cognitive function and its modulation in sleep-deprived persons: a selective overview
Chee, M. W. L., & Lee, S. M. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9780511783005.004 · Cited by 1
A Lewy-testes demencia klinikai és neuropatológiai jellemzői
Bencze, J., Simon, V., Bereczki, E., Majer, R. L., Varkoly, G., Murnyák, B., Kálmán, J., & Hortobágyi, T. (2017) · Orvosi Hetilap · doi:10.1556/650.2017.30735 · Cited by 2
Brain Imaging in Type 2 Diabetes
Moran, C., Beare, R. J., Phan, T. G., & Srikanth, V. K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-809454-9.00004-4 · Cited by 1
Intranasal Insulin as Promising Therapy for Preserving Pragmatic Competence in MCI and AD
Schatz, S. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.90725 · Cited by 1
“[50] argue that hemispheric atrophy rates, measured by ventricular enlargement, correlate more strongly with changes on cognitive tests than medial temporal lobe (MTL) atrophy rates and capture significant variation between subjects with MCI and AD [37, 39].”
Effects of Long Term Consumption of High Calorie Diet on Neurological Disorders
Farooqui, A. A. (2015) · Springer eBooks · doi:10.1007/978-3-319-15254-7_9 · Cited by 1
Epigenetic and non-epigenetic mechanisms in the accelerated cellular aging in late-onset Alzheimer’s disease
Rawat, K., & Garlapally, P. (2023) · Exploration of Neuroprotective Therapy · doi:10.37349/ent.2023.00040 · Cited by 1
Relationship Between Cholesterol Metabolism, Apoe and Brain Volumes In Alzheimer‘s Disease
Leoni, V., & Caccia, C. (2011) · Future Neurology · doi:10.2217/fnl.11.38 · Cited by 1
“In some studies, an increased rate of hippocampal and whole-brain atrophy in APOE e4 carriers has been reported in nondemented individuals, as well as in MCI and AD patients [49–57].”
Background
Independent component analysis based assessment of linked gray and white matter in the initial stages of Alzheimer's disease using structural MRI phase images
Tufail, A. B., Rizvi, S. A. R., Siddiqui, A. M., Younis, M. S., & Abidi, A. I. (2013) · International Computer Science and Engineering Conference · doi:10.1109/icsec.2013.6694804 · Cited by 1
“Several studies have shown the effectiveness of structural magnetic resonance imaging (sMRI) scans in successful diagnosis of patholological changes associated with temporal lobar structures [2], ascertaining hippocampal volume as susceptible biomarker for AD related pathological changes [3]–[5], as well as differenti…”
Background
Correlation between hippocampal volumes and proton magnetic resonance spectroscopy of the posterior cingulate gyrus and hippocampi in Alzheimer's disease
Park, E. J., Lyra, K., Lee, H. W., Caramelli, P., Otaduy, M. C. G., & Leite, C. C. (2010) · Dementia & Neuropsychologia · doi:10.1590/s1980-57642010dn40200006 · Cited by 1
“Hippocampal volume loss is found in AD patients and correlates with dementia severity and neuropathologic changes.(4,5) However, hippocampal atrophy is not specific to AD also occurring in normal aging.”
Background
Predictive Factors for Decline in Activities of Daily Living in Alzheimer's Disease Dementia with More than 2 Follow-up
Kim, S.-H., Yang, H., Choi, Y. J., Kang, H. J., Choi, K., & Jeong, J. H. (2013) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2013.12.4.100 · Cited by 1
“내측두엽위축은 T1-축영상 내측두엽위축 시각척도를 이용하여 각각 오른쪽, 왼쪽을 측정하였 다[22, 23].”
Clinical Utility of Resting State Functional MRI
McAndrews, M. P., & Barnett, A. J. (2018) · Contemporary clinical neuroscience · doi:10.1007/978-3-319-78926-2_3 · Cited by 1
Adaptive ROI search for 3D visualization of MRI medical images
Grandhe, P., Edara, S. R., & Devara, V. (2017) · International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) · doi:10.1109/icecds.2017.8390171 · Cited by 1
“The ensuing signal constitute the Automatic hierarchical annular histogram (AHAH).[1,3,5]”
Background
Mild Cognitive Impairment: Diagnosis and Treatment
Nina-Estrella, R. E. (2018) · Springer eBooks · doi:10.1007/978-3-319-95360-1_26 · Cited by 1
CSF biomarkers in dementia: longitudinal aspects and combination with MRI
Bouwman, F. H. (2008) · Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) · Cited by 1
Treatment of Alzheimer's disease:
Shimohama, S. (2008) · Folia Pharmacologica Japonica · doi:10.1254/fpj.131.351 · Cited by 1
Longitudinal Measurement of Ventricular Volume Gain in the Healthy Old
McManus, M., Sunderland, T., & Cohen, R. M. (2009) · Brain Imaging and Behavior · doi:10.1007/s11682-009-9079-y · Cited by 1
“…temporal lobe structures may be smaller and rates of medial temporal lobe atrophy may be increased in patients with mild cognitive impairment (MCI), a state associated with an increased risk of developing AD, and may even precede the development of MCI (Jack et al. 2004; Rusinek et al. 2003).”
BackgroundInfluential

Authors: Jack, C. R., Shiung, M. M., Gunter, J. L., O’Brien, P. C., Weigand, S. D., Knopman, D. S., Boeve, B. F., Ivnik, R. J., Smith, G. E., Cha, R. H., Tangalos, E. G., & Petersen, R. C.
Affiliations: Mayo Clinic (US)
ORCID: Jack, C. R.
Gunter, J. L.
O’Brien, P. C.
Weigand, S. D.
Knopman, D. S.
Boeve, B. F.
Smith, G. E.
Petersen, R. C.
Venue: Neurology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2004-02-24
Volume: 62
Issue: 4
Pages: 591-600
ISSN: 0028-3878 (ISSN-L)
1526-632X
DOI: 10.1212/01.wnl.0000110315.26026.ef
PMC: PMC2730165
OpenAlex: W2005133023
Cited by: 827
References: 41
Citations by year: 2026: 17 · 2025: 19 · 2024: 21 · 2023: 25 · 2022: 20 · 2021: 36 · 2020: 27 · 2019: 38 · 2018: 36 · 2017: 37 · 2016: 33 · 2015: 39 · 2014: 41 · 2013: 48 · 2012: 38
FWCI: 28.59
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Neurology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute on Aging (AG-06786, AG 16574, U01AG006786, R01AG011378, AG-11378, U19AG010483, R37AG011378, P50AG016574)
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: Alzheimer's disease, brain atrophy rate, serial MRI, hippocampal atrophy, entorhinal cortex atrophy, whole brain atrophy, ventriculomegaly, mild cognitive impairment, disease progression, surrogate markers, cognitive decline, trial size, MCI conversion
MeSH terms: Aged, Aged, 80 and over, Alzheimer Disease, Apolipoproteins E, Atrophy, Brain, Cognition Disorders, Female, Hippocampus, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Cohort Studies, Disease Progression, Entorhinal Cortex, Apolipoprotein E4

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Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD

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