The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging
Abstract
Rates of hippocampal volume loss have been shown to distinguish subjects with dementia of the Alzheimer type (DAT) from nondemented controls. In this study, we obtained magnetic resonance scans in 18 subjects with very mild DAT (CDR 0.5) and 26 age-matched nondemented controls (CDR 0) 2 years apart. Large-deformation high-dimensional brain mapping was used to quantify and compare changes in hippocampal shape as well as volume in the two groups of subjects. Hippocampal volume loss over time was significantly greater in the CDR 0.5 subjects (left = 8.3%, right = 10.2%) than in the CDR 0 subjects (left = 4.0%, right = 5.5%) (ANOVA, F = 7.81, P = 0.0078). We used singular-value decomposition and logistic regression models to quantify hippocampal shape change across time within individuals, and this shape change in the CDR 0.5 and CDR 0 subjects was found to be significantly different (Wilks's lambda, P = 0.014). Further, at baseline, CDR 0.5 subjects, in comparison to CDR 0 subjects, showed inward deformation over 38% of the hippocampal surface; after 2 years this difference grew to 47%. Also, within the CDR 0 subjects, shape change between baseline and follow-up was largely confined to the head of the hippocampus and subiculum, while in the CDR 0.5 subjects, shape change involved the lateral body of the hippocampus as well as the head region and subiculum. These results suggest that different patterns of hippocampal shape change in time as well as different rates of hippocampal volume loss distinguish very mild DAT from healthy aging.
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
“Using a variety of neuroimaging approaches such as radial atrophy (Frisoni et al., 2006), large-deformation high-dimensional brain mapping (Wang et al., 2003), voxel-based morphometry (Chételat et al., 2008) or manual delineation (Mueller et al., 2010), the predominance of atrophy in CA1 (and to a lesser extent in the subiculum) in patients with clinical AD or aMCI has already been shown in vivo.”
From Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia (2013) · cites this paper as Wang et al., 2003
Citations
Cites
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
Self-citationAn Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business and Economic Statistics · doi:10.2307/1391399 · Cited by 9,179
(1982) · Trends in Neurosciences · doi:10.1016/0166-2236(82)90076-5 · Cited by 9,150
Introduction to Multivariate Statistical Analysis.
Madow, W. G., & Anderson, T. W. (1959) · American Mathematical Monthly · doi:10.2307/2308777 · Cited by 3,908
Calabrese, V. P. (1992) · JAMA · doi:10.1001/jama.1992.03490230111048 · Cited by 2,521
Deformable templates using large deformation kinematics
Christensen, G. E., Rabbitt, R. D., & Miller, M. I. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.536892 · Cited by 1,146
Self-citationMandelkow, E. (1998) · Trends in Cell Biology · doi:10.1016/s0962-8924(98)01368-3 · Cited by 980
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
Du, A. (2001) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.71.4.441 · Cited by 736
Clinical Dementia Rating (CDR)
Gauthier, S. G. (2001) · CRC Press eBooks · doi:10.3109/9780203213896-34 · Cited by 696
Cited by
Sorted by citations
Items 201–264 of 264
Willans, M., Hollings, A. L., Boseley, R. E., Munyard, T., Ellison, G., & Hackett, M. J. (2024) · Journal of Inorganic Biochemistry · doi:10.1016/j.jinorgbio.2024.112744 · Cited by 5
Langnes, E., Sneve, M. H., Sederevičius, D., Amlien, I. K., Walhovd, K. B., & Fjell, A. M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/564732 · Cited by 5
Suksuphew, S., & Horkaew, P. (2017) · Brain Sciences · doi:10.3390/brainsci7110155 · Cited by 4
Iida, K., Kagawa, K., Katagiri, M., Seyama, G., Hashizume, A., Abiko, M., Katayama, J., Suzuki, H., Kurisu, K., & Otsubo, H. (2019) · World Neurosurgery · doi:10.1016/j.wneu.2019.08.021 · Cited by 4
Hyperbolic Ricci Flow and Its Application in Studying Lateral Ventricle Morphometry
Shi, J., Thompson, P. M., & Wang, Y. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33530-3_6 · Cited by 4
Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data
Sfikas, G., & Nikou, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61188-4_15 · Cited by 4
Stephen, R. K., & Banu, J. F. (2023) · International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE) · doi:10.1109/rmkmate59243.2023.10369573 · Cited by 3
Zhang, L., Initiative, A. D. N., Fu, Y., Zhao, Z., Cong, Z., Zheng, W., Zhang, Q., Yao, Z., & Hu, B. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215568 · Cited by 3
Ceyhan, E., Beg, M. F., Ceritoglu, C., Wang, L., Morris, J. C., Csernansky, J. G., Miller, M. I., & Ratnanather, J. T. (2012) · Current Alzheimer Research · doi:10.2174/156720512803251138 · Cited by 3
Self-citationShape Deformation Statistics and Regional Texture-Based Appearance Models for Segmentation
Vicory, J. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/58as-6t88 · Cited by 3
Infinitesimal Drift Diffeomorphometry Models for Population Shape Analysis
Lee, B. C., Tward, D. J., Hu, Z., Trouvé, A., & Miller, M. I. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00439 · Cited by 3
Self-citationA novel diagnosis method utilizing MDBO-SVM and imaging genetics for Alzheimer's disease
Yu, X., Sheng, J., Zhang, Q., Song, Y., Wang, L., & Yang, Z. X. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102542 · Cited by 2
Self-citationPace, T., Levenstein, J. M., Quigley, B. L., Houston, R., Bouças, A. P., & Andrews, S. C. (2026) · Aging Cell · doi:10.1111/acel.70497 · Cited by 2
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
Dementia series 2: how dementia affects the ability to communicate
Nazarko, L. (2014) · British Journal of Healthcare Assistants · doi:10.12968/bjha.2014.8.12.614 · Cited by 2
Knijn, F. V., Verhoeff, M. C., Bindels, K. L., Fuh, J., Lin, C., Su, N., & Lobbezoo, F. (2023) · Journal of Oral Rehabilitation · doi:10.1111/joor.13591 · Cited by 2
Statistical Analysis for Recovery of Structure and Function from Brain Images
Wang, Y. M., Zhou, C., & Xi, J. (2011) · InTech eBooks · doi:10.5772/13494 · Cited by 2
Wang, Y., Zhang, J., Chan, T. F.-C., Toga, A. W., & Thompson, P. M. (2009) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2009.5193000 · Cited by 2
Somasundaram, K., & Genish, T. (2014) · International Conference on Electronics and Communication Systems (ICECS) · doi:10.1109/ecs.2014.6892666 · Cited by 2
Roldán-Valadéz, E., Corona-Cedillo, R., Cosme-Labarthe, J., & Martı́nez-López, M. (2007) · PubMed · Cited by 2
Saribudak, A., Subick, A. A., Rutta, J. A., & Uyar, M. Ü. (2016) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2975167.2991037 · Cited by 2
Computation of Pharmacologic Therapy Effects on Cognitive Abilities of Alzheimer’s Disease Patients
Saribudak, A., Subick, A. A., & Uyar, M. Ü. (2016) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2016.49 · 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
Functional and structural brain markers of Alzheimer’s disease : clinical studies using EEG and VBM
van Deursen, J. A. (2009) · Research Publications (Maastricht University) · doi:10.26481/dis.20090409jd · Cited by 2
On the analysis of cortical surface biomarkers
Gibson, E. (2009) · Summit (Simon Fraser University) · Cited by 2
Lower hippocampal volumes at baseline are associated with higher volume loss in healthy elderly
Schultz, V., Schmitz‐Koep, B., Menegaux, A., Thalhammer, M., Schramm, S., Kim, S. H., Zimmer, C., Sorg, C., Giannakopoulos, P., Montandon, M., et al. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1542857 · Cited by 1
Automatic segmentation of hippocampal substructures
Flores, G. G. S. (2012) · Cited by 1
de Florès, R., Joie, R. L., Landeau, B., Perrotin, A., Mézenge, F., Sayette, V. D. L., Eustache, F., Desgranges, B., & Chételat, G. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.05.098 · Cited by 1
Robust measurement of individual localized changes to the aging hippocampus
Xie, J., Fletcher, E., Singh, B., & Carmichael, O. T. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.12.007 · Cited by 2
Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker
Wang, G., Initiative, A. D. N., Zhou, W., Kong, D., Qu, Z., Ba, M., Hao, J., Yao, T., Dong, Q., Su, Y., et al. (2021) · Journal of Alzheimer s Disease · doi:10.3233/jad-215149 · Cited by 1
Improving brain registration and segmentation using anatomical guidance
KHAN, A. R. (2011) · Summit (Simon Fraser University) · Cited by 1
Epifanio, I., & Ventura‐Campos, N. (2008) · Contributions to statistics · doi:10.1007/978-3-7908-2062-1_25 · Cited by 1
Aktuelle Entwicklungen der strukturellen MRT zur Frühdiagnostik der Alzheimer-Demenz
Ewers, M., Teipel, S. J., & Hampel, H. J. (2005) · Nervenheilkunde · doi:10.1055/s-0038-1629942 · Cited by 1
¿Es la atrofia del hipocampo en la enfermedad de Parkinson un predictor de demencia?
Ibarretxe‐Bilbao, N., & Junqué, C. (2011) · Alzheimer Realidades e investigación en demencia · doi:10.5538/1137-1242.2011.49.5 · Cited by 1
10 Imaging Cognitive Decline in Aging: Predicting Decline with Structural Imaging
Kaye, J. A. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195328875.003.0010 · Cited by 1
Jauhiainen, A. M. (2008) · Cited by 1
The contribution of the hippocampus to language processing
Alamri, S. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 1
Brain shape regression components
Xie, J., & Carmichael, O. T. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346516
A path based approach to quantifying the progression of Alzheimer's disease
Kanel, P., & Liu, X. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359826
Structural Imaging of Drug Actions in Neurodegenerative Diseases
Mueller, S. G. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0134-7_12
12 Structural Imaging of Mild Cognitive Impairment
Jack, C. R. (2009) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195328875.003.0012
Segmentation of Subcortical Structures from Nonhuman Primate MRI
Liu, W. H.-T. (2006) · VTechWorks (Virginia Tech)
Image databases in medical applications
Koikkalainen, J. R. (2006) · Aaltodoc (Aalto University)
Cognitive Correlates of Anterolateral Entorhinal Cortex Volume Differences in Older Adults
Yeung, L. (2017) · TSpace (University of Toronto)
Is anxiety a risk factor in cognitive ageing?
Arthur, R. (2019) · ANU Open Research (Australian National University) · doi:10.25911/5d5148b663c66
Shape Analysis Methods for 3D Brain and Skull Imaging
Gutman, B. A. (2013) · eScholarship (California Digital Library)
Cohen, R. A., & Gullett, J. M. (2021) · Springer eBooks · doi:10.1007/978-3-030-22009-9_674
Lévêque, M. (2014) · Springer eBooks · doi:10.1007/978-3-319-01144-8_3
Imaging blood-brain barrier function in aging
Verheggen, I. C. M. (2021) · Research Publications (Maastricht University) · doi:10.26481/dis.20210601iv
Cohen, R. A., & Gullett, J. M. (2019) · Springer eBooks · doi:10.1007/978-3-319-69892-2_674-1
Current awareness in geriatric psychiatry
(2004) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.1063
Michallat-Bragg, G., Bennett, M., Flewitt, B. I., Kazmi, S., Smith, S. J., Wells, C. E., Hollins, A., Ash, C., Thwaites, S., Neil, W., et al. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1547834
Les interventions de psychochirurgie
Lévêque, M. (2013) · Springer eBooks · doi:10.1007/978-2-8178-0454-5_3
Automatic segmentation and shape analysis of human hippocampus in Alzheimer's disease
Shen, K. (2011) · theses.fr (ABES) · doi:10.70675/44a642b1z9128z4596z92f8z0f9767a16911
The reorganization of human brain networks in the early stages of multiple sclerosis
Koubiyr, I. (2019) · theses.fr (ABES) · doi:10.70675/feb6481czb227z4d3ezaf49zdeb3e31a6241
Cassim, S. (2020) · theses.fr (ABES) · doi:10.70675/4c5eee60z339ez457fz845dz81d55b62b4c6
Aderghal, K. (2021) · theses.fr (ABES) · doi:10.70675/b467dba8z5f34z4fafz8d11z5d8aa1f680d5 · Cited by 3
Willans, M., Ellison, G., Reeve, K., Innes, E. S., Boseley, R. E., Howard, D. L., James, S. A., & Hackett, M. J. (2026) · NeuroToxicology · doi:10.1016/j.neuro.2026.103578
Leveraging Preclinical Models to Understand Human Neurobiology and Behavior
Dearborn, J. T., Selmanovic, D., Chaturvedi, S. M., Sarafinovska, S., & Maloney, S. E. (2026) · Current topics in behavioral neurosciences · doi:10.1007/7854_2026_619
Neuronal changes in the hippocampus of post-stroke survivors
Gemmell, E. (2014)
Metadata
- Authors
- Wang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G.
- Affiliations
- Washington University in St. Louis (US)
- Johns Hopkins University (US)
- Corresponding author
- Wang, Lei
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2003-10
- Volume
- 20
- Issue
- 2
- Pages
- 667-682
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1976154578
- Cited by
- 263
- References
- 64
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 4 · 2021: 11 · 2020: 9 · 2019: 12 · 2018: 8 · 2017: 9 · 2016: 11 · 2015: 14 · 2014: 17 · 2013: 12 · 2012: 12
- FWCI
- 6.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- American Health Assistance Foundation
- University of Washington
- National Institutes of Health (1 P41 RR15241-01A1, AG 03991, MH 62130, MH/AG 60883)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, hippocampal atrophy, mild cognitive impairment, healthy aging, hippocampal subiculum, hippocampal head
- MeSH terms
- Aged, Aging, Algorithms, Alzheimer Disease, Female, Hippocampus, Humans, Image Processing, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Psychiatric Status Rating Scales, Regression Analysis, Sex Characteristics, Disease Progression
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
