Modeling the relationship between neuronal activity and the BOLD signal: contributions from astrocyte calcium dynamics

The Dose-Dependent Effects of Fluorocitrate on the Metabolism and Activity of Astrocytes and Neurons

BACKGROUND

Fluorocitrate (FC) ranging from 5 μM to 5 mM is often used as a specific metabolic inhibitor of the astrocytes to study astrocytic functions. Whether FC at such concentrations may affect neuronal metabolism and function in vivo remains unclear.

METHODS

We examined the effects of FC on the ATP levels and Ca2+ activity of the astrocytes and neurons in the motor cortices of living mice using two-photon microscopy.

RESULTS

We found that 25 μM and 250 μM of FC decreased the intracellular ATP levels and Ca2+ activity in the astrocytes in the motor cortex. Equally, 250 μM of FC, but not 25 μM of FC, reduced the intracellular ATP levels in the dendritic processes of the layer 5 pyramidal neurons. However, 25 μM of FC increased the neuronal Ca2+ activity, whereas ≥250 μM of FC decreased it. To test whether the differential effects of FC on neuronal Ca2+ activity reflect the direct effect of FC on the neurons or its indirect effect on the astrocytes, we used the CNO-hM3Dq chemogenetic approach to block astrocytic Ca2+ activity and examined the effect of FC. In the absence of astrocytic Ca2+ activity, 25 μM of FC still increased and ≥250 μM of FC reduced the dendritic Ca2+ activity of the neurons, respectively, suggesting a direct effect of 250 μM of FC on inhibiting neuronal Ca2+ activity. Further, 250 μM, but not 25 μM, of FC increased the size of the dendritic spines over 2 h.

CONCLUSIONS

Our findings suggest that FC at high concentrations (≥250 μM) is not a specific inhibitor of astrocytic functions, as it directly affects neuronal metabolism and synaptic plasticity in vivo.

Characterizing astrocyte-mediated neurovascular coupling by combining optogenetics and biophysical modeling

Vasoactive signaling from astrocytes is an important contributor to the neurovascular coupling (NVC), which aims at providing energy to neurons during brain activation by increasing blood perfusion in the surrounding vasculature. Pharmacological manipulations have been previously combined with experimental techniques (e.g., transgenic mice, uncaging, and multiphoton microscopy) and stimulation paradigms to isolate in vivo individual pathways of the astrocyte-mediated NVC. Unfortunately, these pathways are highly nonlinear and non-additive. To separate these pathways in a unified framework, we combine a comprehensive biophysical model of vasoactive signaling from astrocytes with a unique optogenetic stimulation method that selectively induces astrocytic Ca2+ signaling in a large population of astrocytes. We also use a sensitivity analysis and an optimization technique to estimate key model parameters. Optogenetically-induced Ca2+ signals in astrocytes cause a cerebral blood flow (CBF) response with two major components. Component-1 was rapid and smaller (ΔCBF∼13%, 18 seconds), while component-2 was slowest and highest (ΔCBF ∼18%, 45 seconds). The proposed biophysical model was adequate in reproducing component-2, which was validated with a pharmacological manipulation. Model's predictions were not in contradiction with previous studies. Finally, we discussed scenarios accounting for the existence of component-1, which once validated might be included in our model.

Dialogue mechanisms between astrocytic and neuronal networks: A whole-brain modelling approach

Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles of astrocytes while primarily focusing on neurons. Addressing this oversight, we introduce a biophysical neural mass network model, designed to capture the dynamic interplay between astrocytes and neurons via glutamatergic and GABAergic transmission pathways. This network model proposes that neural dynamics are constrained by a two-layered structural network interconnecting both astrocytic and neuronal populations, allowing us to investigate astrocytes' modulatory influences on whole-brain activity and emerging functional connectivity patterns. By developing a simulation methodology, informed by bifurcation and multilayer network theories, we demonstrate that the dialogue between astrocytic and neuronal networks manifests over fast-slow fluctuation mechanisms as well as through phase-amplitude connectivity processes. The findings from our research represent a significant leap forward in the modeling of glial-neuronal collaboration, promising deeper insights into their collaborative roles across health and disease states.

Modeling of Blood Flow Dynamics in Rat Somatosensory Cortex

Background: The cerebral microvasculature forms a dense network of interconnected blood vessels where flow is modulated partly by astrocytes. Increased neuronal activity stimulates astrocytes to release vasoactive substances at the endfeet, altering the diameters of connected vessels. Methods: Our study simulated the coupling between blood flow variations and vessel diameter changes driven by astrocytic activity in the rat somatosensory cortex. We developed a framework with three key components: coupling between the vasculature and synthesized astrocytic morphologies, a fluid dynamics model to compute flow in each vascular segment, and a stochastic process replicating the effect of astrocytic endfeet on vessel radii. Results: The model was validated against experimental flow values from the literature across cortical depths. We found that local vasodilation from astrocyte activity increased blood flow, especially in capillaries, exhibiting a layer-specific response in deeper cortical layers. Additionally, the highest blood flow variability occurred in capillaries, emphasizing their role in cerebral perfusion regulation. We discovered that astrocytic activity impacted blood flow dynamics in a localized, clustered manner, with most vascular segments influenced by two to three neighboring endfeet. Conclusions: These insights enhance our understanding of neurovascular coupling and guide future research on blood flow-related diseases.

Astrocyte-mediated neuronal irregularities and dynamics: the complexity of the tripartite synapse

Despite significant advancements in recent decades, gaining a comprehensive understanding of brain computations remains a significant challenge in neuroscience. Using computational models is crucial for unraveling this complex phenomenon and is equally indispensable for studying neurological disorders. This endeavor has created many neuronal models that capture brain dynamics at various scales and complexities. However, most existing models do not account for the potential influence of glial cells, particularly astrocytes, on neuronal physiology. This gap persists even with the emerging evidence indicating their critical role in regulating neural network activity, plasticity, and even neurological pathologies. To address this gap, some works proposed models that include neuron-glia interactions. Also, while some literature focuses on sophisticated models of neuron-glia interactions that mimic the complexity of physiological phenomena, there are also existing works that propose simplified models of neural-glial ensembles. Building upon these efforts, we aimed to contribute further to the field by proposing a simplified tripartite synapse model that encompasses the presynaptic neuron, postsynaptic neuron, and astrocyte. We defined the tripartite synapse model based on the Adaptive Exponential Integrate-and-Fire neuron model and a simplified scheme of the astrocyte model previously proposed by Postnov. Through our simulations, we demonstrated how astrocytes can influence neuronal firing behavior by sequentially activating and deactivating different pathways within the tripartite synapse. This modulation by astrocytes can shape neuronal behavior and introduce irregularities in the firing patterns of both presynaptic and postsynaptic neurons through the introduction of new pathways and configurations of relevant parameters.

A multi-scale study of thalamic state-dependent responsiveness

The thalamus is the brain's central relay station, orchestrating sensory processing and cognitive functions. However, how thalamic function depends on internal and external states, is not well understood. A comprehensive understanding would necessitate the integration of single cell dynamics with their collective behavior at population level. For this we propose a biologically realistic mean-field model of the thalamus, describing thalamocortical relay neurons (TC) and thalamic reticular neurons (RE). We perform a multi-scale study of thalamic responsiveness and its dependence on cell and brain states. Building upon existing single-cell experiments we show that: (1) Awake and sleep-like states can be defined via the absence/presence of the neuromodulator acetylcholine (ACh), which indirectly controls bursting in TC and RE. (2) Thalamic response to sensory stimuli is linear in awake state and becomes nonlinear in sleep state, while cortical input generates nonlinear response in both awake and sleep state. (3) Stimulus response is controlled by cortical input, which suppresses responsiveness in awake state while it 'wakes-up' the thalamus in sleep state promoting a linear response. (4) Synaptic noise induces a global linear responsiveness, diminishing the difference in response between thalamic states. Finally, the model replicates spindle oscillations within a sleep-like state, exhibiting a qualitative change in activity and responsiveness. The development of this thalamic mean-field model provides a new tool for incorporating detailed thalamic dynamics in large scale brain simulations.

A Model-Driven Meta-Analysis Supports the Emerging Consensus View that Inhibitory Neurons Dominate BOLD-fMRI Responses

Functional magnetic resonance imaging (fMRI) is a pivotal tool for mapping neuronal activity in the brain. Traditionally, the observed hemodynamic changes are assumed to reflect the activity of the most common neuronal type: excitatory neurons. In contrast, recent experiments, using optogenetic techniques, suggest that the fMRI-signal instead reflects the activity of inhibitory interneurons. However, these data paint a complex picture, with numerous regulatory interactions, and where the different experiments display many qualitative differences. It is therefore not trivial how to quantify the relative contributions of the different cell types and to combine all observations into a unified theory. To address this, we present a new model-driven meta-analysis, which provides a unified and quantitative explanation for all data. This model-driven analysis allows for quantification of the relative contribution of different cell types: the contribution to the BOLD-signal from the excitatory cells is <20 % and 50-80 % comes from the interneurons. Our analysis also provides a mechanistic explanation for the observed experiment-to-experiment differences, e.g. a biphasic vascular response dependent on different stimulation intensities and an emerging secondary post-stimulation peak during longer stimulations. In summary, our study provides a new, emerging consensus-view supporting the larger role of interneurons in fMRI.

Multiscale modeling of neuronal dynamics in hippocampus CA1

The development of biologically realistic models of brain microcircuits and regions constitutes currently a very relevant topic in computational neuroscience. One of the main challenges of such models is the passage between different scales, going from the microscale (cellular) to the meso (microcircuit) and macroscale (region or whole-brain level), while keeping at the same time a constraint on the demand of computational resources. In this paper we introduce a multiscale modeling framework for the hippocampal CA1, a region of the brain that plays a key role in functions such as learning, memory consolidation and navigation. Our modeling framework goes from the single cell level to the macroscale and makes use of a novel mean-field model of CA1, introduced in this paper, to bridge the gap between the micro and macro scales. We test and validate the model by analyzing the response of the system to the main brain rhythms observed in the hippocampus and comparing our results with the ones of the corresponding spiking network model of CA1. Then, we analyze the implementation of synaptic plasticity within our framework, a key aspect to study the role of hippocampus in learning and memory consolidation, and we demonstrate the capability of our framework to incorporate the variations at synaptic level. Finally, we present an example of the implementation of our model to study a stimulus propagation at the macro-scale level, and we show that the results of our framework can capture the dynamics obtained in the corresponding spiking network model of the whole CA1 area.

EEG-fMRI in awake rat and whole-brain simulations show decreased brain responsiveness to sensory stimulations during absence seizures

In patients suffering absence epilepsy, recurring seizures can significantly decrease their quality of life and lead to yet untreatable comorbidities. Absence seizures are characterized by spike-and-wave discharges on the electroencephalogram associated with a transient alteration of consciousness. However, it is still unknown how the brain responds to external stimuli during and outside of seizures. This study aimed to investigate responsiveness to visual and somatosensory stimulation in Genetic Absence Epilepsy Rats from Strasbourg (GAERS), a well-established rat model for absence epilepsy. Animals were imaged under non-curarized awake state using a quiet, zero echo time, functional magnetic resonance imaging (fMRI) sequence. Sensory stimulations were applied during interictal and ictal periods. Whole-brain hemodynamic responses were compared between these two states. Additionally, a mean-field simulation model was used to explain the changes of neural responsiveness to visual stimulation between states. During a seizure, whole-brain responses to both sensory stimulations were suppressed and spatially hindered. In the cortex, hemodynamic responses were negatively polarized during seizures, despite the application of a stimulus. The mean-field simulation revealed restricted propagation of activity due to stimulation and agreed well with fMRI findings. Results suggest that sensory processing is hindered or even suppressed by the occurrence of an absence seizure, potentially contributing to decreased responsiveness during this absence epileptic process.

A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models

Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semianalytical approach, has previously been applied to different types of single-neuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.

Computational modeling of neuron-glia signaling interactions to unravel cellular and neural circuit functioning

Glial cells have been shown to be vital for various brain functions, including homeostasis, information processing, and cognition. Over the past 30 years, various signaling interactions between neuronal and glial cells have been shown to underlie these functions. This review summarizes the interactions, particularly between neurons and astrocytes, which are types of glial cells. Some of the interactions remain controversial in part due to the nature of experimental methods and preparations used. Based on the accumulated data, computational models of the neuron-astrocyte interactions have been developed to explain the complex functions of astrocytes in neural circuits and to test conflicting hypotheses. This review presents the most significant recent models, modeling methods and simulation tools for neuron-astrocyte interactions. In the future, we will especially need more experimental research on awake animals in vivo and new computational models of neuron-glia interactions to advance our understanding of cellular dynamics and the functioning of neural circuits in different brain regions.