Cited by 405Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Imaging Techniques and Applications

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 access

Vlassenko, Andrei G., Vaishnavi, S. Neil, Couture, Lars, Sacco, Dana, Shannon, Benjamin J., Mach, Robert H., Morris, John C., Raichle, Marcus E., Mintun, Mark A.·2010·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1010461107PMC PMC2955133

Open this paper in CoChat — free account

Abstract

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.

Integrity signals

Registry

Crossref · 10.1073/pnas.1010461107

Cites

86 of 86 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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-citation
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
Self-citation
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
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
Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization.
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).”
Background
Tomographic measurement of local cerebral glucose metabolic rate in humans with (F‐18)2‐fluoro‐2‐deoxy‐D‐glucose: Validation of method
Phelps, 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
Self-citation

Show all 84 references

Sorted by citations

Items 201–300 of 405

Neuronal culture microenvironments determine preferences in bioenergetic pathway use
Sünwoldt, J., Bosche, B., Meisel, A., & Mergenthaler, P. (2017) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2017.00305 · Cited by 34
Presymptomatic Alterations in Energy Metabolism and Oxidative Stress in the APP23 Mouse Model of Alzheimer Disease
Hartl, D., Schuldt, V., Forler, S., Zabel, C., Klose, J., & Rohe, M. (2012) · Journal of Proteome Research · doi:10.1021/pr300021e · Cited by 48
Inhibiting NLRP3 Inflammasome Activation by CY-09 Helps to Restore Cerebral Glucose Metabolism in 3×Tg-AD Mice
Han, S., He, Z., Hu, X., Li, X., Zheng, K., Huang, Y., Xiao, P., Xie, Q., Ni, J., & Liu, Q. (2023) · Antioxidants · doi:10.3390/antiox12030722 · Cited by 34
Higher Reliance on Glycolysis Limits Glycolytic Responsiveness in Degenerating Glaucomatous Optic Nerve
Jassim, A. H., Coughlin, L., Harun‐Or‐Rashid, M., Kang, P. T., Chen, Y., & Inman, D. M. (2019) · Molecular Neurobiology · doi:10.1007/s12035-019-1576-4 · Cited by 34
Universal Glia to Neurone Lactate Transfer in the Nervous System: Physiological Functions and Pathological Consequences
Powell, C. L., Davidson, A. R., & Brown, A. M. (2020) · Biosensors · doi:10.3390/bios10110183 · Cited by 33
The Interaction Between Sleep and Metabolism in Alzheimer’s Disease: Cause or Consequence of Disease?
Carroll, C. M., & Macauley, S. L. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00258 · Cited by 32
What is bipolar disorder? A disease model of dysregulated energy expenditure
Mansur, R. B., Lee, Y. L., McIntyre, R. S., & Brietzke, E. M. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.006 · Cited by 33
Trans-Synaptic Spread of Amyloid-βin Alzheimer’s Disease: Paths toβ-Amyloidosis
Pignataro, A., & Middei, S. (2017) · Neural Plasticity · doi:10.1155/2017/5281829 · Cited by 34
Feasibility of simultaneous whole-brain imaging on an integrated PET-MRI system using an enhanced 2-point Dixon attenuation correction method
Anazodo, U., Thiessen, J. D., Ssali, T., Mandel, J., nther, M. G. ⁄., Butler, J., Pavlosky, W. F., Prato, F. S., Thompson, R. T., & Lawrence, K. S. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00434 · Cited by 31
Multimodal Imaging of Alzheimer Pathophysiology in the Brain′s Default Mode Network
Shin, J., Kepe, V., Small, G. W., Phelps, M. E., & Barrio, J. R. (2011) · International Journal of Alzheimer s Disease · doi:10.4061/2011/687945 · Cited by 32
KATP channels are necessary for glucose-dependent increases in amyloid-β and Alzheimer’s disease–related pathology
Grizzanti, J., Moritz, W. R., Pait, M. C., Stanley, M., Kaye, S. D., Carroll, C. M., Constantino, N. J., Deitelzweig, L. J., Snipes, J. A., Kellar, D., et al. (2023) · JCI Insight · doi:10.1172/jci.insight.162454 · Cited by 32
The energetic basis of behavior: bridging behavioral ecology and neuroscience
Rittschof, C. C., Grozinger, C. M., & Robinson, G. E. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2015.07.006 · Cited by 28
Aerobic Glycolysis as a Marker of Tumor Aggressiveness: Preliminary Data in High Grade Human Brain Tumors
Vlassenko, A. G., McConathy, J., Couture, L. E., Su, Y., Massoumzadeh, P., Leeds, H., Chicoine, M. R., Tran, D. D., Huang, J., Dahiya, S. M., et al. (2015) · Disease Markers · doi:10.1155/2015/874904 · Cited by 29
Self-citation
Co-opting the fermentation pathway for tombusvirus replication: Compartmentalization of cellular metabolic pathways for rapid ATP generation
Lin, W., Liu, Y., Molho, M., Zhang, S., Wang, L., Xie, L., & Nagy, P. D. (2019) · PLoS Pathogens · doi:10.1371/journal.ppat.1008092 · Cited by 30
Effects of Brain Amyloid Deposition and Reduced Glucose Metabolism on the Default Mode of Brain Function in Normal Aging
Kikuchi, M., Hirosawa, T., Yokokura, M., Yagi, S., Mori, N., Yoshikawa, E., Yoshihara, Y., Sugihara, G., Takebayashi, K., Iwata, Y., et al. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.2535-11.2011 · Cited by 31
Upregulation of Glycolytic Enzymes, Mitochondrial Dysfunction and Increased Cytotoxicity in Glial Cells Treated with Alzheimer’s Disease Plasma
Jayasena, T., Poljak, A., Braidy, N., Smythe, G., Raftery, M., Hill, M., Brodaty, H., Trollor, J., Kochan, N., & Sachdev, P. (2015) · PLOS ONE · doi:10.1371/journal.pone.0116092 · Cited by 30
Exploring glycolytic enzymes in disease: potential biomarkers and therapeutic targets in neurodegeneration, cancer and parasitic infections
Rojas-Pirela, M., Andrade-Alviárez, D., Rojas, V., Marcos, M., Salete-Granado, D., Chacón-Arnaude, M., Pérez-Nieto, M. Á., Kemmerling, U., Concepción, J. L., Michels, P. A. M., et al. (2025) · Open Biology · doi:10.1098/rsob.240239 · Cited by 31
Sex and menopause impact 31P-Magnetic Resonance Spectroscopy brain mitochondrial function in association with 11C-PiB PET amyloid-beta load
Jett, S., Dyke, J. P., Andy, C., Schelbaum, E., Jang, G., Boneu, C., Pahlajani, S. S., Diaz, I., Brinton, R. D., & Mosconi, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-26573-5 · Cited by 29
How Do Modifiable Risk Factors Affect Alzheimer’s Disease Pathology or Mitigate Its Effect on Clinical Symptom Expression?
Ourry, V., Binette, A. P., St‐Onge, F., Strikwerda‐Brown, C., Chagnot, A., Poirier, J., Breitner, J. C. S., Arenaza‐Urquijo, E. M., Rabin, J. S., Buckley, R. F., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.09.003 · Cited by 30
Age-dependent effect of Alzheimer’s risk variant of CLU on EEG alpha rhythm in non-demented adults
Пономарева, Н. В., Андреева, Т. В., Protasova, M., Shagam, L., Malina, D. D., Goltsov, A., Fokin, V. F., Алексеевич, М. А., & Rogaev, E. I. (2013) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2013.00086 · Cited by 29
„Default-mode“-Netzwerk des Gehirns
Otti, A., Gündel, H., Wohlschläger, A., Zimmer, C., Sorg, C., & Noll‐Hussong, M. (2011) · Der Nervenarzt · doi:10.1007/s00115-011-3307-6 · Cited by 30
Memory decline shows stronger associations with estimated spatial patterns of amyloid deposition progression than total amyloid burden
Yotter, R. A., Doshi, J. J., Clark, V., Sojkova, J., Zhou, Y., Wong, D. F., Ferrucci, L., Resnick, S. M., & Davatzikos, C. A. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.05.030 · Cited by 29
Comparison of Amyloid β and Tau Spread Models in Alzheimer’s Disease
Kim, H., Lee, P., Seo, S. W., Roh, J. H., Oh, M., Oh, J. S., Oh, S. J., Kim, J. S., & Jeong, Y. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy311 · Cited by 32
Role of astrocytic glycolytic metabolism in Alzheimer’s disease pathogenesis
Fu, W., & Jhamandas, J. H. (2014) · Biogerontology · doi:10.1007/s10522-014-9525-0 · Cited by 28
Suboptimal validity of amyloid imaging-based diagnosis and management of Alzheimer’s disease: why it is time to abandon the approach
Alavi, A. A., Barrio, J. R., Werner, T. J., Khosravi, M., Newberg, A. B., & Høilund‐Carlsen, P. F. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04564-5 · Cited by 28
Beyond amyloid: Immune, cerebrovascular, and metabolic contributions to Alzheimer disease in people with Down syndrome
Martini, A. C., Gross, T. J., Head, E., & Mapstone, M. E. (2022) · Neuron · doi:10.1016/j.neuron.2022.04.001 · Cited by 26
Quantitative hemodynamic PET imaging using image-derived arterial input function and a PET/MR hybrid scanner
Su, Y., Vlassenko, A. G., Couture, L. E., Benzinger, T. L. S., Snyder, A. Z., Derdeyn, C. P., & Raichle, M. E. (2016) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x16656200 · Cited by 27
Self-citation
Contribution of polymorphic variation of inositol hexakisphosphate kinase 3 ( IP6K3 ) gene promoter to the susceptibility to late onset Alzheimer's disease
Crocco, P., Saiardi, A., Wilson, M., Maletta, R., Bruni, A. C., Passarino, G., & Rose, G. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2016.06.014 · Cited by 29
Deficits in Mitochondrial Spare Respiratory Capacity Contribute to the Neuropsychological Changes of Alzheimer’s Disease
Bell, S., Marco, M. D., Barnes, K., Shaw, P. J., Ferraiuolo, L., Blackburn, D. J., Mortiboys, H., & Venneri, A. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10020032 · Cited by 30
The Effects of Amyloid and Tau on Functional Network Connectivity in Older Populations
Wales, R. M., & Leung, H.-C. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0902 · Cited by 26
The controllosphere: The neural origin of cognitive effort.
Holroyd, C. B. (2024) · Psychological Review · doi:10.1037/rev0000467 · Cited by 33
The Energetic Collapse of the Alzheimer's Brain: Metabolic Inflexibility Across Cells and Networks
Constantino, N. J., Ashley, C. C., & Macauley, S. L. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70294 · Cited by 29
Functional integrity in children with anoxic brain injury from drowning
Ishaque, M., Manning, J. H., Woolsey, M. D., Franklin, C. G., Tullis, E. W., Beckmann, C. F., & Fox, P. T. (2017) · Human Brain Mapping · doi:10.1002/hbm.23745 · Cited by 27
Astrocytic transporters in Alzheimer's disease
Ugbode, C., Hu, Y., Whalley, B., Peers, C., Rattray, M., & Dallas, M. L. (2017) · Biochemical Journal · doi:10.1042/bcj20160505 · Cited by 25
Hyperpolarized [1-13C] pyruvate MR spectroscopy detect altered glycolysis in the brain of a cognitively impaired mouse model fed high-fat diet
Choi, Y., Kang, S., Ko, S.-Y., Lee, S., Kim, J. Y., Lee, H., Song, J. E., Kim, D., Kim, E., Kim, C. H., et al. (2018) · Molecular Brain · doi:10.1186/s13041-018-0415-2 · Cited by 27
Impact of Genetic Risk Factors for Alzheimer’s Disease on Brain Glucose Metabolism
Cho, S., Lee, H., & Seo, J. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02297-x · Cited by 25
Distinct spatiotemporal accumulation of N-truncated and full-length amyloid-β42 in Alzheimer’s disease
Shinohara, M., Koga, S., Konno, T., Nix, J., Shinohara, M., Aoki, N., Das, P., Parisi, J. E., Petersen, R. C., Rosenberry, T. L., et al. (2017) · Brain · doi:10.1093/brain/awx284 · Cited by 24
A quantitative model of human neurodegenerative diseases involving protein aggregation
Kepp, K. P. (2019) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2019.04.001 · Cited by 23
Using stable isotope tracing to unravel the metabolic components of neurodegeneration: Focus on neuron-glia metabolic interactions
Westi, E. W., Andersen, J. V., & Aldana, B. I. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106145 · Cited by 23
The Potential of Fasting-Mimicking Diet as a Preventive and Curative Strategy for Alzheimer’s Disease
Boccardi, V., Pigliautile, M., Guazzarini, A. G., & Mecocci, P. (2023) · Biomolecules · doi:10.3390/biom13071133 · Cited by 26
Optimization of intracerebroventricular streptozotocin dose for the induction of neuroinflammation and memory impairments in rats
Ghosh, R., Sil, S., Gupta, P., & Ghosh, T. (2020) · Metabolic Brain Disease · doi:10.1007/s11011-020-00588-1 · Cited by 24
Ketotherapeutics for neurodegenerative diseases
Norwitz, N. G., Gilbert‐Jaramillo, J., Clarke, K., & Soto-Mota, A. (2020) · International review of neurobiology · doi:10.1016/bs.irn.2020.02.003 · Cited by 24
The Brain’s Structural Connectome Mediates the Relationship between Regional Neuroimaging Biomarkers in Alzheimer’s Disease
Pandya, S., Initiative, A. D. N., Kuceyeski, A. F., & Raj, A. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160090 · Cited by 24
5‐Methoxyindole‐2‐carboxylic acid (MICA) suppresses A&bgr;‐mediated pathology in C. elegans
Ahmad, W., & Ebert, P. (2018) · Experimental Gerontology · doi:10.1016/j.exger.2018.04.021 · Cited by 19
Communicating 5-Year Risk of Alzheimer’s Disease Dementia: Development and Evaluation of Materials that Incorporate Multiple Genetic and Biomarker Research Results
Mozersky, J. T., Hartz, S. M., Linnenbringer, E. L., Levin, L., Streitz, M. L., Stock, K., Moulder, K. L., & Morris, J. C. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-200993 · Cited by 24
Self-citation
BACE1 activity impairs neuronal glucose oxidation: rescue by beta-hydroxybutyrate and lipoic acid
Findlay, J. A., Hamilton, D. L., & Ashford, M. L. J. (2015) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2015.00382 · Cited by 22
Association of aerobic glycolysis with the structural connectome reveals a benefit–risk balancing mechanism in the human brain
Chen, Y., Lin, Q., Liao, X., Zhou, C., & Yong He, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2013232118 · Cited by 22
A quantitative neural network approach to understanding aging phenotypes
Ash, J. A., & Rapp, P. R. (2014) · Ageing Research Reviews · doi:10.1016/j.arr.2014.02.001 · Cited by 20
System evaluation of automated production and inhalation of 15O-labeled gaseous radiopharmaceuticals for the rapid 15O-oxygen PET examinations
Iguchi, S., Moriguchi, T., Yamazaki, M., Hori, Y., Koshino, K., Toyoda, Ḱ., Teuho, J., Shimochi, S., Terakawa, Y., Fukuda, T., et al. (2018) · EJNMMI Physics · doi:10.1186/s40658-018-0236-5 · Cited by 21
β-amyloid monomers drive up neuronal aerobic glycolysis in response to energy stressors
Santangelo, R., Giuffrida, M. L., Satriano, C., Tomasello, M. F., Zimbone, S., & Copani, A. (2021) · Aging · doi:10.18632/aging.203330 · Cited by 21
Should Alzheimer's disease be equated with human brain ageing?: A maladaptive interaction between brain evolution and senescence
Neill, D. (2011) · Ageing Research Reviews · doi:10.1016/j.arr.2011.06.004 · Cited by 22
Hypoperfusion is a potential inducer of immunosuppressive network in Alzheimer's disease
Salminen, A. (2020) · Neurochemistry International · doi:10.1016/j.neuint.2020.104919 · Cited by 20
Synaptic Plasticity and the Warburg Effect
Magistretti, P. J. (2014) · Cell Metabolism · doi:10.1016/j.cmet.2013.12.012 · Cited by 20
The effect of amyloid deposition on longitudinal resting-state functional connectivity in cognitively normal older adults
Lin, C., Ly, M., Karim, H. T., Wei, W., Snitz, B. E., Klunk, W. E., & Aizenstein, H. J. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-019-0573-1 · Cited by 19
Glycolytic dysregulation in Alzheimer’s disease: unveiling new avenues for understanding pathogenesis and improving therapy
Wu, Y., Yang, L., Jiang, W., Zhang, X., & Yao, Z. (2024) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-24-00190 · Cited by 19
L-Lactate Administration Improved Synaptic Plasticity and Cognition in Early 3xTg-AD Mice
Wang, Y., Zhao, J., & Zhao, L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26041486 · Cited by 19
Hospitalisation for COVID-19 predicts long lasting cerebrovascular impairment: A prospective observational cohort study
Tsvetanov, K. A., Spindler, L. R. B., Stamatakis, E. A., Newcombe, V., Lupson, V. C., Chatfield, D. A., Manktelow, A. E., Outtrim, J. G., Elmer, A., Kingston, N., et al. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103253 · Cited by 20
Deuterium Metabolic Imaging of Alzheimer Disease at 3-T Magnetic Field Strength: A Pilot Case-Control Study
Khan, A. S., Peterson, K. A., Vittay, O. I., McLean, M. A., Kaggie, J. D., O'Brien, J. T., Rowe, J. B., Gallagher, F. A., & Matys, T. (2024) · Radiology · doi:10.1148/radiol.232407 · Cited by 19
The Relationship between Amyloid Deposition, Neurodegeneration, and Cognitive Decline in Dementia
Vandenberghe, R. R. (2014) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-014-0498-9 · Cited by 21
Amelioration of cognitive impairments in APPswe/PS1dE9 mice is associated with metabolites alteration induced by total salvianolic acid
Shen, L., Han, B., Geng, Y., Wang, J., Wang, Z., & Wang, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174763 · Cited by 19
Activation of Adenosine Monophosphate-Activated Protein Kinase Drives the Aerobic Glycolysis in Hippocampus for Delaying Cognitive Decline Following Electroacupuncture Treatment in APP/PS1 Mice
Li, J., Zhang, B., Jia, W., Yang, M., Zhang, Y., Zhang, J., Li, L., Jin, T., Wang, Z., Tao, J., et al. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.774569 · Cited by 19
Altered functional connectivity strength in informant‐reported subjective cognitive decline: A resting‐state functional magnetic resonance imaging study
Dong, C., Liu, T., Wen, W., Kochan, N. A., Jiang, J., Li, Q., Liu, H., Niu, H., Zhang, W., Wang, Y., et al. (2018) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.08.011 · Cited by 18
Hypoxia/ischemia impairs CD33 (Siglec-3)/TREM2 signaling: Potential role in Alzheimer's pathogenesis
Salminen, A., Kaarniranta, K., & Kauppinen, A. (2021) · Neurochemistry International · doi:10.1016/j.neuint.2021.105186 · Cited by 20
Brain Energy Metabolism in Health and Disease
Felipe, A., Anbal, I., Paz, M., & Maite, A. (2012) · InTech eBooks · doi:10.5772/36092 · Cited by 18
Intermittent fasting and Alzheimer's disease—Targeting ketone bodies as a potential strategy for brain energy rescue
Ye, Y., Chai, S.-F., Li, X.-R., Wu, M., Cai, H.-Y., & Wang, Z.-J. (2023) · Metabolic Brain Disease · doi:10.1007/s11011-023-01288-2 · Cited by 18
Quantitative Analysis of the Effective Functional Structure in Yeast Glycolysis
la Fuente, I. M. D., & Cortés, J. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0030162 · Cited by 19
Mechanisms underlying pathological cortical bursts during metabolic depletion
Dutta, S., Iyer, K. K., Vanhatalo, S., Breakspear, M. J., & Roberts, J. A. (2023) · Nature Communications · doi:10.1038/s41467-023-40437-0 · Cited by 16
Resting‐State Glucose Metabolism Level Is Associated with the Regional Pattern of Amyloid Pathology in Alzheimer′s Disease
Shin, J., Tsui, W., Li, Y., Lee, S., Kim, S. J., Cho, S., Kim, Y.-B., & de Leon, M. J. (2011) · International Journal of Alzheimer s Disease · doi:10.4061/2011/759780 · Cited by 16
Spatiotemporal relationship between subthreshold amyloid accumulation and aerobic glycolysis in the human brain
Goyal, M. S., Gordon, B. A., Couture, L. E., Flores, S. E., Xiong, C., Morris, J. C., Raichle, M. E., Benzinger, T. L. S., & Vlassenko, A. G. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.08.019 · Cited by 16
Self-citation
Rates of β-amyloid deposition indicate widespread simultaneous accumulation throughout the brain
LaPoint, M. R., Baker, S. L., Landau, S. M., Harrison, T. M., & Jagust, W. J. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.03.005 · Cited by 18
Pharmacologic enrichment of exosome yields and mitochondrial cargo
Wang, X., Berkowicz, A., King, K., Menta, B. W., Gabrielli, A. P., Novikova, L., Troutwine, B., Pleen, J., Wilkins, H., & Swerdlow, R. H. (2022) · Mitochondrion · doi:10.1016/j.mito.2022.04.001 · Cited by 16
Low Residual CBF Variability in Alzheimer’s Disease after Correction for CO2 Effect
Rodell, A. B., Aanerud, J., Brændgaard, H., & GJEDDE, A. H. (2012) · Frontiers in Neuroenergetics · doi:10.3389/fnene.2012.00008 · Cited by 15
Neuroimaging Biomarkers of Caloric Restriction on Brain Metabolic and Vascular Functions
Lin, A., Parikh, I. J., Hoffman, J. D., & Ma, D. (2017) · Current Nutrition Reports · doi:10.1007/s13668-017-0187-9 · Cited by 16
The significance of glycolysis index and its correlations with immune infiltrates in Alzheimer’s disease
Qiu, Z., Bai, X., Ji, X., Wang, X., Han, X., Wang, D., Jiang, F., & An, Y. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.960906 · Cited by 15
Altered resting-state functional connectivity patterns in late middle-aged and older adults with obstructive sleep apnea
Villar, G. M., Daneault, V., Martineau‐Dussault, M., Baril, A., Gagnon, K., Lafond, C., Gilbert, D., Thompson, C., Marchi, N. A., Lina, J., et al. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1215882 · Cited by 15
The Role of Microglia with Mitochondrial Dysfunction and Its Therapeutic Prospects in Alzheimer's Disease
Li, Y., Li, T., Chen, T., Li, C., Yu, W., Xu, Y., Zeng, X., & Zheng, F. (2024) · Journal of Integrative Neuroscience · doi:10.31083/j.jin2305091 · Cited by 15
Urinary Homocysteic Acid Levels Correlate with Mini-Mental State Examination Scores in Alzheimer's Disease Patients
Hasegawa, T., Ichiba, M., Matsumoto, S., Kasanuki, K., Hatano, T., Fujishiro, H., Iseki, E., Hattori, N., Yamada, T., & Tabira, T. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-120022 · Cited by 13
Proenergetic effects of resveratrol in the murine neuronal cell line Neuro2a
Nguyen, N. T. Q., Ooi, L., Piller, S. C., & Münch, G. W. (2013) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201300145 · Cited by 13
Distribution of Amyloid Burden Differs between Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease
Kondo, M., Tokuda, T., Itsukage, M., Kuriyama, N., Matsushima, S., Yamada, K., Nakanishi, H., Ishikawa, M., & Nakagawa, M. (2013) · The Neuroradiology Journal · doi:10.1177/197140091302600107 · Cited by 13
Hypothesis and Theory: Characterizing Abnormalities of Energy Metabolism Using a Cellular Platform as a Personalized Medicine Approach for Alzheimer’s Disease
Ryu, W., Cohen, B. M., & Sonntag, K. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.697578 · Cited by 14
Correlation of brain amyloid with “aerobic glycolysis”: a question of assumptions?
Phelps, M. E., & Barrio, J. R. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1012684107 · Cited by 12
A whole‐brain functional connectivity model of Alzheimer's disease pathology
Prakash, R. S., McKenna, M. R., Gbadeyan, O., Shankar, A., Pugh, E., Teng, J., Andridge, R., Berry, A. S., & Scharre, D. W. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14349 · Cited by 12
Phosphoproteomic Profiling of Selenate-Treated Alzheimer's Disease Model Cells
Chen, P., Wang, L., Wang, Y., Li, S., Shen, L., Liu, Q., & Ni, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0113307 · Cited by 12
Time‐dependent impact of type 2 diabetes mellitus on incident prodromal Alzheimer disease: A longitudinal study in 1395 participants
Luo, T., Tu, Y., Huang, S., Ma, Y., Wang, Q., Wang, Y., Wang, J., & Initiative, A. D. N. (2023) · European Journal of Neurology · doi:10.1111/ene.15868 · Cited by 13
ApoE: The link between Alzheimer’s-related glucose hypometabolism and Aβ deposition?
Patil, S. P., Ballard, R., Sanchez, S., Osborn, J., & Santangelo, D. (2012) · Medical Hypotheses · doi:10.1016/j.mehy.2012.01.015 · Cited by 13
Supragranular Pyramidal Cells Exhibit Early Metabolic Alterations in the 3xTg-AD Mouse Model of Alzheimer’s Disease
Piquet, J., Toussay, X., Hepp, R., Lerchundi, R., Douce, J. L., Faivre, É., Guiot, E., Bonvento, G., & Cauli, B. (2018) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2018.00216 · Cited by 13
Astrocytes as Metabolic Sensors Orchestrating Energy‐Driven Brain Vulnerability in Alzheimer's Disease
de Muniain, L. S., Escalada, P., Ramírez, M. J., & Solas, M. (2025) · Journal of Neurochemistry · doi:10.1111/jnc.70252 · Cited by 11
Cerebral perfusion correlates with amyloid deposition in patients with mild cognitive impairment due to Alzheimer's disease
Wang, C., Ji, D., Su, X., Liu, F., Zhang, Y., Lu, Q., Cai, L., Wang, Y., Qin, W., Xing, G., et al. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2024.100031 · Cited by 10
Statistical mechanics of interacting metabolic networks
Fernández-de-Cossio-Díaz, J., & Mulet, R. (2020) · Physical review. E · doi:10.1103/physreve.101.042401 · Cited by 10
Persistent changes in extracellular lactate dynamics following synaptic potentiation
Bingul, D., Kalra, K., Murata, E. M., Belser, A., & Dash, M. B. (2020) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2020.107314 · Cited by 13
Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades
Li, K., Laird, A. R., Price, L. R., McKay, D., Blangero, J., Glahn, D. C., & Fox, P. T. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00137 · Cited by 10
Qualitative agreement and diagnostic performance of arterial spin labelling MRI and FDG PET-CT in suspected early-stage dementia
Weyts, K., Vernooij, M. W., Steketee, R. M. E., Valkema, R., & Smits, M. (2017) · Clinical Imaging · doi:10.1016/j.clinimag.2017.05.008 · Cited by 11
Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain
Klug, S., Murgaš, M., Godbersen, G. M., Hacker, M., Lanzenberger, R. R., & Hahn, A. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120658 · Cited by 8
Carnosine supplementation improves cognitive outcomes in younger participants of the NEAT trial
O’Toole, T. E., Amraotkar, A. R., Gao, H. T., Sears, C. G., Rai, S. N., Basner, M., & Bhatnagar, A. (2025) · Neurotherapeutics · doi:10.1016/j.neurot.2025.e00541 · Cited by 9
Monitoring Acidification Preceding Aβ Deposition in Alzheimer's Disease
Zhu, G., Zhang, H., Xie, R., Younis, M. R., Fu, S., Wang, X., Liu, B., Li, K., Lui, S., & Wu, M. C. (2025) · Advanced Healthcare Materials · doi:10.1002/adhm.202404907 · Cited by 8
Mitochondrial Dysfunction Correlates with Brain Amyloid Binding, Memory, and Executive Function in Down Syndrome: Implications for Alzheimer’s Disease in Down Syndrome
Beresford‐Webb, J., McAllister, C. J., Sleigh, A., Walpert, M., Holland, A. J., & Zaman, S. H. (2025) · Brain Sciences · doi:10.3390/brainsci15020130 · Cited by 10
Brain Under Fatigue – Can Perceived Fatigability in Multiple Sclerosis Be Seen on the Level of Functional Brain Network Architecture?
Sobczak, 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
Locus Coeruleus and Noradrenergic Pharmacology in Neurodegenerative Disease
Matt, R. A., Martin, R. S., Evans, A. K., Gever, J. R., Vargas, G. A., Shamloo, M., & Ford, A. P. D. W. (2023) · Handbook of experimental pharmacology · doi:10.1007/164_2023_677 · Cited by 11
The Effects of Capillary Transit Time Heterogeneity (CTH) on the Cerebral Uptake of Glucose and Glucose Analogs: Application to FDG and Comparison to Oxygen Uptake
Angleys, H., Jespersen, S. N., & Østergaard, L. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00103 · Cited by 8
Reply to Biskup et al. and Tu et al.: Sex differences in metabolic brain aging
Goyal, M. S., Vlassenko, A. G., & Raichle, M. E. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1904673116 · Cited by 8
Self-citation

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: Vlassenko, Andrei G.
Vaishnavi, S. Neil
Sacco, Dana
Mach, Robert H.
Morris, John C.
Raichle, Marcus E.
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
DOI: 10.1073/pnas.1010461107
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

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Spatial correlation between brain aerobic glycolysis and amyloid-β (Aβ) deposition

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks