The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease
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This paper examines morphometry of MRI biomarkers derived from the network of temporal lobe structures including the amygdala, entorhinal cortex and hippocampus in subjects with preclinical Alzheimer's disease (AD). Based on template-centered population analysis, it is demonstrated that the structural markers of the amygdala, hippocampus and entorhinal cortex are statistically significantly different between controls and those with preclinical AD. Entorhinal cortex is the most strongly significant based on the linear effects model (p < .0001) for the high-dimensional vertex- and Laplacian-based markers corresponding to localized atrophy. The hippocampus also shows significant localized high-dimensional change (p < .0025) and the amygdala demonstrates more global change signaled by the strength of the low-dimensional volume markers. The analysis of the three structures also demonstrates that the volume measures are only weakly discriminating between preclinical and control groups, with the average atrophy rates of the volume of the entorhinal cortex higher than amygdala and hippocampus. The entorhinal cortex thickness also exhibits an atrophy rate nearly a factor of two higher in the ApoE4 positive group relative to the ApoE4 negative group providing weak discrimination between the two groups.
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Entorhinal cortex undergoes accelerated atrophy in preclinical AD.
“The regional specificity of these findings aligns with evidence that entorhinal cortex is a site of preclinical neuropathological tau deposition that is elevated for APOE4 carriers [37] and predicts cognitive decline [38], and which undergoes accelerated atrophy in preclinical AD [39].”
From Effects of APOE2 and APOE4 on brain microstructure in older adults: modification by age, sex, and cognitive status (2024) · cites this paper as [39]
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Self-citationPettigrew, C. A., Soldan, A., Zhu, Y., Cai, Q., Wang, M., Moghekar, A., Miller, M. I., Singh, B., Martinez, O., Fletcher, E. M., et al. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.12.003 · Cited by 47 · see citing works
Self-citationTang, X., Initiative, A. D. N., Holland, D., Dale, A. M., Laurent Younes, B., & Miller, M. I. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-141605 · Cited by 43 · see citing works
Self-citationTang, X., Initiative, A. D. N., Holland, D., Dale, A. M., & Miller, M. I. (2015) · Journal of Alzheimer s Disease · doi:10.3233/jad-150262 · Cited by 42 · see citing works
Self-citationProgressive medial temporal lobe atrophy during preclinical Alzheimer's disease
Pettigrew, C. A., Soldan, A., Sloane, K. L., Cai, Q., Wang, J., Wang, M., Moghekar, A., Miller, M. I., & Albert, M. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.08.022 · Cited by 39 · see citing works
Self-citationSchäfer, A., Chaggar, P., Thompson, T., Goriely, A., Ellen Kuhl, A., & Initiative, F. T. A. D. N. (2021) · Brain Multiphysics · doi:10.1016/j.brain.2021.100039 · Cited by 39 · see citing works
Stouffer, K. M., Chen, C., Kulason, S., Xu, E., Witter, M. P., Ceritoglu, C., Albert, M., Mori, S., Troncoso, J. C., Tward, D. J., et al. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103374 · Cited by 35 · see citing works
Self-citationRelationship between Brain Age-Related Reduction in Gray Matter and Educational Attainment
Rzezak, P., Squarzoni, P., Duran, F., Alves, T. C. T. F., Tamashiro-Duran, J. H., Bottino, C. M. C., Ribeiz, S. R. I., Lotufo, P. A., Menezes, P. R., Scazufca, M., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140945 · Cited by 34 · see citing works
Tward, D. J., Sicat, C. S., Brown, T., Bakker, A., Gallagher, M., Albert, M., Miller, M. I., & Initiative, A. D. N. (2017) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.07.005 · Cited by 34 · see citing works
Self-citationRegional subcortical shape analysis in premanifest Huntington's disease
Tang, X., Ross, C. A., Johnson, H. J., Paulsen, J. S., Laurent Younes, B., Albin, R. L., Ratnanather, J. T., & Miller, M. I. (2018) · Human Brain Mapping · doi:10.1002/hbm.24456 · Cited by 34 · see citing works
Self-citationLaczó, M., Martinkovič, L., Lerch, O., Wiener, J. M., Kalinová, J., Matušková, V., Nedelská, Z., Vyhnálek, M., Hort, J., & Laczó, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.886778 · Cited by 33 · see citing works
AD risk score for the early phases of disease based on unsupervised machine learning
Wang, Z., Tang, Z., Zhu, Y., Pettigrew, C. A., Soldan, A., Gross, A. L., & Albert, M. (2020) · Alzheimer s & Dementia · doi:10.1002/alz.12140 · Cited by 31 · see citing works
Self-citationJové, M., Mota‐Martorell, N., Torres, P., Ayala, V., Portero‐Otín, M., Ferrer, I., & Pamplona, R. (2021) · Life · doi:10.3390/life11050388 · Cited by 30 · see citing works
Ma, X., Li, Z., Jing, B., Liu, H., Li, D., Li, J., & Initiative, T. A. D. N. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00243 · Cited by 31 · see citing works
Wu, D., Faria, A. V., Laurent Younes, B., Mori, S., Brown, T., Johnson, H. J., Paulsen, J. S., Ross, C. A., Miller, M. I., & the PREDICT‐HD Investigators and Coordinators of the Huntington Study Group (2017) · Human Brain Mapping · doi:10.1002/hbm.23713 · Cited by 29 · see citing works
Self-citationReduced Lexical Access to Verbs in Individuals With Subjective Cognitive Decline
Macoir, J., Lafay, A., & Hudon, C. (2018) · American Journal of Alzheimer s Disease & Other Dementias® · doi:10.1177/1533317518790541 · Cited by 26 · see citing works
Risk of early-onset dementia among persons with tinnitus: a retrospective case–control study
Cheng, Y., Xirasagar, S., Yang, T.-H., Wu, C.-S., Kao, Y., & Lin, H. (2021) · Scientific Reports · doi:10.1038/s41598-021-92802-y · Cited by 21 · see citing works
High-throughput neuro-imaging informatics
Miller, M. I., Faria, A. V., Oishi, K., & Mori, S. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00031 · Cited by 21 · see citing works
Self-citationPlatero, C. A., & Tobar, M. C. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108698 · Cited by 21 · see citing works
Prediction of rapid amyloid and phosphorylated‐Tau accumulation in cognitively healthy individuals
Koychev, I. G., Vaci, N., Bilgel, M., An, Y., Terrera, G. M., Wong, D. F., Gallacher, J., Mogekhar, A., Albert, M., & Resnick, S. M. (2020) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12019 · Cited by 21 · see citing works
Self-citationReas, E. T., Triebswetter, C., Banks, S. J., & McEvoy, L. K. (2024) · Alzheimer's Research & Therapy · doi:10.1186/s13195-023-01380-w · Cited by 20 · see citing works
“The regional specificity of these findings aligns with evidence that entorhinal cortex is a site of preclinical neuropathological tau deposition that is elevated for APOE4 carriers [37] and predicts cognitive decline [38], and which undergoes accelerated atrophy in preclinical AD [39].”
BackgroundTward, D. J., Miller, M. I., Trouvé, A., & Laurent Younes, B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2578317 · Cited by 19 · see citing works
Self-citationSacktor, N. C., Soldan, A., Grega, M. A., Farrington, L., Cai, Q., Wang, M., Gottesman, R. F., Turner, R. S., & Albert, M. (2017) · Alzheimer Disease & Associated Disorders · doi:10.1097/wad.0000000000000194 · Cited by 18 · see citing works
Self-citationTang, X., Chen, N., Zhang, S., Jones, J. A., Zhang, B., Li, J., Liu, P., & Liu, H. (2017) · Human Brain Mapping · doi:10.1002/hbm.23855 · Cited by 17 · see citing works
Jarholm, J. A., Bjørnerud, A., Dalaker, T. O., Akhavi, M. S., Kirsebom, B., Pålhaugen, L., Nordengen, K., Grøntvedt, G. R., Nakling, A. E., Kalheim, L. F., et al. (2023) · Journal of Alzheimer s Disease · doi:10.3233/jad-221274 · Cited by 16 · see citing works
Hotz, I., Deschwanden, P. F., Mérillat, S., & Jäncke, L. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120461 · Cited by 13 · see citing works
Offspring Study of Racial and Ethnic Disparities in Alzheimer’s Disease: Objectives and DesignPreprint
Manly, J. J., Rentería, M. A., Avila‐Rieger, J. F., Turney, I. C., Vonk, J. M. J., Lao, P. J., Šeblová, D., Fleurimont, J., Martinez, M. N., & Brickman, A. M. (2020) · doi:10.31234/osf.io/frbkj · Cited by 14 · see citing works
Jain, S., Tward, D. J., Lee, D. S., Kolasny, A., Brown, T., Ratnanather, J. T., Miller, M. I., & Laurent Younes, B. (2014) · Extreme Science and Engineering Discovery Environment · doi:10.1145/2616498.2616553 · Cited by 13 · see citing works
Self-citationAlm, K. H., Faria, A. V., Moghekar, A., Pettigrew, C. A., Soldan, A., Mori, S., Albert, M., & Bakker, A. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.11.011 · Cited by 13 · see citing works
Self-citationZhou, Y., Tan, C., Dong, W., Sun, H., Han, W. H., & Xu, Y. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00092 · Cited by 13 · see citing works
Cardiac-induced cerebral pulsatility, brain structure, and cognition in middle and older-aged adults
Kim, T., Kim, S.-Y., Agarwal, V., Cohen, A. D., Roush, R. E., Chang, Y., Cheng, Y., Snitz, B. E., Huppert, T. J., Bagic, A. I., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117956 · Cited by 12 · see citing works
Wei, Y., Huang, N., Liu, Y., Zhang, X., Wang, S., & Tang, X. (2021) · Current Alzheimer Research · doi:10.2174/1567205018666210218150223 · Cited by 10 · see citing works
Geodesic distance on a Grassmannian for monitoring the progression of Alzheimer's disease
Gui, L.-Y., Tang, X., & Moura, J. M. F. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.025 · Cited by 10 · see citing works
Miller, M. I., Tward, D. J., & Trouvé, A. (2022) · BME Frontiers · doi:10.34133/2022/9868673 · Cited by 10 · see citing works
Self-citationCong, S., Yao, X., Huang, Z., Risacher, S. L., Nho, K. T., Saykin, A. J., & Shen, L. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.07.005 · Cited by 9 · see citing works
Pan, N., Liu, S., Ge, X., Zheng, Y., & Initiative, A. D. N. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251314785 · Cited by 7 · see citing works
Tang, X., Kutten, K. S., Ceritoglu, C., Mori, S., & Miller, M. I. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2080830 · Cited by 6 · see citing works
Self-citationMi, M., Tward, D., & Trouvé, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.19.440540 · Cited by 6 · see citing works
Marco, M. D., Bocchetta, M., Venneri, A., & Initiative, A. D. N. (2023) · Brain Sciences · doi:10.3390/brainsci13050806 · Cited by 5 · see citing works
Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model
Kim, S., Kim, S.-W., Noh, Y., Lee, P. H., Na, D. L., Seo, S. W., & Seong, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.869387 · Cited by 5 · see citing works
Guan, D., Tian, L., Li, W., & Gao, H. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108218 · Cited by 4 · see citing works
IHDNA: Identical Hybrid Deep Neural Networks for Alzheimer's Detection using MRI Dataset
Singh, A. P., Upadhyay, N. R., Shankar, V. G., & Devi, B. (2023) · International Conference on Intelligent Communication and Computational Techniques (ICCT) · doi:10.1109/icct56969.2023.10075912 · Cited by 4 · see citing works
Censoring Distances Based on Labeled Cortical Distance Maps in Cortical Morphometry
Ceyhan, E., Nishino, T., Alexopolous, D., Todd, R. D., Botteron, K. N., Miller, M. I., & Ratnanather, J. T. (2013) · Frontiers in Neurology · doi:10.3389/fneur.2013.00155 · Cited by 4 · see citing works
Self-citationZhang, J., & Shi, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87234-2_67 · Cited by 3 · see citing works
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 · see citing works
Self-citationApplying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data
He, R., Tward, D. J., & Initiative, A. D. N. (2023) · Neuroinformatics · doi:10.1007/s12021-023-09634-6 · Cited by 1 · see citing works
Analysis of cortical morphometric variability using labeled cortical distance maps
Ceyhan, E., Nishino, T., Botteron, K. N., Miller, M. I., & Ratnanather, J. T. (2016) · Statistics and Its Interface · doi:10.4310/sii.2017.v10.n2.a13 · Cited by 1 · see citing works
Self-citationShape Diffeomorphometry of Brain Structures in Neurodegeneration and Neurodevelopment
Ratnanather, J. T., Liu, C.-F., & Miller, M. I. (2021) · Handbook of Neuroengineering · doi:10.1007/978-981-15-2848-4_85-1 · Cited by 1 · see citing works
Self-citationChange Point Estimation of the Hippocampal Volumes in Alzheimer's Disease
Tang, X., Albert, M., Miller, M. I., & Laurent Younes, B. (2016) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv.2016.13 · Cited by 1 · see citing works
Self-citationShape Diffeomorphometry of Brain Structures in Neurodegeneration and Neurodevelopment
Ratnanather, J. T., Liu, C.-F., & Miller, M. I. (2023) · Handbook of Neuroengineering · doi:10.1007/978-981-16-5540-1_85 · Cited by 1 · see citing works
Self-citationTIMELINE OF BRAIN ALTERATIONS IN ALZHEIMER’S DISEASE ACROSS THE ENTIRE LIFESPANPreprint
Pierrick Coupé, A., Manjón, J. V., Lanuza, E., Catheline, G., & Initiative, A. D. N. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/229195 · Cited by 1 · see citing works
Detecting prodromal Alzheimer’s disease with MRI through deep learningPreprint
Feng, X., Provenzano, F. A., Small, S. A., & Initiative, A. D. N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/813899 · Cited by 2 · see citing works
Nabizadeh, F., Rostami, M. R., Balabandian, M., & Ahmadi, N. (2020) · doi:10.21203/rs.3.rs-135997/v1 · Cited by 1 · see citing works
Neuroimaging Advances in Preclinical Alzheimers disease
Sheng, C., Huang, Y. M., Li, H., Sun, Y., Chen, Q., Ma, C., Liu, F., & Han, Y. (2018) · Neuropsychiatry · doi:10.4172/neuropsychiatry.1000508
Public Mental Health and the Brain Across the Life Span
Carlson, M. C., Moored, K., Rebok, G. W., & Eaton, W. W. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190916602.003.0011
Zhang, X., Caffo, B. S., Soldan, A., Pettigrew, C. A., Erus, G., Davatzikos, C. A., Morris, J. C., Benzinger, T. L. S., Johnson, S. C., Masters, C. L., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70205
Self-citationBarile, B., Chen, E., Grenier, T., & Sappey‐Marinier, D. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.109095
Wang, K., Zhu, Y., Albert, M., Miller, M. I., Davatzikos, C. A., Moghekar, A., Soldan, A., & Wang, M. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251393729
Self-citationReas, E. T., Triebswetter, C., Banks, S. J., & McEvoy, L. K. (2024) · Figshare · doi:10.6084/m9.figshare.24987857
Feng, X., Provenzano, F. A., & Small, S. A. (2022) · Figshare · doi:10.6084/m9.figshare.19451209
Altered hippocampal shape morphology in schizophrenia and at-risk mental state
Takayanagi, Y., Padova, D., Ceritoglu, C., Sasabayashi, D., Nishiyama, S., Kobayashi, H., Sakamoto, K., Takayanagi, M., Noguchi, K., Tsujii, N., et al. (2025) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2025.112130
Self-citation
- Authors
- Miller, M. I., Younes, L., Ratnanather, J. T., Brown, T., Trinh, H., Postell, E., Lee, D. S., Wang, M.-C., Mori, S., O'Brien, R., & Albert, M.
- Affiliations
- Johns Hopkins University (US)
- Venue
- NeuroImage: Clinical
- Publisher
- Elsevier BV
- Published
- 2013
- Volume
- 3
- Pages
- 352-360
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC3863771
- OpenAlex
- W2066648187
- Cited by
- 88
- References
- 58
- Citations by year
- 2025: 5 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 10 · 2020: 10 · 2019: 7 · 2018: 5 · 2017: 9 · 2016: 10 · 2015: 9 · 2014: 5 · 2013: 2
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
- PubMed Central (publishedVersion, cc-by-nc-sa)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (U01-AG03365, P50-AG005146, P41-RR015241)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- preclinical Alzheimer's disease, amygdala, entorhinal cortex, hippocampus, brain atrophy, MRI biomarkers, entorhinal cortex thickness, APOE ε4, hippocampal volume, amygdala volume
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