The DOI matches its Crossref record; 86 of the 86 references it cites were found in a registry or an index.
Spatial correlation between brain aerobic glycolysis and amyloid-β (Aβ) deposition
Open accessAbstract
Amyloid-β (Aβ) plaque deposition can precede the clinical manifestations of dementia of the Alzheimer type (DAT) by many years and can be associated with changes in brain metabolism. Both the Aβ plaque deposition and the changes in metabolism appear to be concentrated in the brain's default-mode network. In contrast to prior studies of brain metabolism which viewed brain metabolism from a unitary perspective that equated glucose utilization with oxygen consumption, we here report on regional glucose use apart from that entering oxidative phosphorylation (so-called "aerobic glycolysis"). Using PET, we found that the spatial distribution of aerobic glycolysis in normal young adults correlates spatially with Aβ deposition in individuals with DAT and cognitively normal participants with elevated Aβ, suggesting a possible link between regional aerobic glycolysis in young adulthood and later development of Alzheimer pathology.
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In the citing paper
“Similarly, brain AG was previously found to be highest in regions most vulnerable to amyloid deposition”
From Aerobic glycolysis in the human brain is associated with development and neotenous gene expression (2014) · cites this paper as Vlassenko et al., 2010
In the citing paper
“Further, AG identifies regions most vulnerable to amyloid-beta deposition in Alzheimer’s disease”
From Aerobic glycolysis in the human brain is associated with development and neotenous gene expression (2014) · cites this paper as Vlassenko et al., 2010
In the citing paper
“Young ApoE4 carriers showed increased brain activity in default network regions, as well as in the hippocampus and the temporal lobe, possibly due to increased aerobic glycolysis.34”
From ApoE4-dependent lysosomal cholesterol accumulation impairs mitochondrial homeostasis and oxidative phosphorylation in human astrocytes (2023) · cites this paper as [34]
In the citing paper
“Whether Aβ changes precede either age-related or AD-related brain bioenergetic changes is less clear-cut, especially since abundant data from human and cell-based studies either suggest or demonstrate bioenergetic factors regulate both APP processing and Aβ production [95–100].”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
In the citing paper
“Whether Aβ changes precede either age-related or AD-related brain bioenergetic changes is less clear-cut, especially since abundant data from human and cell-based studies either suggest or demonstrate bioenergetic factors regulate both APP processing and Aβ production [95–100].”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
In the citing paper
“Brain plaque deposition anatomically corresponds to the functional magnetic resonance imaging (fMRI)-defined default mode network (DMN), a brain region characterized by relatively high glycolysis rates [69, 99, 136].”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
In the citing paper
“Bioenergetic metabolism certainly affects APP processing and Aβ production.”
From Brain aging, Alzheimer's disease, and mitochondria (2011) · cites this paper as [99]
Citations
Cites
Understanding the Warburg Effect: The Metabolic Requirements of Cell Proliferation
Vander Heiden, M. G., Cantley, L. C., & Thompson, C. B. (2009) · Science · doi:10.1126/science.1160809 · Cited by 16,197
The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics
Hardy, J., & Selkoe, D. J. (2002) · Science · doi:10.1126/science.1072994 · Cited by 13,864
A 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
Self-citationThe 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
Self-citationThe 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
Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound‐B
Klunk, W. E., Engler, H., Nordberg, A., Wang, Y., Blomqvist, G., Holt, D. P., Bergström, M., Savitcheva, I., Huang, G., Estrada, S., et al. (2004) · Annals of Neurology · doi:10.1002/ana.20009 · Cited by 4,496
Pathways towards and Away from Alzheimer’s Disease
Mattson, M. P. (2004) · Nature · doi:10.1038/nature02621 · Cited by 3,052
Pellerin, L., & Magistretti, P. J. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.22.10625 · Cited by 2,927 · cited 1×
“First, it is essential for the removal of glutamate into astrocytes from the synaptic cleft (40).”
BackgroundPhelps, M. E., Huang, S. C., Hoffman, E. J., Selin, C. E., Sokoloff, L., & Kuhl, D. E. (1979) · Annals of Neurology · doi:10.1002/ana.410060502 · Cited by 2,417
A default mode of brain function: a brief history of an evolving idea
Raichle, M. E., & Snyder, A. Z. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.041 · Cited by 2,173
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Raichle, M. E. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014030 · Cited by 3,904
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Tönnies, E., & Trushina, E. (2017) · Journal of Alzheimer s Disease · doi:10.3233/jad-161088 · Cited by 1,750
Alzheimer’s Disease: The Challenge of the Second Century
Holtzman, D. M., Morris, J. C., & Goate, A. M. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002369 · Cited by 1,424
Self-citationRegulation of pyruvate metabolism and human disease
Gray, L. R., Tompkins, S. C., & Taylor, E. B. (2013) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-013-1539-2 · Cited by 983
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Bero, A. W., Yan, P., Roh, J. H., Cirrito, J. R., Stewart, F. R., Raichle, M. E., Lee, J.-M., & Holtzman, D. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2801 · Cited by 880
Self-citationBrain Glucose Metabolism: Integration of Energetics with Function
Dienel, G. A. (2018) · Physiological Reviews · doi:10.1152/physrev.00062.2017 · Cited by 911
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Swerdlow, R. H. (2018) · Journal of Alzheimer’s Disease · doi:10.3233/jad-170585 · Cited by 850
Fjell, A. M., McEvoy, L., Holland, D., Dale, A. M., & Walhovd, K. B. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2014.02.004 · Cited by 799
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Swerdlow, R. H., Burns, J. M., & Khan, S. M. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2013.09.010 · Cited by 786
Regional aerobic glycolysis in the human brain
Vaishnavi, S. N., Vlassenko, A. G., Rundle, M. M., Snyder, A. Z., Mintun, M. A., & Raichle, M. E. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010459107 · Cited by 738
Self-citationMemory loss in Alzheimer's disease
Jahn, H. (2013) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2013.15.4/hjahn · Cited by 653
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Bonvento, G., & Bolaños, J. P. (2021) · Cell Metabolism · doi:10.1016/j.cmet.2021.07.006 · Cited by 580
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Resting-state functional connectivity in normal brain aging
Ferreira, L. K., & Busatto, G. F. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.01.017 · Cited by 631
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Valvona, C. J., Fillmore, H. L., Nunn, P. B., & Pilkington, G. J. (2015) · Brain Pathology · doi:10.1111/bpa.12299 · Cited by 570
Reiman, E. M., Quiroz, Y. T., Fleisher, A. S., Chen, K., Velez-Pardo, C., Jimenez-Del-Rio, M., Fagan, A. M., Shah, A. R., Alvarez, S., Arbelaez, A., et al. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70228-4 · Cited by 541
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Musiek, E. S., Lim, M. M., Yang, G., Bauer, A. Q., Qi, L., Lee, Y., Roh, J. H., Ortiz-Gonzalez, X., Dearborn, J. T., Culver, J. P., et al. (2013) · Journal of Clinical Investigation · doi:10.1172/jci70317 · Cited by 524
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Sheline, Y. I., & Raichle, M. E. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.11.028 · Cited by 509
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Grieb, P. (2015) · Molecular Neurobiology · doi:10.1007/s12035-015-9132-3 · Cited by 373
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Goyal, M. S., Vlassenko, A. G., Blazey, T. M., Su, Y., Couture, L. E., Durbin, T. J., Bateman, R. J., Benzinger, T. L.-S., Morris, J. C., & Raichle, M. E. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.07.010 · Cited by 318
Self-citationBrain aging, Alzheimer's disease, and mitochondria
Swerdlow, R. H. (2011) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2011.08.012 · Cited by 306
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Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265
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Mitochondrial dysfunction in microglia: a novel perspective for pathogenesis of Alzheimer’s disease
Li, Y., Xia, X., Wang, Y., & Zheng, J. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02613-9 · Cited by 231
Carvalho, D. Z., Louis, E. K. S., Knopman, D. S., Boeve, B. F., Lowe, V. J., Roberts, R. O., Mielke, M. M., Przybelski, S. A., Machulda, M. M., Petersen, R. C., et al. (2018) · JAMA Neurology · doi:10.1001/jamaneurol.2018.0049 · Cited by 216
Yamazaki, Y., Shinohara, M., Shinohara, M., Yamazaki, A., Murray, M. E., Liesinger, A. M., Heckman, M. G., Lesser, E. R., Parisi, J. E., Petersen, R. C., et al. (2019) · Brain · doi:10.1093/brain/awz011 · Cited by 202
Bidirectional Relationship between Functional Connectivity and Amyloid-β Deposition in Mouse Brain
Bero, A. W., Bauer, A. Q., Stewart, F. R., White, B. R., Cirrito, J. R., Raichle, M. E., Culver, J. P., & Holtzman, D. M. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.5845-11.2012 · Cited by 181
Self-citationImpaired default network functional connectivity in autosomal dominant Alzheimer disease
Chhatwal, J. P., Schultz, A. P., Johnson, K., Benzinger, T. L. S., Jack, C., Ances, B. M., Sullivan, C. A., Salloway, S. P., Ringman, J. M., Koeppe, R. A., et al. (2013) · Neurology · doi:10.1212/wnl.0b013e3182a1aafe · Cited by 179
Self-citationMusiek, E. S., Chen, Y., Korczykowski, M., Saboury, B., Martinez, P. M., Reddin, J. S., Alavi, A. A., Kimberg, D. Y., Wolk, D. A., Julin, P., et al. (2011) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2011.06.003 · Cited by 180
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Self-citationYao, J., Chen, S., Mao, Z., Cadenas, E., & Brinton, R. D. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021788 · Cited by 169
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Kumar, R. K., Chaterjee, P., Sharma, P. K., Singh, A. K., Gupta, A., Gill, K., Tripathi, M., Dey, A. B., & Dey, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061560 · Cited by 167
Vogel, J. W., Corriveau‐Lecavalier, N., Franzmeier, N., Pereira, J. B., Brown, J. A., Maaß, A., Botha, H., Seeley, W. W., Bassett, D. S., Jones, D. T., et al. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00731-8 · Cited by 180
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Pathak, D., Berthet, A., & Nakamura, K. (2013) · Annals of Neurology · doi:10.1002/ana.24014 · Cited by 162
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Mormino, E. C., Brandel, M. G., Madison, C. M., Marks, S. M., Baker, S. L., & Jagust, W. J. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhr255 · Cited by 151
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Yablonskiy, D. A., Sukstanskii, A. L., & He, X. (2012) · NMR in Biomedicine · doi:10.1002/nbm.2839 · Cited by 148
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Amyloid-β diurnal pattern: possible role of sleep in Alzheimer's disease pathogenesis
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Buckley, R. F., Schultz, A. P., Hedden, T., Papp, K. V., Hanseeuw, B. J., Marshall, G. A., Sepulcre, J., Smith, E. E., Rentz, D. M., Johnson, K. A., et al. (2017) · Neurology · doi:10.1212/wnl.0000000000004059 · Cited by 145
Neuroketotherapeutics: A modern review of a century-old therapy
Koppel, S. J., & Swerdlow, R. H. (2017) · Neurochemistry International · doi:10.1016/j.neuint.2017.05.019 · Cited by 142
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Grothe, M. J., Sepulcre, J., Gonzalez-Escamilla, G., Jelistratova, I., Schöll, M., Hansson, O., Teipel, S. J., & Initiative, A. D. N. (2018) · Brain · doi:10.1093/brain/awy189 · Cited by 142
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Hansen, J. Y., Shafiei, G., Vogel, J. W., Smart, K. A., Bearden, C. E., Hoogman, M., Franke, B., van Rooij, D., Buitelaar, J. K., McDonald, C. R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32420-y · Cited by 130
Within-patient correspondence of amyloid-β and intrinsic network connectivity in Alzheimer’s disease
Myers, N., Pasquini, L., Göttler, J., Grimmer, T., Koch, K., Ortner, M., Neitzel, J., Mühlau, M., Förster, S., Kurz, A., et al. (2014) · Brain · doi:10.1093/brain/awu103 · Cited by 132
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Human neoteny revisited: The case of synaptic plasticity
Bufill, E., Agustí, J., & Blesa, R. (2011) · American Journal of Human Biology · doi:10.1002/ajhb.21225 · Cited by 130
Fairley, L. H., Lai, K. O., Wong, J. H., Chong, W. J., Vincent, A. S., D’Agostino, G. A., Wu, X., Naik, R. R., Jayaraman, A., Langley, S. R., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209177120 · Cited by 132
Ryu, W., Bormann, M. K., Shen, M., Kim, D., Forester, B. P., Park, Y., So, J., Seo, H., Sonntag, K., & Cohen, B. M. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01068-3 · Cited by 126
Lactate deficit in an Alzheimer disease mouse model: the relationship with neuronal damage
Zhang, M., Cheng, X., Dang, R., Zhang, W., Zhang, J., & Yao, Z. (2018) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nly102 · Cited by 130
Yao, J., Rettberg, J. R., Klosinski, L. P., Cadenas, E., & Brinton, R. D. (2011) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2011.10.005 · Cited by 125
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., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1221536110 · Cited by 122
Socially responsive effects of brain oxidative metabolism on aggression
Li‐Byarlay, H., Rittschof, C. C., Massey, J. H., Pittendrigh, B. R., & Robinson, G. E. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1412306111 · Cited by 129
Liguori, C., Chiaravalloti, A., Sancesario, G., Stefani, A., Sancesario, G. M., Mercuri, N. B., Schillaci, O., & Pierantozzi, M. (2016) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-016-3417-2 · Cited by 117
Esteras, N., Rohrer, J. D., Hardy, J., Wray, S., & Abramov, A. Y. (2017) · Redox Biology · doi:10.1016/j.redox.2017.03.008 · Cited by 120
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Newington, J. T., Pitts, A., Chien, A., Arseneault, R., Schubert, D. A., & Cumming, R. C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0019191 · Cited by 121
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Fairley, L. H., Wong, J. H., & Barron, A. M. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.624538 · Cited by 120
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Poisnel, G., Hérard, A., Tayara, N. E. T. E., Bourrin, E., Volk, A., Kober, F., Delatour, B., Delzescaux, T., Debeir, T., Rooney, T., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.09.026 · Cited by 123
Förster, S., Grimmer, T., Miederer, I., Henriksen, G., Yousefi, B. H., Graner, P., Wester, H.-J., Förstl, H., Kurz, A., Dickerson, B. C., et al. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.04.023 · Cited by 119
Elman, J. A., Madison, C. M., Baker, S. L., Vogel, J. W., Marks, S. M., Crowley, S., O’Neil, J. P., & Jagust, W. J. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu259 · Cited by 122
Newington, J. T., Rappon, T., Albers, S. S., Wong, D. Y. L., Rylett, R. J., & Cumming, R. C. (2012) · Journal of Biological Chemistry · doi:10.1074/jbc.m112.366195 · Cited by 115
In vivo characterization of the early states of the amyloid-beta network
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Fagan, A. M., & Perrin, R. J. (2012) · Biomarkers in Medicine · doi:10.2217/bmm.12.42 · Cited by 118
Shinohara, M., Fujioka, S., Murray, M. E., Wojtas, A., Baker, M., Rovelet-Lecrux, A., Rademakers, R., Das, P., Parisi, J. E., Graff-Radford, N. R., et al. (2014) · Brain · doi:10.1093/brain/awu046 · Cited by 115
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Nugent, S., Tremblay, S., Chen, K. W., Ayutyanont, N., Roontiva, A., Castellano, C., Fortier, M., Roy, M., Courchesne‐Loyer, A., Bocti, C., et al. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.11.027 · Cited by 113
Caselli, R. J., & Reiman, E. M. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-129026 · Cited by 111
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Self-citationVascular cognitive impairment, dementia, aging and energy demand. A vicious cycle
Popa‐Wagner, A., Buga, A.-M., Popescu, B. O., & Muresanu, D. F. (2013) · Journal of Neural Transmission · doi:10.1007/s00702-013-1129-3 · Cited by 107
Early remodeling of the neocortex upon episodic memory encoding
Bero, A. W., Meng, J., Cho, S., Shen, A. H., Canter, R. G., Ericsson, M., & Tsai, L. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408378111 · Cited by 116
Tau and amyloid β proteins distinctively associate to functional network changes in the aging brain
Sepulcre, J., Sabuncu, M. R., Li, Q., Fakhri, G. E., Sperling, R. A., & Johnson, K. A. (2017) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2017.02.011 · Cited by 106
Aerobic glycolysis in the primate brain: Reconsidering the implications for growth and maintenance
Bauernfeind, A. L., Barks, S. K., Duka, T., Grossman, L. I., Hof, P. R., & Sherwood, C. C. (2014) · Brain Structure and Function · doi:10.1007/s00429-013-0662-z · Cited by 105
Oh, H., Madison, C. M., Baker, S. L., Rabinovici, G. D., & Jagust, W. J. (2016) · Brain · doi:10.1093/brain/aww108 · Cited by 113
Ossenkoppele, R., Iaccarino, L., Schonhaut, D. R., Brown, J. A., Joie, R. L., O’Neil, J. P., Janabi, M., Baker, S. L., Kramer, J. H., Gorno‐Tempini, M., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101848 · Cited by 109
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Stacpoole, P. W. (2012) · Aging Cell · doi:10.1111/j.1474-9726.2012.00805.x · Cited by 107
Johnson, L. A., Torres, E. R. S., Impey, S., Stevens, J. F., & Raber, J. (2017) · Scientific Reports · doi:10.1038/srep43701 · Cited by 103
Newington, J. T., Harris, R. A., & Cumming, R. C. (2013) · Journal of Neurodegenerative Diseases · doi:10.1155/2013/234572 · Cited by 109
Harris, R. A., Tindale, L. C., Lone, A., Singh, O., Macauley, S. L., Stanley, M., Holtzman, D. M., Bartha, R., & Cumming, R. C. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3131-15.2016 · Cited by 102
Metadata
- Authors
- Vlassenko, A. G., Vaishnavi, S. N., Couture, L., Sacco, D., Shannon, B. J., Mach, R. H., Morris, J. C., Raichle, M. E., & Mintun, M. A.
- Affiliations
- Washington University in St. Louis (US)
- Departments of aRadiology,
- Neurology,
- Corresponding author
- Mintun, Mark A.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2010-09-13
- Volume
- 107
- Issue
- 41
- Pages
- 17763-17767
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2955133
- OpenAlex
- W2137243283
- Cited by
- 404
- References
- 84
- Citations by year
- 2026: 20 · 2025: 21 · 2024: 15 · 2023: 21 · 2022: 26 · 2021: 22 · 2020: 24 · 2019: 20 · 2018: 28 · 2017: 41 · 2016: 20 · 2015: 23 · 2014: 28 · 2013: 33 · 2012: 28
- FWCI
- 20.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5p20mh077967-04, AG05681, AG003991)
- National Institute on Aging (P50AG005681, P01AG003991)
- National Institute of Mental Health (P20MH-077967)
- National Institute of Neurological Disorders and Stroke (F30NS057901, P30NS048056, P50NS006833)
- 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 › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- aerobic glycolysis, amyloid-beta deposition, Alzheimer's disease, default mode network, brain glucose metabolism, oxidative phosphorylation, spatial correlation, young adults, positron emission tomography
- MeSH terms
- Adult, Alzheimer Disease, Amyloid, Brain, Female, Glucose, Glycolysis, Humans, Male, Oxygen, Positron-Emission Tomography
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