The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.
Intrinsic connectivity networks in healthy subjects explain clinical variability in Alzheimer’s disease
Open accessAbstract
Although previous studies have emphasized the vulnerability of the default mode network (DMN) in Alzheimer’s disease (AD), little is known about the involvement of other functional networks and their relationship to clinical phenotype. To test whether clinicoanatomic heterogeneity in AD is driven by the involvement of specific networks, network connectivity was assessed in healthy subjects by seeding regions commonly and specifically atrophied in three clinical AD variants: early-onset AD (age at onset, <65 y; memory and executive deficits), logopenic variant primary progressive aphasia (language deficits), and posterior cortical atrophy (visuospatial deficits). Four-millimeter seed regions of interest were used to obtain intrinsic connectivity maps in 131 healthy controls (age, 65.5 ± 3.5 y). Atrophy patterns in independent cohorts of AD variant patients and their correspondence to connectivity networks in controls were also assessed. The connectivity maps of commonly atrophied regions of interest support posterior DMN and precuneus network involvement across AD variants, whereas seeding regions specifically atrophied in each AD variant revealed distinct, syndrome-specific connectivity patterns. Goodness-of-fit analysis of each connectivity map with network templates showed the highest correspondence between the early-onset AD seed connectivity map and anterior salience and right executive-control networks, the logopenic aphasia seed connectivity map and the language network, and the posterior cortical atrophy seed connectivity map and the higher visual network. Connectivity maps derived from controls matched regions commonly and specifically atrophied in the patients. Our findings indicate that the posterior DMN and precuneus network are commonly affected in AD variants, whereas syndrome-specific neurodegenerative patterns are driven by the involvement of specific networks outside the DMN.
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“An alternative explanation is that Aβ accumulation is caused by the high metabolic demand and metabolic stress in these neurons (high energy consumption and frequent fluctuations in activation and deactivation) and not the neuronal activation per se30.”
From Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity (2017) · cites this paper as [30]
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“Previous research has demonstrated lower connectivity among regions involved in specific brain networks of patients with AD, including the default mode network (Bai et al., 2011; Damoiseaux, Prater, Miller, & Greicius, 2012; Lehmann et al., 2013; Simic, Babic, Borovecki, & Hof, 2014).”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Lehmann et al., 2013
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McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · cited 2×
“J Neuropsychiatry Clin Neurosci 22(1):iv–, 1–7.”
BackgroundA default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Self-citationThe precuneus: a review of its functional anatomy and behavioural correlates
Cavanna, A. E., & Trimble, M. R. (2006) · Brain · doi:10.1093/brain/awl004 · Cited by 5,128
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Self-citationGusnard, D. A., Akbudak, E., Shulman, G. L., & Raichle, M. E. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.071043098 · Cited by 2,997
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
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King, J. B., Prigge, M. B. D., Koppelmans, V., Hoffman, J. M., & Duff, K. M. (2026) · Journal of the International Neuropsychological Society · doi:10.1017/s135561772610191x · Cited by 1
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Schonhaut, D. R., & Rabinovici, G. D. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800105-9.00017-2 · Cited by 1
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Xu, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14700 · Cited by 1
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Chhatwal, J. P. (2015) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
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van Hooren, R. W. E. (2022) · Research Publications (Maastricht University) · doi:10.26481/dis.20221215rh
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Schulz, M., Malherbe, C., Cheng, B., Thomalla, G., & Schlemm, E. (2021) · Figshare · doi:10.6084/m9.figshare.14538908
Shen, S., Liu, W., Zhou, M., Yang, R., Li, J., & Zhou, D. (2021) · Figshare · doi:10.6084/m9.figshare.16680160
Metadata
- Authors
- Lehmann, M., Madison, C. M., Ghosh, P. M., Seeley, W. W., Mormino, E., Greicius, M. D., Gorno-Tempini, M. L., Kramer, J. H., Miller, B. L., Jagust, W. J., & Rabinovici, G. D.
- Affiliations
- University of California, San Francisco (US)
- University of California, Berkeley (US)
- Stanford Medicine (US)
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA 94158;
- Lawrence Berkeley National Laboratory (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-06-24
- Volume
- 110
- Issue
- 28
- Pages
- 11606-11611
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3710820
- OpenAlex
- W1999605218
- Cited by
- 122
- References
- 73
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 5 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 17 · 2018: 14 · 2017: 10 · 2016: 15 · 2015: 13 · 2014: 11 · 2013: 4
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Hellman Family Foundation
- National Institute on Aging (P01 AG1972403, K23AG031861, P01AG019724, R01AG034570, P50AG023501)
- National Institute of Neurological Disorders and Stroke (R01NS073498, R01NS050915)
- 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
- Alzheimer's disease, resting-state networks, default mode network, precuneus, early-onset Alzheimer's disease, logopenic variant primary progressive aphasia, posterior cortical atrophy, brain atrophy, clinical phenotype, salience network, frontoparietal network, language network, healthy controls
- MeSH terms
- Aged, Alzheimer Disease, Brain, Female, Humans, Male, Middle Aged, Nerve Net, Reference Values, Task Performance and Analysis, Cohort Studies
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