Resting state neural networks and energy metabolism

Abstract

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.

Perception affects the brain’s metabolic response to sensory stimulation

“…In particular, elevated brain lactate and glutamate levels are associated with wakefulness and memory formation, which naturally require the processing of incoming sensory stimuli, like the control exerted by the central visual pathways for either gating or filtering out behaviorally relevant or irrelevant visual information. In particular, aerobic glycolysis and lactate might reflect cortical information processing and, in turn, intracortical communication, in agreement with the relation between regional metabolic rates of glucose utilization and resting-state network dynamics in the cerebral cortex (80)(81)(82)(83)(84).…”

Energy Constraints Improve Liquid State Machine Performance

“…Another work by the same lab, that of [13], developed an extension to the Izhikevich neuron model that also models the supply of energy to the neuron by a glial cell, with one glial cell per neuron. They found that, when instantiating a number of these in a network, similar spiking activity to that of a phenomenon they identified in the human brain was observed.…”

“…Other work has been previously performed to analyze the impact of energy constraints on artificial neural networks (ANNs). These include analyzing the impact of adding simple energy constraints to artificial spiking neurons [3], creating a biorealistic neuron-glial model for energy constraints [13], analyzing the impact of adding artificial astrocytes to a network [16], and also training a network to operate within arbitrarily defined energy constraints [19]. No work, to our knowledge, has been done to analyze the impact of energy constraints on a network trained to perform a computational task.…”

Control energy assessment of spatial interactions among macro‐scale brain networks

“…Thus, it is more likely to achieve a better understanding of the relationship between energy consumption and functional network interactions. In the literature, a variety of approaches have been proposed to analyze the energy characteristics of functional networks, such as computation of the maximum entropy model (Ashourvan, Gu, Mattar, Vettela, & Bassetta, 2017;Ezaki, Watanabe, Ohzeki, & Masuda, 2017;Gu et al, 2018;Kang, Pae, & Park, 2017;Watanabe et al, 2014) and detection of the cerebral energy metabolism (Liang, Zou, He, & Yang, 2013;Lord, Expert, Huckins, & Turkheimer, 2013;Noack, Manjesh, Ruszinko, Siegelmann, & Kozma, 2017). Specifically, the maximum entropy model defines an energy function for the system with a maximum entropy probability distribution.…”

“…Correspondence between functional and structural connections has not been well examined from the viewpoint of the energy consumption. (3) Most of the previous studies used resting state fMRI to analyze the energy consumption of brain networks (Ashourvan et al, 2017;Ezaki et al, 2017;Gu et al, 2018;Kang et al, 2017;Krzeminski et al, 2020;Noack et al, 2017;Tomasi et al, 2017;Watanabe et al, 2014). Depicting what the energy consumption characteristics of brain networks are also needs to be addressed in task-related studies.…”