Neuroimaging of mouse models of Alzheimer’s disease
Open accessAbstract
Magnetic resonance imaging (MRI) and positron emission tomography (PET) have made great strides in the diagnosis and our understanding of Alzheimer’s Disease (AD). Despite the knowledge gained from human studies, mouse models have and continue to play an important role in deciphering the cellular and molecular evolution of AD. MRI and PET are now being increasingly used to investigate neuroimaging features in mouse models and provide the basis for rapid translation to the clinical setting. Here, we provide an overview of the human MRI and PET imaging landscape as a prelude to an in-depth review of preclinical imaging in mice. A broad range of mouse models recapitulate certain aspects of the human AD, but no single model simulates the human disease spectrum. We focused on the two of the most popular mouse models, the 3xTg-AD and the 5xFAD models, and we summarized all known published MRI and PET imaging data, including contrasting findings. The goal of this review is to provide the reader with broad framework to guide future studies in existing and future mouse models of AD. We also highlight aspects of MRI and PET imaging that could be improved to increase rigor and reproducibility in future imaging studies.
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Age-related brain atrophy and myelin loss are observed on advanced high-field MRI in the 3×Tg-AD model.
“Alongside the progression of AD pathology, age-related brain atrophy and myelin loss are observed on advanced high-field MRI (Ni, 2021; Jullienne et al., 2022; Bergamino et al., 2023).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Jullienne et al., 2022
Section 4.2.1 reports MRI-observed cortical atrophy in 18-month-old 3xTg-AD mice, and the Summary describes volumetric declines with increasing age. But section 4.2.4 reports no changes in myelin staining in mice aged 11–17 months; it does not support the claimed myelin loss. The supplied passages do not establish that these findings were observed specifically on high-field MRI (page numbers unavailable). (LLM, conf=0.87)
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Striatum and entorhinal cortex atrophy in AD mouse models: MRI comprehensive analysis
Micotti, E., Paladini, A., Balducci, C., Tolomeo, D., Frasca, A., Marizzoni, M., Filibian, M., Caroli, A., Valbusa, G., Dix, S. L., et al. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.10.027 · Cited by 28 · see citing works
Piersson, A. D., Mohamad, M., Rajab, F., & Suppiah, S. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.06.006 · Cited by 28 · see citing works
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Algarzae, N. K., Hebron, M. L., Miessau, M., & Moussa, C. (2012) · Neuroscience · doi:10.1016/j.neuroscience.2012.08.057 · Cited by 27 · see citing works
Association of Amyloid Burden, Brain Atrophy and Memory Deficits in Aged Apolipoprotein ε4 Mice
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Modrego, P., & Fayed, N. (2011) · American Journal of Alzheimers Disease & Other Dementias · doi:10.1177/1533317511433809 · Cited by 26 · see citing works
Liang, S., Huang, J., Liu, W., Jin, H., Li, L., Zhang, X., Nie, B., Lin, R., Tao, J., Zhao, S., et al. (2017) · Behavioural Brain Research · doi:10.1016/j.bbr.2017.08.005 · Cited by 26 · see citing works
Imaging Transcriptomics in Neurodegenerative Diseases
Mroczek, M., Desouky, A., & Sirry, W. (2020) · Journal of Neuroimaging · doi:10.1111/jon.12827 · Cited by 26 · see citing works
Gray matter changes related to microglial activation in Alzheimer's disease
Nicastro, N., Malpetti, M., Mak, E., Williams, G. B., Bevan-Jones, W. R., Carter, S. F., Passamonti, L., Fryer, T., Hong, Y. T., Aigbirhio, F., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.010 · Cited by 25 · see citing works
Falangola, M. F., Nie, X., Ward, R., Dhiman, S., Voltin, J., Nietert, P. J., & Jensen, J. H. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.06.022 · Cited by 25 · see citing works
Ishibashi, K., Onishi, A., Fujiwara, Y., Ishiwata, K., & Ishii, K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181400 · Cited by 24 · see citing works
Esteras, N., Alquézar, C., Bartolomé, F. B., Antequera, D., Barrios, L., Carro, E., Cerdán, S., & Martín‐Requero, Á. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-111967 · Cited by 23 · see citing works
Forster, D. M., Davies, K. E., & Williams, S. R. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24349 · Cited by 23 · see citing works
Kuhla, A., Rühlmann, C., Lindner, T., Polei, S., Hadlich, S., Krause, B. J., Vollmar, B., & Teipel, S. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.009 · Cited by 23 · see citing works
Early Detection of Aβ Deposition in the 5xFAD Mouse by Amyloid PET
Oh, S. J., Lee, H., Kang, K. J., Han, S. J., Lee, Y. J., Lee, K. C., Lim, S. M., Yoon, D., Kim, K. M., Park, J. A., et al. (2018) · Contrast Media & Molecular Imaging · doi:10.1155/2018/5272014 · Cited by 23 · see citing works
Kastyak-Ibrahim, M. Z., Di Curzio, D. L., Buist, R., Herrera, S. L., Albensi, B. C., Del Bigio, M. R., & Martin, M. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.06.013 · Cited by 23 · see citing works
Guo, Y., Li, X., Zhang, M., Chen, N., Wu, S., Lei, J., Wang, Z., Wang, R., Wang, J., & Liu, H. (2019) · Molecular Medicine Reports · doi:10.3892/mmr.2019.9950 · Cited by 23 · see citing works
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PET Imaging in Huntington’s Disease
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Alterations of functional circuitry in aging brain and the impact of mutated APP expression
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Flagged first, then by citations
3 of the 232 references couldn’t be matched to a known paper.
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Alcohol Use Disorder and Dementia: A Review
Zahr, N. M. (2024) · Alcohol research · doi:10.35946/arcr.v44.1.03 · Cited by 38 · see citing works
Cortical cerebrovascular and metabolic perturbations in the 5xFAD mouse model of Alzheimer’s disease
Jullienne, A., Szu, J. I., Quan, R., Trinh, M. V., Norouzi, T., Noarbe, B. P., Bedwell, A. A., Eldridge, K., Persohn, S. A., Territo, P. R., et al. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1220036 · Cited by 30 · see citing works
Self-citationZeng, X., Cheung, S. K. K., Shi, M., Or, P. M. Y., Li, Z., Liu, J. Y. H., Ho, W. L.-H., Liu, T., Lu, K., Rudd, J. A., et al. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02970-z · Cited by 27 · see citing works
Andrade-Guerrero, J., Rodríguez-Arellano, P., Barron-Leon, N., Orta‐Salazar, E., Ledesma-Alonso, C., Díaz-Cintra, S. Y., & Soto-Rojas, L. O. (2023) · Cells · doi:10.3390/cells12212531 · Cited by 27 · see citing works
Progressive Vascular Abnormalities in the Aging 3xTg-AD Mouse Model of Alzheimer’s Disease
Jullienne, A., Quan, R., Szu, J. I., Trinh, M. V., Behringer, E. J., & Obenaus, A. (2022) · Biomedicines · doi:10.3390/biomedicines10081967 · Cited by 19 · see citing works
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Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging
Maharjan, S., Tsai, A. P., Lin, P. B., Ingraham, C., Jewett, M. R., Landreth, G. E., Oblak, A. L., & Wang, N. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.964654 · Cited by 11 · see citing works
“Different MRI techniques have been used to study the brain structure and function of 5xFAD mice (Mlynarik et al., 2012; Igarashi et al., 2020; Jullienne et al., 2022).”
MethodLiang, C., Nguyen, G. A. H., Danh, T. B., Sandhu, A. K., Melkonyan, L. L., Syed, A. U., & Mukherjee, J. (2023) · Synapse · doi:10.1002/syn.22265 · Cited by 11 · see citing works
Characterization of cerebrovascular changes in Alzheimer’s disease mice by photoacoustic imaging
Li, X., Zhang, Z., Shi, H., & Zhou, F. (2024) · Journal of Innovative Optical Health Sciences · doi:10.1142/s179354582450007x · Cited by 9 · see citing works
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Chicote‐González, A., Solana‐Balaguer, J., Garcia‐Segura, P., Campoy‐Campos, G., Pérez, I. P., Pérez‐Navarro, E., Rodríguez, M. J., Alberch, J., Giralt, A., Sòria, G., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70051 · Cited by 7 · see citing works
Wang, R., Li, J., Li, X., Guo, Y., Chen, P., & Tian, P. (2025) · Reviews in the Neurosciences · doi:10.1515/revneuro-2025-0013 · Cited by 7 · see citing works
WhatNIs N-ough for MRI-Based Animal Neuroimaging?
Grandjean, J., Lake, E. M. R., Pagani, M., & Mandino, F. (2024) · eNeuro · doi:10.1523/eneuro.0531-23.2024 · Cited by 5 · see citing works
Das, R. K., Sahoo, N., Roy, D., Nguyen, L., Rodrigo, H., Duttaroy, A. K., Fields, J. A., & Roy, U. (2026) · Journal of Neuroinflammation · doi:10.1186/s12974-026-03713-6 · Cited by 4 · see citing works
van den Broek, S. L., Sehlin, D., Andersen, J. V., Aldana, B. I., Beschörner, N., Nedergaard, M., Knudsen, G. M., Syvänen, S., & Herth, M. M. (2022) · Frontiers in Nuclear Medicine · doi:10.3389/fnume.2022.1001722 · Cited by 4 · see citing works
Sheppard, P. A. S., Oomen, C. A., Bussey, T. J., & Saksida, L. M. (2024) · eNeuro · doi:10.1523/eneuro.0120-24.2024 · Cited by 3 · see citing works
Afxenti, S., Bourdakou, M. M., Loizidou, E. M., Zachariou, M., Lambrianides, A., Pantzaris, M., & Spyrou, G. M. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2584129 · Cited by 3 · see citing works
Copper Peptides in Regenerative Aesthetic Dermatology
Sarbaziha, R., & Goldberg, D. J. (2026) · Dermatological Reviews · doi:10.1002/der2.70067 · Cited by 4 · see citing works
Riffo-Lepe, N., González-Sanmiguel, J., Armijo‐Weingart, L., Saavedra, P., Hernandez, D., Ramos, G., Martín, L. S., & Aguayo, L. G. (2025) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-24-01052 · Cited by 2 · see citing works
Raikes, A. C., Bhattrai, A., Wang, T., Wiegand, J. L., & Brinton, R. D. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1843319 · Cited by 2 · see citing works
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Special Issue “Alzheimer’s Disease—115 Years after Its Discovery”
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A relaxation-diffusion MRI dataset of aging mouse brains at 9.4 Tesla
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Exploring the Relation Between Aerobic Exercise, BDNF and Alzheimer’s Disease: A Research Protocol
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Gezginer, I., Karakatsani, M. E., Nanda, P., Chalasani, P., Kindler, D., Storz, R., Belau, M., Ni, R., Schratt, G. M., Deán‐Ben, X. L., et al. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-026-02109-1
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- Authors
- Jullienne, A., Trinh, M. V., & Obenaus, A.
- Affiliations
- University of California, Irvine (US)
- Department of Pediatrics, University of California, Irvine, CA 92697, USA
- Venue
- Biomedicines
- Publisher
- MDPI AG
- Published
- 2022-01-28
- Volume
- 10
- Issue
- 2
- Pages
- 305
- ISSN
- 2227-9059 (ISSN-L)
- PMC
- PMC8869427
- OpenAlex
- W4210462365
- Cited by
- 32
- References
- 232
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 6 · 2023: 6 · 2022: 4
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Biomedicines (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (AG054345-01, AG054349-01)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, mouse models of Alzheimer's disease, 3xTg-AD mice, 5xFAD mice, magnetic resonance imaging, positron emission tomography, imaging biomarkers
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.
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