Resting state neural networks and energy metabolism
Abstract
The human brain is an energy hungry organ. How that brain manages its energy consumption in maintaining its health and executing sensori-motor and cognitive functions is an important but overlooked research area in contemporary cognitive neuroscience. It is argued here that the principal method whereby the human brain manages its energy utilization is through maintaining a relatively elevated level of activity in what can be referred to as “resting state networks” (RSN). The elevated energy consumption in the human brain's varied RSNs is driven and maintained by a physiological mechanism we call the Frame-Formation Energy Cycle (FFEC). Running the FFEC cycle is metabolically expensive and therefore offers a mechanism to explain the increased energy consumption in human-brain RSNs as compared to regions not involved in such networks.
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Aerobic glycolysis and lactate might reflect cortical information processing and, in turn, intracortical communication.
“In particular, aerobic glycolysis and lactate might reflect cortical information processing and, in turn, intracortical communication”
From Perception is associated with the brain’s metabolic response to sensory stimulation (2022) · cites this paper as Noack et al., 2017
The multi-level model (p. 4) links neuron–glia metabolism, including a lactate shuttle, to activity patterns that carry cognitive content. The Conclusions (p. 7) suggest that the lactate shuttle may supply energy for those patterns and relate energy metabolism to functional connectivity. Our copy does not establish that aerobic glycolysis and lactate reflect cortical information processing or, specifically, intracortical communication. (LLM, conf=0.76)
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Cites
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680 · see citing works
Hebb, D. O. (2005) · doi:10.4324/9781410612403 · Cited by 9,825 · see citing works
indicated, N. A. (1950) · Journal of Consulting Psychology · doi:10.1037/h0049612 · Cited by 6,984 · see citing works
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700 · see citing works
Simple model of spiking neurons
Izhikevich, E. M. (2003) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2003.820440 · Cited by 4,828 · see citing works
Exploring the brain network: A review on resting-state fMRI functional connectivity
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2010.03.008 · Cited by 3,326 · see citing works
Sridharan, D., Levitin, D. J., & Menon, V. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800005105 · Cited by 2,892 · see citing works
Decoding subject-driven cognitive states with whole-brain connectivity patterns
Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012) · Cerebral Cortex · doi:10.1093/cercor/bhr099 · Cited by 1,843 · see citing works
Emerging concepts for the dynamical organization of resting-state activity in the brain
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn2961 · Cited by 1,798 · see citing works
Mass action in the nervous system
Freeman, W. J. (1975) · Cited by 1,480 · see citing works
Mass action in the nervous system
Jindra, R. H. (1976) · Neuroscience · doi:10.1016/0306-4522(76)90135-4 · Cited by 653 · see citing works
Cognitive relevance of the community structure of the human brain functional coactivation network
Crossley, N. A., Mechelli, A., Vértes, P. E., Winton‐Brown, T., Patel, A. X., Ginestet, C. E., McGuire, P. K., & Bullmore, E. T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220826110 · Cited by 449 · see citing works
Haimovici, A., Tagliazucchi, E., Balenzuela, P., & Chialvo, D. R. (2013) · Physical Review Letters · doi:10.1103/physrevlett.110.178101 · Cited by 438 · see citing works
Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework
Ros, T., Baars, B. J., Lanius, R. A., & Vuilleumier, P. O. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01008 · Cited by 263 · see citing works
Raichle, M. E. (2010) · Scientific American · doi:10.1038/scientificamerican0310-44 · Cited by 158 · see citing works
An integrative dynamic model of brain energy metabolism using in vivo neurochemical measurements
Cloutier, M., Bolger, F. B., Lowry, J. P., & Wellstead, P. E. (2009) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0152-8 · Cited by 111 · see citing works
Jolivet, R., Coggan, J. S., Allaman, I., & Magistretti, P. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004036 · Cited by 105 · see citing works
Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking
Heinonen, J., Numminen, J., Hlushchuk, Y., Antell, H., Taatila, V. P., & Suomala, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162234 · Cited by 92 · see citing works
A cinematographic hypothesis of cortical dynamics in perception
Freeman, W. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2005.12.009 · Cited by 74 · see citing works
Kozma, R., & Freeman, W. (2015) · Studies in systems, decision and control · doi:10.1007/978-3-032-00242-6 · Cited by 74 · see citing works
Self-citationProposed Cortical “Shutter” Mechanism in Cinematographic Perception
Freeman, W. (2007) · Springer eBooks · doi:10.1007/978-3-540-73267-9_2 · Cited by 37 · see citing works
Haatveit, B. C., Jensen, J., Alnæs, D., Kaufmann, T., Brandt, C. L., Thoresen, C., Andreassen, O. A., Melle, I. S., Ueland, T., & Westlye, L. T. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.08.012 · Cited by 25 · see citing works
Solving the “human problem”: The frontal feedback model
Noack, R. A. (2012) · Consciousness and Cognition · doi:10.1016/j.concog.2012.01.011 · Cited by 18 · see citing works
Self-citationCalcium Signaling in Cerebral Vasoregulation
Ghosh, S., & Basu, A. (2012) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-2888-2_37 · Cited by 2 · see citing works
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Perception is associated with the brain’s metabolic response to sensory stimulation
DiNuzzo, M., Mangia, S., Moraschi, M., Mascali, D., Hagberg, G. E., & Giove, F. (2022) · eLife · doi:10.7554/elife.71016 · Cited by 25 · see citing works
- Authors
- Noack, R., Manjesh, C., Ruszinko, M., Siegelmann, H., & Kozma, R.
- Affiliations
- University of Massachusetts Amherst (US)
- ORCID
- Siegelmann, Hava
- Venue
- 2017 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 228-235
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2734505272
- Cited by
- 12
- References
- 24
- Citations by year
- 2026: 1 · 2024: 1 · 2022: 4 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 1
- FWCI
- 2.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- Funders
- National Science Foundation
- Defense Advanced Research Projects Agency
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- resting-state networks, brain energy metabolism, brain energy consumption, neuronal activity, cognitive neuroscience
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