Average firing rate rather than temporal pattern determines metabolic cost of activity in thalamocortical relay neurons

Neural circuit-selective, multiplexed pharmacological targeting of prefrontal cortex-projecting locus coeruleus neurons drives antinociception

Selective manipulation of neural circuits using optogenetics and chemogenetics holds great translational potential but requires genetic access to neurons. Here, we demonstrate a general framework for identifying genetic tool-independent, pharmacological strategies for neural circuit-selective modulation. We developed an economically accessible calcium imaging-based approach for large-scale pharmacological scans of endogenous receptor-mediated neural activity. As a testbed for this approach, we used the mouse locus coeruleus due to the combination of its widespread, modular efferent neural circuitry and its wide variety of endogenously expressed GPCRs. Using machine learning-based action potential deconvolution and retrograde tracing, we identified an agonist cocktail that selectively inhibits medial prefrontal cortex-projecting locus coeruleus neurons. In vivo, this cocktail produces synergistic antinociception, consistent with selective pharmacological blunting of this neural circuit. This framework has broad utility for selective targeting of other neural circuits under different physiological and pathological states, facilitating non-genetic translational applications arising from cell type-selective discoveries.

Metabolic response of auditory brainstem neurons to their broad physiological activity range

Neurons exhibit a high energetic need, and the question arises as how they metabolically adapt to changing activity states. This is relevant for interpreting functional neuroimaging in different brain areas. Particularly, neurons with a broad firing range might exhibit metabolic adaptations. Therefore, we studied MNTB (medial nucleus of the trapezoid body) principal neurons, which generate action potentials (APs) at frequencies up to several hundred hertz. We performed the experiments in acute brainstem slices of the Mongolian gerbil (Meriones unguiculatus) at 22.5-24.5°C. Upon electrical stimulation of afferent MNTB fibres with 400 stimuli at varying frequencies, we monitored autofluorescence levels of NAD(P)H and FAD and determined the extremum amplitudes of their biphasic response. Additionally, we compared these data with alterations in O2 concentrations measured with an electrochemical sensor. These O2 changes are prominent since MNTB neurons rely on oxidative phosphorylation as shown by our pharmacological experiments. We calculated the O2 consumption rate as change in O2 concentration divided by stimulus durations, because these periods varied inversely with stimulus frequency as a result of the constant number of 400 stimuli applied. The O2 consumption rate increased with stimulation frequency up to a constant value at 600 Hz; that is, energy demand depends on temporal characteristics of activity despite the same number of stimuli. The rates showed no correlation with peak amplitudes of NAD(P)H or FAD, whilst the values of the two molecules were linearly correlated. This points at the complexity of analysing autofluorescence imaging for quantitative metabolic studies, because these values report only relative net changes of many superimposed oxidative and reductive processes. Monitoring O2 concentration rates is, thus, an important tool to improve the interpretation of NAD(P)H/FAD autofluorescence data, as they do not under all conditions and in all systems appropriately reflect the metabolic activity or energy demand.

Divisive normalization is an efficient code for multivariate Pareto-distributed environments

Divisive normalization is a canonical computation in the brain, observed across neural systems, that is often considered to be an implementation of the efficient coding principle. We provide a theoretical result that makes the conditions under which divisive normalization is an efficient code analytically precise: We show that, in a low-noise regime, encoding an n-dimensional stimulus via divisive normalization is efficient if and only if its prevalence in the environment is described by a multivariate Pareto distribution. We generalize this multivariate analog of histogram equalization to allow for arbitrary metabolic costs of the representation, and show how different assumptions on costs are associated with different shapes of the distributions that divisive normalization efficiently encodes. Our result suggests that divisive normalization may have evolved to efficiently represent stimuli with Pareto distributions. We demonstrate that this efficiently encoded distribution is consistent with stylized features of naturalistic stimulus distributions such as their characteristic conditional variance dependence, and we provide empirical evidence suggesting that it may capture the statistics of filter responses to naturalistic images. Our theoretical finding also yields empirically testable predictions across sensory domains on how the divisive normalization parameters should be tuned to features of the input distribution.

High Iron and Iron Household Protein Contents in Perineuronal Net-Ensheathed Neurons Ensure Energy Metabolism with Safe Iron Handling

A subpopulation of neurons is less vulnerable against iron-induced oxidative stress and neurodegeneration. A key feature of these neurons is a special extracellular matrix composition that forms a perineuronal net (PN). The PN has a high affinity to iron, which suggests an adapted iron sequestration and metabolism of the ensheathed neurons. Highly active, fast-firing neurons-which are often ensheathed by a PN-have a particular high metabolic demand, and therefore may have a higher need in iron. We hypothesize that PN-ensheathed neurons have a higher intracellular iron concentration and increased levels of iron proteins. Thus, analyses of cellular and regional iron and the iron proteins transferrin (Tf), Tf receptor 1 (TfR), ferritin H/L (FtH/FtL), metal transport protein 1 (MTP1 aka ferroportin), and divalent metal transporter 1 (DMT1) were performed on Wistar rats in the parietal cortex (PC), subiculum (SUB), red nucleus (RN), and substantia nigra (SNpr/SNpc). Neurons with a PN (PN⁺) have higher iron concentrations than neurons without a PN: PC 0.69 mM vs. 0.51 mM, SUB 0.84 mM vs. 0.69 mM, SN 0.71 mM vs. 0.63 mM (SNpr)/0.45 mM (SNpc). Intracellular Tf, TfR and MTP1 contents of PN⁺ neurons were consistently increased. The iron concentration of the PN itself is not increased. We also determined the percentage of PN⁺ neurons: PC 4%, SUB 5%, SNpr 45%, RN 86%. We conclude that PN⁺ neurons constitute a subpopulation of resilient pacemaker neurons characterized by a bustling iron metabolism and outstanding iron handling capabilities. These properties could contribute to the low vulnerability of PN⁺ neurons against iron-induced oxidative stress and degeneration.

Neural optimization: Understanding trade-offs with Pareto theory

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function, as well as additional factors such as robustness to environmental changes and energetic limitations. Oftentimes these objectives compete, and quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems from biophysically detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.

Extending the integrate-and-fire model to account for metabolic dependencies

It is widely accepted that the brain, like any other physical system, is subjected to physical constraints that restrict its operation. The brain's metabolic demands are particularly critical for proper neuronal function, but the impact of these constraints continues to remain poorly understood. Detailed single-neuron models are recently integrating metabolic constraints, but these models' computational resources make it challenging to explore the dynamics of extended neural networks, which are governed by such constraints. Thus, there is a need for a simplified neuron model that incorporates metabolic activity and allows us to explore the dynamics of neural networks. This work introduces an energy-dependent leaky integrate-and-fire (EDLIF) neuronal model extension to account for the effects of metabolic constraints on the single-neuron behavior. This simple, energy-dependent model could describe the relationship between the average firing rate and the Adenosine triphosphate (ATP) cost as well as replicate a neuron's behavior under a clinical setting such as amyotrophic lateral sclerosis (ALS). Additionally, EDLIF model showed better performance in predicting real spike trains - in the sense of spike coincidence measure - than the classical leaky integrate-and-fire (LIF) model. The simplicity of the energy-dependent model presented here makes it computationally efficient and, thus, suitable for studying the dynamics of large neural networks.

Frequency-Dependent Energy Demand of Dendritic Responses to Deep Brain Stimulation in Thalamic Neurons: A Model-Based Study

Thalamic deep brain stimulation (DBS) generates excitatory postsynaptic currents and action potentials (APs) by triggering large numbers of synaptic inputs to local cells, which also activates axonal spikes to antidromically invade the soma and dendrites. To maintain signaling, the evoked dendritic responses require metabolic energy to restore ion gradients in each dendrite. The objective of this study is to estimate the energy demand associated with dendritic responses to thalamic DBS. We use a morphologically realistic computational model to simulate dendritic activity in thalamocortical (TC) relay neurons with axonal intracellular stimulation or DBS-like extracellular stimulation. We determine the metabolic cost by calculating the number of adenosine triphosphate (ATP) expended to pump Na⁺ and Ca²⁺ ions out of each dendrite. The ATP demand of dendritic activity exhibits frequency dependence, which is determined by the number of spikes in the dendrites. Each backpropagating AP from the soma activates a spike in the dendrites, and the dendritic firing is dominated by antidromic activation of the soma. High stimulus frequencies decrease dendritic ATP cost by reducing the fidelity of antidromic activation. Synaptic inputs and stimulus-induced polarization govern the ATP cost of dendritic responses by facilitating/suppressing antidromic activation, which also influences the ATP cost by depolarizing/hyperpolarizing each dendrite. These findings are important for understanding the synaptic signaling energy in TC relay neurons and metabolism-dependent functional imaging data of thalamic DBS.

Mice Preferentially Use Increases in Cerebral Cortex Spiking to Detect Changes in Visual Stimuli

Whenever the retinal image changes, some neurons in visual cortex increase their rate of firing whereas others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice of both sexes to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements in spiking were generated in the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity has costs. This work clarifies neuronal decoding strategies used by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.SIGNIFICANCE STATEMENT Visual responses in the primary visual cortex (V1) are diverse, in that neurons can be either excited or inhibited by the onset of a visual stimulus. We selectively potentiated or suppressed V1 spiking in mice while they performed contrast change detection tasks. In other experiments, excitation or inhibition was delivered to V1 independent of visual stimuli. Mice readily detected increases in V1 spiking while equivalent reductions in V1 spiking suppressed the probability of detection, even when increases and decreases in V1 spiking were generated in the same neuronal populations. Our data raise the striking possibility that only increments in spiking are used to render information to structures downstream of V1.

Metabolic tuning of inhibition regulates hippocampal neurogenesis in the adult brain

Hippocampus-engaged behaviors stimulate neurogenesis in the adult dentate gyrus by largely unknown means. To explore the underlying mechanisms, we used tetrode recording to analyze neuronal activity in the dentate gyrus of freely moving adult mice during hippocampus-engaged contextual exploration. We found that exploration induced an overall sustained increase in inhibitory neuron activity that was concomitant with decreased excitatory neuron activity. A mathematical model based on energy homeostasis in the dentate gyrus showed that enhanced inhibition and decreased excitation resulted in a similar increase in neurogenesis to that observed experimentally. To mechanistically investigate this sustained inhibitory regulation, we performed metabolomic and lipidomic profiling of the hippocampus during exploration. We found sustainably increased signaling of sphingosine-1-phosphate, a bioactive metabolite, during exploration. Furthermore, we found that sphingosine-1-phosphate signaling through its receptor 2 increased interneuron activity and thus mediated exploration-induced neurogenesis. Taken together, our findings point to a behavior-metabolism circuit pathway through which experience regulates adult hippocampal neurogenesis.

Metabolic Cost of Dendritic Ca2+ Action Potentials in Layer 5 Pyramidal Neurons

Pyramidal neurons consume most signaling-related energy to generate action potentials (APs) and perform synaptic integration. Dendritic Ca²⁺ spike is an important integration mechanism for coupling inputs from different cortical layers. Our objective was to quantify the metabolic energy associated with the generation of Ca²⁺ APs in the dendrites. We used morphology-based computational models to simulate the dendritic Ca²⁺ spikes in layer 5 pyramidal neurons. We calculated the energy cost by converting Ca²⁺ influx into the number of ATP required to restore and maintain the homeostasis of intracellular Ca²⁺ concentrations. We quantified the effects of synaptic inputs, dendritic voltage, back-propagating Na⁺ spikes, and Ca²⁺ inactivation on Ca²⁺ spike cost. We showed that much more ATP molecules were required for reversing Ca²⁺ influx in the dendrites than for Na⁺ ion pumping in the soma during a Ca²⁺ AP. Increasing synaptic input increased the rate of dendritic depolarization and underlying Ca²⁺ influx, resulting in higher ATP consumption. Depolarizing dendritic voltage resulted in the inactivation of Ca²⁺ channels and reduced the ATP cost, while dendritic hyperpolarization increased the spike cost by de-inactivating Ca²⁺ channels. A back-propagating Na⁺ AP initiated in the soma increased Ca²⁺ spike cost in the apical dendrite when it coincided with a synaptic input within a time window of several milliseconds. Increasing Ca²⁺ inactivation rate reduced Ca²⁺ spike cost, while slowing Ca²⁺ inactivation increased the spike cost. The results revealed that the energy demand of a Ca²⁺ AP was dynamically dependent on the state of dendritic activity. These findings were important for predicting the energy budget for signaling in pyramidal cells, interpreting functional imaging data, and designing energy-efficient neuromorphic devices.