ArticleProceedings of the National Academy of Sciences of the United States of America2015
Cortical network models of impulse firing in the resting and active states predict cortical energetics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis.Frontiers in systems neuroscience · 2021Review
- Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks.Frontiers in neuroscience · 2017Article
- Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses.PloS one · 2015Article
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4 authors.
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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)).
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