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Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer’s disease
Open accessAbstract
There is striking overlap between the spatial distribution of amyloid-β pathology in patients with Alzheimer's disease and the spatial distribution of high intrinsic functional connectivity in healthy persons. This overlap suggests a mechanistic link between amyloid-β and intrinsic connectivity, and indeed there is evidence in patients for the detrimental effects of amyloid-β plaque accumulation on intrinsic connectivity in areas of high connectivity in heteromodal hubs, and particularly in the default mode network. However, the observed spatial extent of amyloid-β exceeds these tightly circumscribed areas, suggesting that previous studies may have underestimated the negative impact of amyloid-β on intrinsic connectivity. We hypothesized that the known positive baseline correlation between patterns of amyloid-β and intrinsic connectivity may mask the larger extent of the negative effects of amyloid-β on connectivity. Crucially, a test of this hypothesis requires the within-patient comparison of intrinsic connectivity and amyloid-β distributions. Here we compared spatial patterns of amyloid-β-plaques (measured by Pittsburgh compound B positron emission tomography) and intrinsic functional connectivity (measured by resting-state functional magnetic resonance imaging) in patients with prodromal Alzheimer's disease via spatial correlations in intrinsic networks covering fronto-parietal heteromodal cortices. At the global network level, we found that amyloid-β and intrinsic connectivity patterns were positively correlated in the default mode and several fronto-parietal attention networks, confirming that amyloid-β aggregates in areas of high intrinsic connectivity on a within-network basis. Further, we saw an internetwork gradient of the magnitude of correlation that depended on network plaque-load. After accounting for this globally positive correlation, local amyloid-β-plaque concentration in regions of high connectivity co-varied negatively with intrinsic connectivity, indicating that amyloid-β pathology adversely reduces connectivity anywhere in an affected network as a function of local amyloid-β-plaque concentration. The local negative association between amyloid-β and intrinsic connectivity was much more pronounced than conventional group comparisons of intrinsic connectivity would suggest. Our findings indicate that the negative impact of amyloid-β on intrinsic connectivity in heteromodal networks is underestimated by conventional analyses. Moreover, our results provide first within-patient evidence for correspondent patterns of amyloid-β and intrinsic connectivity, with the distribution of amyloid-β pathology following functional connectivity gradients within and across intrinsic networks.
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“A certain overlap between the DMN and the distribution of Aβ fibrils in AD has also been noted in previous publications23–26.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [25]
In the citing paper
“There is an increased Aβ-PET signal within the CEN and the SN in the progression of AD.244,245”
From Amyloid β-based therapy for Alzheimer’s disease: Challenges, successes and future (2023) · cites this paper as [244]
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Cites
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 1×
“The dissociation between heteromodal and primary networks is in line with findings that primary sensory and sensorimotor regions are relatively spared in earlier stages of Alzheimer’s disease (Braak and Braak, 1991).”
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
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507 · cited 1×
“…MRI image (using affine registration), and normalized to a template in the stereotactic space of the Montreal Neurological Institute (MNI) with the ‘segment’ function (SPM5), which uses an iterative combination of non-linear registration and cortical segmentation (Ashburner and Friston, 2005).”
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429 · cited 2×
“Framewise displacement (Power et al., 2012) or the root-mean-”
Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade
Jack, C. R., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., Petersen, R. C., & Trojanowski, J. Q. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(09)70299-6 · Cited by 4,806
Alzheimer's Disease Is a Synaptic Failure
Selkoe, D. J. (2002) · Science · doi:10.1126/science.1074069 · Cited by 4,420 · cited 1×
“Aberrant clearance of amyloid-b precursor protein is thought to be a critical initial event in the disease’s pathogenesis, leading to amyloid-b peptide accumulation and plaque formation 20 to 30 years before cognitive symptoms arise (Selkoe, 2002; Jack et al., 2010; Bateman et al., 2012).”
Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria
Dubois, B., Feldman, H. H., Jacova, C., DeKosky, S. T., Barberger-Gateau, P., Cummings, J., Delacourte, A., Galasko, D., Gauthier, S., Jicha, G., et al. (2007) · The Lancet Neurology · doi:10.1016/s1474-4422(07)70178-3 · Cited by 4,411 · cited 1×
“Prodromal Alzheimer’s disease has recently been defined by the coincidence of both mild cognitive impairment and the presence of at least one of five supportive biological signs for Alzheimer’s disease, such as medial temporal lobe atrophy or significant PiB uptake (Dubois et al., 2007).”
Moms, J. C., Heyman, A., Mohs, R. C., Hughes, J. P., van Belle, G., Fillenbaum, G., Mellits, E. D., & Clark, C. (1989) · Neurology · doi:10.1212/wnl.39.9.1159 · Cited by 4,130 · cited 1×
“…participant included medical history, neurological examination, informant interview (Morris, 1993), neuropsychological assessment by the neuropsychological assessment battery of the Consortium to Establish a Registry for Alzheimer’s disease (CERAD, Morris et al., 1989), structural MRI and PiB-PET.”
Gauthier, S. G., Reisberg, ., Zaudig, M., Petersen, R. C., Ritchie, K. A., Broich, K., Belleville, S., Brodaty, H., Bennett, D. A., Chertkow, H. M., et al. (2006) · The Lancet · doi:10.1016/s0140-6736(06)68542-5 · Cited by 4,094
Clinical and biomarker changes in dominantly inherited Alzheimer's disease
Bateman, R. J., Xiong, C., Benzinger, T. L. S., Fagan, A. M., Goate, A., Fox, N. C., Marcus, D. S., Cairns, N. J., Xie, X., Blazey, T. M., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1202753 · Cited by 3,814 · cited 3×
“Given that significant PiB-uptake is measurable in the DMN even in early preclinical stages of Alzheimer’s disease (Jack et al., 2010; Bateman et al., 2012), where conventional measures of connectivity do not report robust connectivity reductions, the method presented here may help detect early, subtle reductions in c…”
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Items 101–132 of 132
Sadeghi, M., Azargoonjahromi, A., Nasiri, H., Yaghoobi, A., Sadeghi, M., Chavoshi, S. S., Baghaeikia, S., Mahzari, N., Valipour, A., Oskouei, R. R., et al. (2024) · Molecular Brain · doi:10.1186/s13041-024-01136-z · Cited by 8
Brain functional specialization and cooperation in Alzheimer's disease
Wu, Y., Gao, M., Lv, L., Yan, Y., Gao, L., Geng, Z., Zhou, S., Zhu, W., Yu, Y., Tian, Y., et al. (2024) · Brain and Behavior · doi:10.1002/brb3.3550 · Cited by 7
“Our results suggest the abnormal function of intrinsic functional networks, which is in line with previous studies that showed that AD is a network disease (Myers et al., 2014).”
Neitzel, J., Nuttall, R., & Sorg, C. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00026 · Cited by 7
Self-citationSobczak, A. M., Bohaterewicz, B., Ceglarek, A., Żyrkowska, A., Fąfrowicz, M., Słowik, A., Wnuk, M. A., Marona, M., Nowak, K., Żur‐Wyrozumska, K., et al. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.852981 · Cited by 8
Son, S. J., Hong, C. H., Kim, N., Choi, J. W., Roh, H. W., Lee, H., Seo, S. W., Choi, S. H., Kim, E., Kim, B. C., et al. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.05.010 · Cited by 8
Adhikari, M. H., Vasilkovska, T., Cachope, R., Tang, H., Liu, L., Keliris, G. A., Muñoz-Sanjuán, I., Pustina, D., der Linden, A. V., & Verhoye, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-36812-y · Cited by 8
The default mode network, depression and Alzheimer’s disease
Sachdev, P. S., & Wang, H. (2022) · International Psychogeriatrics · doi:10.1017/s1041610222000539 · Cited by 7
Griffa, A., Legdeur, N., Badissi, M., van den Heuvel, M. P., Stam, C. J., Visser, P. J., & Hillebrand, A. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.746373 · Cited by 9
Network-targeted transcranial magnetic stimulation (TMS) for mild cognitive impairment (MCI)
Taylor, J. L., Bhatt, P., Hernandez, B., Iv, M., Adamson, M. M., Heath, A. M., Yesavage, J. A., & McNerney, M. W. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103819 · Cited by 6
Sorg, C., Göttler, J., & Zimmer, C. (2015) · Clinical Neuroradiology · doi:10.1007/s00062-015-0438-3 · Cited by 6
Self-citationLissaman, R., Lancaster, T. M., Parker, G. D., Graham, K. S., Lawrence, A. D., & Hodgetts, C. J. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100126 · Cited by 4
Um, Y. H., Choi, W. H., Jung, W. S., Park, Y. H., Lee, C., & Lim, H. K. (2017) · Psychiatry Investigation · doi:10.4306/pi.2017.14.6.883 · Cited by 4
Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer’s disease
Tu, J. C., Millar, P. R., Strain, J. F., Eck, A., Adeyemo, B., Daniels, A., Karch, C. M. M., Huey, E. D., McDade, E., Day, G. S., et al. (2024) · Network Neuroscience · doi:10.1162/netn_a_00395 · Cited by 4
Han, F., Liu, X., Mailman, R. B., Huang, X., Liu, X., & Initiative, T. A. D. N. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.24.501309 · Cited by 4
Sundaram, G. S. M., Jones, L. A., Zhou, Y., Laforest, R. A., & Sharma, V. (2019) · Nuclear Medicine and Biology · doi:10.1016/j.nucmedbio.2019.10.004 · Cited by 2
The Role of Functional Networks in Neuropsychiatric Disorders
Canuet, L., Aoki, Y., Ishii, R., & Maestú, F. (2016) · Springer eBooks · doi:10.1007/978-3-319-32265-0_8 · Cited by 2
Shi, X., Zheng, W., Hou, X., Ya, C., Chen, H., Yao, W., Lv, T., & Bai, F. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251320124 · Cited by 1
Sohn, W. S., Lee, T. Y., Yoo, K., Kim, M., Yun, J., Hur, J., Yoon, Y. B., Seo, S. W., Na, D. L., Jeong, Y., et al. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00238
Neitzel, J. (2017) · doi:10.5282/edoc.20999
Self-citationResting-state Co-activation Patterns as Accurate Predictors of Alzheimer’s Disease in Aged MicePreprint
Adhikari, M. H., Belloy, M. E., der Linden, A. V., Keliris, G. A., & Verhoye, M. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.22.349852
Network changes underlying neural disorders: Relation to the MMN networks
Eggermont, J. J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15548-2.00012-0
Öztürk, G., Harı, E., Yıldırım, K., Bayram, A., Yıldırım, Z., Demıralp, T., & Gurvit, H. I. (2025) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2025.109082
Katayama, O., Stern, Y., Habeck, C. G., Lee, S., Harada, K., Makino, K., Tomida, K., Morikawa, M., Yamaguchi, R., Nishijima, C., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26654557
Katayama, O., Stern, Y., Habeck, C. G., Lee, S., Harada, K., Makino, K., Tomida, K., Morikawa, M., Yamaguchi, R., Nishijima, C., et al. (2023) · Figshare · doi:10.6084/m9.figshare.24844585
Katayama, O., Stern, Y., Habeck, C. G., Lee, S., Harada, K., Makino, K., Tomida, K., Morikawa, M., Yamaguchi, R., Nishijima, C., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26654554
Katayama, O., Stern, Y., Habeck, C. G., Lee, S., Harada, K., Makino, K., Tomida, K., Morikawa, M., Yamaguchi, R., Nishijima, C., et al. (2024) · Figshare · doi:10.6084/m9.figshare.24844713
Chi, L., Qiao, M., Huang, A. C., Chen, S., Yang, X., Wu, H., Lin, X., Chen, J., & Initiative, A. D. N. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261436704
“Compared to CN, MCI-A β + group showed worse inter-hemispheric functional synchrony in the PCUN, SFG, and Cere6, all of which are known connectivity hubs 27,28 and early sites of A β deposition.”
Borcuk, C. (2021) · theses.fr (ABES) · doi:10.70675/c316f2dcz54d3z41cfzb3f5z99c7b2d3cd95 · Cited by 1
Dautricourt, S. (2021) · theses.fr (ABES) · doi:10.70675/63472a25z31eaz4ebczaebdzd8d2e061b4b8
Attention Networks Connectivity After Cognitive Rehabilitation in Alzheimer’s Disease
Salmon, É., Bahri, M. A., Kurth, S., Collette, F., Phillips, C. L. M., Majerus, S., Lekeu, F., Quittre, A., Wojtasik, V., Olivier, C., et al. (2026) · Journal of Geriatric Psychiatry and Neurology · doi:10.1177/08919887261462178
Greater amyloid burden in cognitive networks in preclinical Alzheimer's disease
Nolin, S. A., Aghamoosa, S., Rieter, W. J., Jones, A., Nietert, P. J., & Benitez, A. M. (2026) · Journal of Alzheimer s Disease · doi:10.1177/13872877261493984
Metadata
- Authors
- Myers, N., Pasquini, L., Göttler, J., Grimmer, T., Koch, K., Ortner, M., Neitzel, J., Mühlau, M., Förster, S., Kurz, A., Förstl, H., Zimmer, C., Wohlschläger, A. M., Riedl, V., Drzezga, A., & Sorg, C.
- Affiliations
- TUM Klinikum (DE)
- Technical University of Munich (DE)
- University of Cologne (DE)
- Corresponding author
- Myers, Nicholas
- ORCID
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-04-26
- Volume
- 137
- Issue
- 7
- Pages
- 2052-2064
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- PMC
- PMC4065018
- OpenAlex
- W2031921419
- Cited by
- 132
- References
- 57
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 9 · 2023: 13 · 2022: 9 · 2021: 15 · 2020: 5 · 2019: 10 · 2018: 13 · 2017: 20 · 2016: 11 · 2015: 14 · 2014: 7
- FWCI
- 9.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Brain (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- mediaTUM (Technical University of Munich) (submittedVersion)
- Funders
- Wellcome Trust
- Bundesministerium für Bildung und Forschung (01ER0803, 01EV0710)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- 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
- Keywords
- amyloid plaques, intrinsic functional connectivity, Alzheimer's disease, prodromal Alzheimer's disease, default mode network, frontoparietal attention network, within-patient comparison, Pittsburgh compound B PET, resting-state fMRI
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Analysis of Variance, Aniline Compounds, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Neuropsychological Tests, Thiazoles, Amyloid beta-Peptides, Positron-Emission Tomography, Plaque, Amyloid
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