title: Cortical network models of impulse firing in the resting and active states predict cortical energetics.
abstract: Measurements of the cortical metabolic rate of glucose oxidation [CMR(glc(ox))] have provided a number of interesting and, in some cases, surprising observations. One is the decline in CMR(glc(ox)) during anesthesia and non-rapid eye movement (NREM) sleep, and another, the inverse relationship between the resting-state CMR(glc(ox)) and the transient following input from the thalamus. The recent establishment of a quantitative relationship between synaptic and action potential activity on the one hand and CMR(glc(ox)) on the other allows neural network models of such activity to probe for possible mechanistic explanations of these phenomena. We have carried out such investigations using cortical models consisting of networks of modules with excitatory and inhibitory neurons, each receiving excitatory inputs from outside the network in addition to intermodular connections. Modules may be taken as regions of cortical interest, the inputs from outside the network as arising from the thalamus, and the intermodular connections as long associational fibers. The model shows that the impulse frequency of different modules can differ from each other by less than 10%, consistent with the relatively uniform CMR(glc(ox)) observed across different regions of cortex. The model also shows that, if correlations of the average impulse rate between different modules decreases, there is a concomitant decrease in the average impulse rate in the modules, consistent with the observed drop in CMR(glc(ox)) in NREM sleep and under anesthesia. The model also explains why a transient thalamic input to sensory cortex gives rise to responses with amplitudes inversely dependent on the resting-state frequency, and therefore resting-state CMR(glc(ox)).
authors.affiliation: Brain and Mind Research Institute, University of Sydney, Camperdown, NSW 2050, Australia; and Centre for Mathematical Biology and max.bennett@sydney.edu.au.
authors.forename: Maxwell R
authors.initials: MR
authors.lastname: Bennett
authors.affiliation: Centre for Mathematical Biology and School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Les
authors.initials: L
authors.lastname: Farnell
authors.affiliation: Centre for Mathematical Biology and School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: William G
authors.initials: WG
authors.lastname: Gibson
authors.affiliation: Brain and Mind Research Institute, University of Sydney, Camperdown, NSW 2050, Australia; and.
authors.forename: Jim
authors.initials: J
authors.lastname: Lagopoulos
mesh_terms: D000200:Action Potentials; D000758:Anesthesia; D000818:Animals; D001369:Axons / Q000378:metabolism; D002540:Cerebral Cortex / Q000378:metabolism / Q000502:physiology; D005947:Glucose / Q000378:metabolism*; D006801:Humans; D023421:Models, Animal; D008959:Models, Neurological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D010100:Oxygen / Q000378:metabolism*; D016012:Poisson Distribution; D012890:Sleep; D009435:Synaptic Transmission; D013788:Thalamus / Q000502:physiology
chemical_list: D005947:Glucose; D010100:Oxygen
keywords: cortical energetic; cortical networks; impulse firing; resting-state networks