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Hippocampal activation is associated with longitudinal amyloid accumulation and cognitive decline
Open accessAbstract
The amyloid hypothesis suggests that beta-amyloid (Aβ) deposition leads to alterations in neural function and ultimately to cognitive decline in Alzheimer’s disease. However, factors that underlie Aβ deposition are incompletely understood. One proposed model suggests that synaptic activity leads to increased Aβ deposition. More specifically, hyperactivity in the hippocampus may be detrimental and could be one factor that drives Aβ deposition. To test this model, we examined the relationship between hippocampal activity during a memory task using fMRI and subsequent longitudinal change in Aβ using PIB-PET imaging in cognitively normal older adults. We found that greater hippocampal activation at baseline was associated with increased Aβ accumulation. Furthermore, increasing Aβ accumulation mediated the influence of hippocampal activation on declining memory performance, demonstrating a crucial role of Aβ in linking hippocampal activation and memory. These findings support a model linking increased hippocampal activation to subsequent Aβ deposition and cognitive decline.
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![Figure 1. Model-derived individual slopes for amyloid and memory change over time. (A) [11C] Pittsburgh Compound-B distribution volume ratio slope (PIB slope) for each participant over time, relative to baseline MRI (x = 0), where black line indicates average PIB slope, blue lines indicate little/no change in PIB, and red lines indicate positive PIB slope. The horizontal dotted line indicates the criterion for a positive PIB scan (1.06), (B) California Verbal Learning Test Long-Delay Free Recall slope (CVLT slope) for each participant over time, relative to baseline MRI (x = 0), where black line indicates average CVLT slope, red lines indicate negative CVLT slope, and blue lines indicate positive CVLT slope. All slopes were obtained from the linear mixed model. The following source data (Figure 1—source data 1) is relevant for Figures 1–4. DOI: http://dx.doi.org/10.7554/eLife.22978.004](/paper-figures/6088ed594b9f426c8f93c7567cffe899/hippocampal-activation-is-associated-with-longitudinal-amyloid-accumulation-and-figure-1.jpg)
![Figure 2. Relationship between brain activation at baseline and longitudinal amyloid accumulation. (a) Positive relationship between hippocampal activation (contrast values for subsequent hits versus misses) at baseline and [11C] Pittsburgh Compound-B distribution volume ratio (DVR) slopes (PIB slope measured as PIB DVR values over time) obtained from the linear mixed model, (b) No relationship between non-hippocampal activation (contrast values for subsequent hits versus misses in inferior frontal cortex and occipital cortex) at baseline and [11C] Pittsburgh Compound-B distribution volume ratio (DVR) slopes (PIB slope measured as PIB DVR values over time) obtained from the linear mixed model. DOI: http://dx.doi.org/10.7554/eLife.22978.006](/paper-figures/e23515494bea46d4ba8cc742d940d8f1/hippocampal-activation-is-associated-with-longitudinal-amyloid-accumulation-and-figure-2.jpg)
![Figure 3. Relationship between longitudinal amyloid accumulation and memory decline. Negative relationship between [11C] Pittsburgh Compound-B distribution volume ratio slopes (PIB slope) and California Verbal Learning Test Long-Delay Free Recall slope (CVLT slope measured as CVLT scores over time) obtained from the linear mixed model. DOI: http://dx.doi.org/10.7554/eLife.22978.008](/paper-figures/ecb207ac988b44899be35916b0bf23a0/hippocampal-activation-is-associated-with-longitudinal-amyloid-accumulation-and-figure-3.jpg)
![Figure 4. Mediation analysis between hippocampal activation, longitudinal amyloid accumulation, and memory decline. Mediation analysis with hippocampal activation at baseline (predictor, X), mediated by [11C] Pittsburgh Compound-B distribution volume ratio slopes (PIB Slope) (mediator, M), predicting California Verbal Learning Test Long-Delay Free Recall slope (CVLT Slope). Hippocampal activation positively predicted PIB Slope, PIB Slope negatively predicted CVLT Slope, which resulted in a significant mediation (see text for more details). DOI: http://dx.doi.org/10.7554/eLife.22978.010](/paper-figures/0945bd640c2346f98eb17c8c64c616b0/hippocampal-activation-is-associated-with-longitudinal-amyloid-accumulation-and-figure-4.jpg)
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“and even predicts future cognitive decline in cognitively healthy elderly [178].”
From Apolipoprotein E4, inhibitory network dysfunction, and Alzheimer’s disease (2019) · cites this paper as [178]
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Cites
(2013) · Cited by 44,563
Amyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
Alzheimer's Disease Is a Synaptic Failure
Selkoe, D. J. (2002) · Science · doi:10.1126/science.1074069 · Cited by 4,420
Phases of Aβ-deposition in the human brain and its relevance for the development of AD
Thal, D. R., Rüb, U., Orantes, M., & Braak, H. (2002) · Neurology · doi:10.1212/wnl.58.12.1791 · Cited by 3,490
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5062-08.2009 · Cited by 2,862
Staging of alzheimer's disease-related neurofibrillary changes
Braak, H., & Braak, E. (1995) · Neurobiology of Aging · doi:10.1016/0197-4580(95)00021-6 · Cited by 2,357
Palop, J. J., Chin, J., Roberson, E. D., Wang, J., Thwin, M. T., Bien-Ly, N., Yoo, J., Ho, K. O., Yu, G.-Q., Kreitzer, A., et al. (2007) · Neuron · doi:10.1016/j.neuron.2007.07.025 · Cited by 1,653
APP Processing and Synaptic Function
Kamenetz, F., Tomita, T., Hsieh, H., Seabrook, G., Borchelt, D., Iwatsubo, T., Sisodia, S., & Malinow, R. (2003) · Neuron · doi:10.1016/s0896-6273(03)00124-7 · Cited by 1,644
Patterns of Brain Activation in People at Risk for Alzheimer's Disease
Bookheimer, S. Y., Strojwas, M. H., Cohen, M. S., Saunders, A. M., Pericak-Vance, M. A., Mazziotta, J. C., & Small, G. W. (2000) · New England Journal of Medicine · doi:10.1056/nejm200008173430701 · Cited by 1,328
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Cirrito, J. R., Yamada, K. A., Finn, M. B., Sloviter, R. S., Bales, K. R., May, P. C., Schoepp, D. D., Paul, S. M., Mennerick, S., & Holtzman, D. M. (2005) · Neuron · doi:10.1016/j.neuron.2005.10.028 · Cited by 1,265
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Bjorkli, C., Ebbesen, N. C., Julian, J. B., Witter, M. P., Sandvig, A., & Sandvig, I. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.05.498797 · Cited by 2
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Wang, T., Lai, X.-X., Xu, L., Shen, Y., Lin, J., Gao, S., & Hu, H. (2020) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2020/9058135 · Cited by 1
Purohit, P., & Roy, P. K. (2022) · Springer eBooks · doi:10.1007/978-981-19-1352-5_11 · Cited by 1
Chernek, P., Yazdi, B. G., Hojjati, S. H., Ozoria, S., Calimag, J., Wang, X. H., Modarresi, A., Picha, S. G., Chiang, G. C.-Y., & Razlighi, Q. R. (2025) · medRxiv · doi:10.1101/2025.11.02.25339341 · Cited by 1
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Sheng, C., Huang, Y. M., Li, H., Sun, Y., Chen, Q., Ma, C., Liu, F., & Han, Y. (2018) · Neuropsychiatry · doi:10.4172/neuropsychiatry.1000508
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Pardo, J. V. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.83824
Cappa, S. F. (2020) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735988257.793576095
Branch, A., Glover, L. R., & Gallagher, M. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1238444
Shin, J., Brady, E. S., Chen, C., Lauderdale, K., Agrawal, A., Zhang, Y., Jiang, X., Nambiar, P., Herbert, J., Mallen, D. D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.11.687887
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Ploux, E. (2022) · theses.fr (ABES) · doi:10.70675/e7b5bba0z9abcz4325zae71z841a60892f8b
Clemens, S. G., Hobbs, D. A., McKay, N. S., Millar, P. R., Morris, J. C., Hassenstab, J. J., Zacks, J. M., Balota, D. A., & Gordon, B. A. (2026) · Brain Communications · doi:10.1093/braincomms/fcag167
Adrita, N., Claassen, I., Huerta, M., Black, S. M., Estévez, Á. G., Roberts, B. R., Beckman, J. S., & Franco, M. C. (2026) · Redox Biology · doi:10.1016/j.redox.2026.104423
Solar, K. G., Kashyap, S., Deom, N., Uludaǧ, K., Zotovic, L., Lanctôt, K. L., Black, S. E., & McAndrews, M. P. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01208-9
Metadata
- Authors
- Leal, S. L., Landau, S. M., Bell, R. K., & Jagust, W. J.
- Affiliations
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- Corresponding author
- Leal, Stephanie L
- Jagust, William J
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2017-02-08
- Volume
- 6
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC5325620
- OpenAlex
- W2586682108
- Cited by
- 117
- References
- 48
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 11 · 2023: 16 · 2022: 11 · 2021: 16 · 2020: 14 · 2019: 20 · 2018: 10 · 2017: 4
- FWCI
- 10.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Aging (AG054116, AG034570)
- 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
- hippocampal activation, amyloid-beta deposition, cognitive decline, Alzheimer's disease, amyloid hypothesis, memory performance, PiB PET, functional MRI, older adults, synaptic activity, longitudinal change
- MeSH terms
- Aged, Aged, 80 and over, Female, Hippocampus, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Amyloid beta-Peptides, Positron-Emission Tomography, Cognitive Dysfunction
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