Cortical network models of impulse firing in the resting and active states predict cortical energetics

How Energy Supports Our Brain to Yield Consciousness: Insights From Neuroimaging Based on the Neuroenergetics Hypothesis

Consciousness is considered a result of specific neuronal processes and mechanisms in the brain. Various suggested neuronal mechanisms, including the information integration theory (IIT), global neuronal workspace theory (GNWS), and neuronal construction of time and space as in the context of the temporospatial theory of consciousness (TTC), have been laid forth. However, despite their focus on different neuronal mechanisms, these theories neglect the energetic-metabolic basis of the neuronal mechanisms that are supposed to yield consciousness. Based on the findings of physiology-induced (sleep), pharmacology-induced (general anesthesia), and pathology-induced [vegetative state/unresponsive wakeful syndrome (VS/UWS)] loss of consciousness in both human subjects and animals, we, in this study, suggest that the energetic-metabolic processes focusing on ATP, glucose, and γ-aminobutyrate/glutamate are indispensable for functional connectivity (FC) of normal brain networks that renders consciousness possible. Therefore, we describe the energetic-metabolic predispositions of consciousness (EPC) that complement the current theories focused on the neural correlates of consciousness (NCC).

Energetic Constraints Produce Self-sustained Oscillatory Dynamics in Neuronal Networks

Overview: We model energy constraints in a network of spiking neurons, while exploring general questions of resource limitation on network function abstractly.

Background: Metabolic states like dietary ketosis or hypoglycemia have a large impact on brain function and disease outcomes. Glia provide metabolic support for neurons, among other functions. Yet, in computational models of glia-neuron cooperation, there have been no previous attempts to explore the effects of direct realistic energy costs on network activity in spiking neurons. Currently, biologically realistic spiking neural networks assume that membrane potential is the main driving factor for neural spiking, and do not take into consideration energetic costs.

Methods: We define local energy pools to constrain a neuron model, termed Spiking Neuron Energy Pool (SNEP), which explicitly incorporates energy limitations. Each neuron requires energy to spike, and resources in the pool regenerate over time. Our simulation displays an easy-to-use GUI, which can be run locally in a web browser, and is freely available.

Results: Energy dependence drastically changes behavior of these neural networks, causing emergent oscillations similar to those in networks of biological neurons. We analyze the system via Lotka-Volterra equations, producing several observations: (1) energy can drive self-sustained oscillations, (2) the energetic cost of spiking modulates the degree and type of oscillations, (3) harmonics emerge with frequencies determined by energy parameters, and (4) varying energetic costs have non-linear effects on energy consumption and firing rates.

Conclusions: Models of neuron function which attempt biological realism may benefit from including energy constraints. Further, we assert that observed oscillatory effects of energy limitations exist in networks of many kinds, and that these findings generalize to abstract graphs and technological applications.

Behavior, neuropsychology and fMRI

Cognitive neuroscientists in the late 20th century began the task of identifying the part(s) of the brain concerned with normal behavior as manifest in the psychological capacities as affective powers, reasoning, behaving purposively and the pursuit of goals, following introduction of the 'functional magnetic resonance imaging' (fMRI) method for identifying brain activity. For this research program to be successful two questions require satisfactory answers. First, as the fMRI method can currently only be used on stationary subjects, to what extent can neuropsychological tests applicable to such stationary subjects be correlated with normal behavior. Second, to what extent can correlations between the various neuropsychological tests on the one hand, and sites of brain activity determined with fMRI on the other, be regarded as established. The extent to which these questions have yet received satisfactory answers is reviewed, and suggestions made both for improving correlations of neuropsychological tests with behavior as well as with the results of fMRI-based observations.

On the origins of the 'global signal' determined by functional magnetic resonance imaging in the resting state

OBJECTIVE

Functional magnetic resonance imaging blood oxygen level dependent (BOLD) determinations of correlations between 'resting-state' neuronal activity in different regions of cortex have generated much interest. Determination of these correlations requires regressing out signals that are correlated in all parts of the cortex and are taken to be artefactual, such as those due to movement, respiration and cardiovascular activity. However when these are removed there still remains a 'global signal' (GS), which is taken to be of unknown physiological origin, and is regressed out by some researchers but not by others.

APPROACH

We have investigated the origin of this GS using cortical models consisting of coupled networks of modules representing regions of interest.

MAIN RESULTS

We show that the GS has an amplitude that is linearly related to the average correlation between the modules/voxels in the network over a large range of such correlations. The GS arises as a consequence of feedback between the modules/voxels leading to correlations in their BOLD signals. Given the relationship between the GS and the average correlations it might be anticipated that regressing out the GS during preprocessing will significantly modify the correlations subsequently determined. This is shown to be the case when comparing the connections of individual modules with that predicted by the correlations.

SIGNIFICANCE

The present model shows that such correlations can arise as a consequence of the intermodular feedback connectivity without recourse to imposing a GS independent of the connectivity. Our model indicates that the GS reflects the extent of feedback pathways provided by the intermodular/inter-regional connections and hence the average correlation between modules or regions of cortex. However the model has not been used to elucidate the possible contributions of a GS independent of the connectivity, which might indeed contribute to the GS of the cortex.

Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses

Measurements of blood oxygenation level dependent (BOLD) signals have produced some surprising observations. One is that their amplitude is proportional to the entire activity in a region of interest and not just the fluctuations in this activity. Another is that during sleep and anesthesia the average BOLD correlations between regions of interest decline as the activity declines. Mechanistic explanations of these phenomena are described here using a cortical network model consisting of modules with excitatory and inhibitory neurons, taken as regions of cortical interest, each receiving excitatory inputs from outside the network, taken as subcortical driving inputs in addition to extrinsic (intermodular) connections, such as provided by associational fibers. The model shows that the standard deviation of the firing rate is proportional to the mean frequency of the firing when the extrinsic connections are decreased, so that the mean BOLD signal is proportional to both as is observed experimentally. The model also shows that if these extrinsic connections are decreased or the frequency of firing reaching the network from the subcortical driving inputs is decreased, or both decline, there is a decrease in the mean firing rate in the modules accompanied by decreases in the mean BOLD correlations between the modules, consistent with the observed changes during NREM sleep and under anesthesia. Finally, the model explains why a transient increase in the BOLD signal in a cortical area, due to a transient subcortical input, gives rises to responses throughout the cortex as observed, with these responses mediated by the extrinsic (intermodular) connections.