Resting state neural networks and energy metabolism

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.

[1]  M. Raichle The brain's dark energy. , 2010 .

[2]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

[3]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[4]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[5]  Walter J. Freeman,et al.  Proposed Cortical “Shutter” Mechanism in Cinematographic Perception , 2007 .

[6]  Walter J Freeman,et al.  A cinematographic hypothesis of cortical dynamics in perception. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[7]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[8]  Cedric E. Ginestet,et al.  Cognitive relevance of the community structure of the human brain functional coactivation network , 2013, Proceedings of the National Academy of Sciences.

[9]  T. Ros,et al.  Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework , 2014, Front. Hum. Neurosci..

[10]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[11]  Robert Kozma,et al.  Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields , 2015 .

[12]  Donald O. Walter,et al.  Mass action in the nervous system , 1975 .

[13]  A. Basu,et al.  Calcium signaling in cerebral vasoregulation. , 2012, Advances in experimental medicine and biology.

[14]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[15]  Dante R Chialvo,et al.  Brain organization into resting state networks emerges at criticality on a model of the human connectome. , 2012, Physical review letters.

[16]  I. Melle,et al.  Reduced load-dependent default mode network deactivation across executive tasks in schizophrenia spectrum disorders , 2016, NeuroImage: Clinical.

[17]  Jyrki Suomala,et al.  Default Mode and Executive Networks Areas: Association with the Serial Order in Divergent Thinking , 2016, PloS one.

[18]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[19]  V. Menon,et al.  A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks , 2008, Proceedings of the National Academy of Sciences.

[20]  John P. Lowry,et al.  An integrative dynamic model of brain energy metabolism using in vivo neurochemical measurements , 2009, Journal of Computational Neuroscience.

[21]  Raymond Noack Solving the “human problem”: The frontal feedback model , 2012, Consciousness and Cognition.

[22]  Pierre J. Magistretti,et al.  Multi-timescale Modeling of Activity-Dependent Metabolic Coupling in the Neuron-Glia-Vasculature Ensemble , 2015, PLoS Comput. Biol..