A New Computational Model for Neuro-Glio-Vascular Coupling: Astrocyte Activation Can Explain Cerebral Blood Flow Nonlinear Response to Interictal Events

Solenna Blanchard; Sandrine Saillet; Anton Ivanov; Pascal Benquet; Christian-George Bénar; Mélanie Pélégrini-Issac; Habib Benali; Fabrice Wendling

doi:10.1371/journal.pone.0147292

Abstract

Developing a clear understanding of the relationship between cerebral blood flow (CBF) response and neuronal activity is of significant importance because CBF increase is essential to the health of neurons, for instance through oxygen supply. This relationship can be investigated by analyzing multimodal (fMRI, PET, laser Doppler…) recordings. However, the important number of intermediate (non-observable) variables involved in the underlying neurovascular coupling makes the discovery of mechanisms all the more difficult from the sole multimodal data. We present a new computational model developed at the population scale (voxel) with physiologically relevant but simple equations to facilitate the interpretation of regional multimodal recordings. This model links neuronal activity to regional CBF dynamics through neuro-glio-vascular coupling. This coupling involves a population of glial cells called astrocytes via their role in neurotransmitter (glutamate and GABA) recycling and their impact on neighboring vessels. In epilepsy, neuronal networks generate epileptiform discharges, leading to variations in astrocytic and CBF dynamics. In this study, we took advantage of these large variations in neuronal activity magnitude to test the capacity of our model to reproduce experimental data. We compared simulations from our model with isolated epileptiform events, which were obtained in vivo by simultaneous local field potential and laser Doppler recordings in rats after local bicuculline injection. We showed a predominant neuronal contribution for low level discharges and a significant astrocytic contribution for higher level discharges. Besides, neuronal contribution to CBF was linear while astrocytic contribution was nonlinear. Results thus indicate that the relationship between neuronal activity and CBF magnitudes can be nonlinear for isolated events and that this nonlinearity is due to astrocytic activity, highlighting the importance of astrocytes in the interpretation of regional recordings.

Citation: Blanchard S, Saillet S, Ivanov A, Benquet P, Bénar C-G, Pélégrini-Issac M, et al. (2016) A New Computational Model for Neuro-Glio-Vascular Coupling: Astrocyte Activation Can Explain Cerebral Blood Flow Nonlinear Response to Interictal Events. PLoS ONE 11(2): e0147292. https://doi.org/10.1371/journal.pone.0147292

Editor: Sven G. Meuth, University of Muenster, GERMANY

Received: July 23, 2015; Accepted: January 1, 2016; Published: February 5, 2016

Copyright: © 2016 Blanchard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was funded by a grant from the Agence Nationale de la Recherche ANR 2010 BLAN 030901 named MULTIMODEL project: http://www.agence-nationale-recherche.fr/?Project=ANR-10-BLAN-0309. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The dynamics of cerebral blood flow (CBF) changes are an essential element of neuronal environment, as they reflect nutriment supplies such as oxygen and glucose. Their link to neuronal activity, usually called neurovascular coupling, can now be investigated by multimodal recordings [1] such as simultaneous Local Field Potential (LFP)-laser Doppler (LD) recordings [2]. However, due to the multiple signaling pathways underlying this coupling, such multimodal data remain difficult to interpret.

Computational models can provide a key tool to this interpretation. For instance, the well-known model [3] representing CBF dynamics has been widely studied and used, including for data interpretation [4, 5]. In this model, CBF increase is driven by one neuronal input. Later, a number of studies have shown that astrocytes [6] also contribute to CBF increase, in parallel to a neuronal contribution [7, 8]. Actually, astrocytes have been shown to have a significant impact on either CBF dynamics [9] or metabolic regulation [10], although this impact cannot be measured easily. Computational models have the capacity to reveal different (patho-)physiological mechanisms [11] by comprising non-easily observable variables. Some computational studies have included a link to physiological literature, for instance by modeling neuronal-metabolic coupling through glucose and/or oxygen activities [12–16]. Nonetheless, these models do not account for astrocytes and lack essential details needed for interpreting patho-physiological mechanisms such as neurotransmitter exchanges. Actually, a comprehensive literature review (S1 File) showed that the cycles of glutamate and GABA (main neurotransmitters of the central nervous system) lie at the origin of astrocytic contribution to CBF changes. Some computational models describe pathways including glutamate and GABA transport and/or astrocytic activity [17–24], however these models deal with exchanges that occur at the cellular level of the tripartite synapse. Our goal was thus to conceive a new neuro-glio-vascular (NGV) model, representing the relationship between local neuronal activity (as measured by LFP) and CBF changes (as measured by LD) and introducing the contribution of astrocytes to CBF changes. Bimodal LFP-LD recordings (Fig 1) involve interactions between cell populations at the voxel scale (about 3 mm³). In the continuity of the pioneer work [25–27] expanded in [28–30] and introducing astrocytes in their metabolic role, we used a compartmental model (Fig 1) with an extracellular space to account for neurotransmitter exchanges. Since glutamate and GABA are primarily released by pyramidal cells and interneurons respectively, we distinguished between these two types of neuronal populations in the model.

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Fig 1. Main neuro-glio-vascular interactions in a voxel and their link to bimodal LFP-LD recordings.

Arrows represent the interactions between the five compartments of the model: the compartments of pyramidal cells and interneurons provide a representation of the neuronal activity as measured by local field potential (LFP); the astrocytic compartment represents the key role of astrocytes in neurotransmitter (glutamate and GABA) cycling and cerebral blood flow (CBF) dynamics; the vessel compartment involves the CBF dynamics as measured by laser Doppler (LD); the extracellular space represents neurotransmitter exchanges between the other compartments.

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Comparing a computational model and experimental data allows one to test the limitations of the model and its ability to reproduce physiological phenomena (choice of mathematical descriptions and incorporated variables, resulting complexity, see Materials and Methods). Simulations from our model were compared with isolated events extracted from simultaneous (LFP-LD) recordings (Materials and Methods) acquired in the context of epilepsy. More precisely, isolated events called epileptiform discharges occur in LFP signals after bicuculline injection in the cortex. These discharges elicit CBF events visible on LD data, the variations of which are sufficiently large to enable the study of the relationship between the magnitude of the discharges and that of the resulting CBF events. We obtained a good agreement between the model and the data according to the shape of the responses, which indicate that the model is able to reproduce epileptic phenomena. Besides, the sets of parameters found to obtain close simulated and experimental CBF magnitudes suggest that the relationship between neuronal activity and CBF response is nonlinear for epileptiform isolated discharges; and that this nonlinearity is due to the contribution of the astrocytes to CBF magnitude.

Results

Adaptation of a neural mass model to an animal acute model of epilepsy

The first part of the model concerns neuronal activity as measured by LFP (Fig 1). It is widely agreed that LFP can be interpreted as the sum of average excitatory and inhibitory post-synaptic potentials (EPSP_PC and IPSP_PC) [31]. Firing rates from pyramidal cells and interneurons were also crucial variables (FR_PC and FR_IN) to incorporate into the model as triggers to the release of glutamate and GABA (Fig 2A). These criteria have already been accounted for at the level of populations by a variety of neural mass models. We chose the model described in [32, 33]. These models are often used in epilepsy in order to study the deregulation of the excitation/inhibition balance.

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Fig 2. Modeling and experimental recording of neuronal activity.

(A) Variables and relationships of the neural mass model (Table 1 and S1 Table). The input to the model is the activity coming from afferent populations, together with the injection of bicuculline in order to elicit an epileptic activity. The model output is the simulated local field potential (LFP). (B) and (C) Model-data comparison between the neural mass model (black) and LFP recording (light gray) for two isolated discharges. The mean value of p was set to m_B = 3.07 and its standard deviation to σ_p = 0. Magnitudes of the discharges (B) A_peak = 8.14 mV and (C) A_peak = 4.13 mV were obtained with the gain in Eq 2 set to (B) G₁ = 965 and (C) G₉ = 535.

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In this context, the excitatory input p (Fig 2A) is usually defined as a Gaussian noise to represent the average density of afferent action potentials. Indeed, a Gaussian noise is able to induce spontaneous epileptiform discharges, which represent the epileptic activity induced by the pathology in experimental models where animals become epileptic, for instance after three weeks in the kainate mouse model [34]. Here, we relied on an acute model of epilepsy, namely epileptiform discharges elicited after injection of bicuculline (with injection site at 1000–1500 μm) in a healthy cortex (Materials and Methods), together with the effect of the afferent populations (LFP recorded at about 500 μm). Therefore, we described the input p to the model as a Gaussian noise with mean m_p and standard deviation σ_p: (1) where (2) is the sum of the mean value m_B representing the effect of the afferent populations and a linear combination representing the effect of bicuculline injection (Fig 2A). In Eq 2, the effect of bicuculline injection is the rectangular function lasting Δ = 10 samples (i.e. 8 ms at 1250 Hz) and defined as the difference between the Heaviside function H(t_i) at time t_i, and its Δ-delayed version H(t_i-Δ). Gains G_i in Eq 2 represent the magnitude of the effect at time t_i and thus decreased over time as the magnitude of the discharges was reduced with bicuculline wash-out.

The average excitatory post-synaptic potential (from pyramidal cells to pyramidal cells, Fig 2A) was defined as: (3) where EPSP_IN is the average excitatory post-synaptic potential (from pyramidal cells to interneurons, Fig 2A) of interneurons. The sigmoid function in Eq 3 was defined as: (4) where x is the dummy variable, V the maximum value, s the threshold and r the slope of the sigmoid (see Table 1 for a description of all the parameters used in the proposed NGV model, as well as the values either used in the literature or modified in the present study).

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Table 1. Parameters values chosen from the experimental literature.

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The average inhibitory post-synaptic potential IPSP_PC (from interneurons to pyramidal cells, Fig 2A) was similarly defined as: (5) where FR_IN is the firing rate of interneurons, related to their EPSP_IN according to the equation: (6) using the sigmoid function from Eq 4. The average excitatory post-synaptic potential EPSP_IN of interneurons depended on the firing rate FR_PC of pyramidal cells according to: (7) where FR_PC was defined as the sigmoid function from Eq 4 applied to the simulated LFP, as follows: (8)

Comparison between the neural mass model and experimental interictal-like discharges

Simultaneous LFP-LD data were recorded after bicuculline injection and isolated events were extracted (Materials and Methods, S1 Fig). We found that the magnitude of the isolated discharges decreased over time, as bicuculline was washed out. For the nine extracted discharges, this magnitude varied (mean 6.38±1.33, n = 9) from A_peak = 8.14 mV (Fig 2B) to A_peak = 4.13 mV (Fig 2C). Parameters of the neural mass part (except for gains G_i), described by Eqs 1–8, were set to values leading to simulated LFP as close as possible to the experimental discharges (Materials and Methods, S2 Fig). This was done by visualizing both the simulated and experimental LFP. Indeed, as parameters of the neural mass model are well known (see typical values and references in Table 1), we thus had a priori information about their effect on the temporal dynamics of the simulated epileptiform discharges. Note that the obtained parameters values (Table 1) were included in the typical range of the literature. Values of gains G_i in Eq 2 varied in order to obtain magnitudes of the simulated LFP reaching exactly the magnitudes A_peak of the extracted discharges. Due to bicuculline wash-out, the obtained values corresponded to reduced discharge magnitudes: G₁ = 965, G₂ = 929, G₃ = 923, G₄ = 810, G₅ = 756, G₆ = 690, G₇ = 707, G₈ = 673 and G₉ = 535. Using these values, the simulated LFP (in black, Fig 2B and 2C) matched extremely well the LFP recording (in gray, Fig 2B and 2C). As the gain was the only modified parameter across simulated discharges, these results can be interpreted as the influence of bicuculline wash-out on a physiological state (here, the studied rat) depicted by the other pre-defined parameters. Besides, because the simulated discharges were the input to the remaining of the sequential model (S2 and S3 Figs), achieving a satisfactory agreement between the model and the data for the neuronal part of the model was essential in order to ensure a similar consistency for the subsequent compartments.

A simple model of the neurotransmitter (glutamate and GABA) cycles

Neuro-glial interactions occurring at the glutamatergic and GABAergic synapses [55], i.e. between the neuronal and astrocytic compartments of the model, involve a variety of processes called neurotransmitter cycles. A first version of the neurotransmitter cycles [50, 56] was simplified (Fig 3A, S1 File) to glutamate and GABA releases and uptakes. When defining equations and parameter values, we considered the physiological interactions at the level of synapses and we converted them to the population scale according to the experimental literature (Table 1).

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Fig 3. Modeling of the glutamate and GABA cycles according to the experimental literature.

(A) Main physiological principles of glutamate and GABA cycles are glutamate and GABA releases by pyramidal cells and interneurons respectively, glutamate uptake by astrocytes and GABA uptake by both neurons and astrocytes (S1 File). (B) The glutamate release (solid line) from Eq 9 matches the experimental impulse response (circles) depicted in Fig 4 in [40] for the parameter set {W = 0.59, w₁ = 90, w₂ = 33}. (C) Comparison between the simulated glutamate uptake from Eq 11 and Michaelis-Menten representations {V_M, K_M} obtained from the experimental literature and converted to the same unit (Table 1). The Michaelis-Menten representations are numbered according to the experimental literature with 1: {V_M = 9.5, K_M = 91} for [42]; 2a: {V_M = 4.2, K_M = 18.6}, 2b: {V_M = 4.8, K_M = 37}, 2c: {V_M = 2, K_M = 16} for [44]; 4a: {V_M = 6.8, K_M = 18.9}, 4b: {V_M = 1.5, K_M = 54.9} for [45]. Setting parameters of Eq 11 to {V_mg = 5, r_g = 0.5, s_g = 9} led to a sigmoid function (solid line), which was close to the experimental measures for the usual physiological values of extracellular glutamate concentration (below 10 μM). (D) Comparison between Michaelis-Menten responses of the GABA uptake from Eqs 15 and 16 with {V_m1 = 5, K_m1 = 24} for GAT1 transport (neurons, in gray) and {V_m1 = 2, K_m1 = 8} for GAT3 transport (astrocytes, in black).

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We started from Fig 4 from [40] giving the extracellular concentration of glutamate in response to a single action potential (impulse response). The shape of this response (Fig 3B), with very different rise time and decay time, led us to introduce the bi-exponential function described in [39, 57]. In order to adapt the model to the population scale, we applied this function to the firing rate FR_PC to define the glutamate release Glu_N→E as follows: (9)

The dynamics of GABA release by interneurons found in the experimental literature look like a bi-exponential function [58–60] with a variety of dynamic constants [41]. We thus chose the bi-exponential function [61] applied to the firing rate FR_IN for the GABA release GABA_N→E equation, as follows: (10)

Our NGV model considered glutamate uptake from both neurons and astrocytes (Fig 3A). Astrocytes demonstrate a sensing capacity to probe the extracellular space, i.e. glutamate uptake by astrocytes is activated when a given level of extracellular glutamate concentration is reached. Moreover, glutamate uptake by astrocytes presents a saturation effect. In order to take these mechanisms into account, we chose to represent the glutamate uptake Glu_E→A by astrocytes with the sigmoid function of Eq 4: (11)

Glutamate reuptake by neurons being typically 10% [62] to 20% [63] of the total glutamate concentration taken from the extracellular space, we simply defined glutamate reuptake by neurons Glu_E→N as follows: (12) where M corresponds to the fraction of glutamate reabsorbed by neurons. For simplification purposes, we set M = 0 in this study. The variation in the extracellular concentration of glutamate was defined as the difference between the release rate of Eq 9 and the uptake rate of Eqs 11 and 12, given by: (13)

Complex mechanisms occur into the astrocytic compartment (S1 File). As a first approximation, we considered that the consumption rate V_gme of glutamate into the astrocytes was constant. This led to the astrocytic glutamate concentration Glu_A defined as follows: (14)

As opposed to glutamate uptake, which is mainly achieved by astrocytes, GABA uptake is mainly due to reuptake by interneurons [64]. We kept the Michaelis-Menten representation (Table 1) used in the experimental literature to model GABA uptakes, whereas a sigmoid function was used to model glutamate uptake, which is a more significant process acting on CBF dynamics [50]. Actually, GABA transport and metabolism seem to involve less complex processes than those of glutamate [46, 65]. Therefore, the neuronal and astrocytic GABA uptakes were, respectively: (15) and (16) where GABA_E is the extracellular GABA concentration and {V_m1, K_m1, V_m3, K_m3} are the Michaelis-Menten parameters (Table 1). In this type of representation, the parameter V_m defines a saturation phenomenon (maximum rate) and the parameter K_m (Michaelis-Menten constant) defines the curve slope for increased values of the extracellular concentration. We described the variation of GABA_E by the difference between the release rate of Eq 10 and the uptake rates of Eqs 15 and 16, which led to the following equation: (17)

In the same way as for glutamate degradation into the astrocytic compartment, a constant rate V_gba was considered for GABA degradation, so that the astrocytic GABA concentration was simply given by: (18)

Comparison between the modeled neurotransmitter cycles and the experimental literature

We did not have access to experimental data linked to these cycles, such as recordings with glutamate probes [66, 67]. The parameter values for the neurotransmitter cycle part of the model (Eqs 9–18) were thus chosen in the experimental range found in the experimental literature (Fig 3 and Table 1). For model-data comparison regarding glutamate release, we manually tuned parameters {W, w₁, w₂} of Eq 9 as close as possible (solid line in Fig 3B) to the experimental impulse response of an action potential (dotted line in Fig 3B) of [40]. In order to adapt the model from the synaptic scale of an action potential to the population scale of the firing rate FR_PC, we kept the values of w₁ and w₂ that ensured physiological rise and decay times and we modified the magnitude W of the response according to the stationary state of the model (S3 File). The same methodology was followed to set the parameters {Z, z₁, z₂} of GABA release in Eq 10 (Table 1). We chose the sigmoid function of Eq 11 to represent glutamate uptake by astrocytes although experimental studies are usually based on Michaelis-Menten representation. As a consequence, we had to manually tune parameters {V_mg, r_g, s_g} so that the resulting sigmoid was as close as possible to the different Michaelis-Menten experimental curves (Fig 3C). We thus adapted the parameters of the sigmoid in order to obtain, for typical values of Glu_E (range 0–10 μM) in our study, both the same level of saturation and the same slope as for the Michaelis-Menten curves. GABA uptake parameters {V_m1, K_m1} of the neuronal contribution of Eq 15 correspond to mGAT1 for mice and GAT1 for rats/humans (S1 File). Similarly, GABA uptake parameters {V_m3, K_m3} of the astrocytic contribution of Eq 16 correspond to mGAT4 for mice and GAT3 for rats/humans (S1 File). After unit conversion (Table 1), we chose the values {V_m1 = 5, K_m1 = 24, V_m3 = 2, K_m3 = 8} leading to a more important uptake for GAT1 than for GAT3 (Fig 3D) as depicted in the literature (Table 1, S1 File).

Simulations of the neurotransmitter cycles agree with the phenomenological literature

With pre-defined parameters for the neurotransmitter cycle part of the model, we were able to obtain the simulated temporal dynamics of the extracellular concentrations Glu_E and GABA_E (Fig 4B and 4C) for the highest and lowest level isolated discharges (Fig 4A) separated by 105 s.

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Fig 4. Temporal simulations of the NVG model.

(A) Simulated LFP for discharges number 1 (with the highest level) and number 9 (with the lowest level) separated by 105 s reproduced bicuculline wash-out as a function of time. (B) Temporal simulation of the resulting extracellular concentration of glutamate Glu_E was in a good agreement with the temporal dynamics of experimental recordings with a glutamate probe such as those found in Fig 2 in [67]. (C) The resulting extracellular GABA concentration GABA_E had a slower dynamics than Glu_E.

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The obtained dynamics of Glu_E (Fig 4B) were in good agreement with the experimental literature such as the dynamics depicted in Fig 2 in [67]. The dynamics of the extracellular GABA concentration GABA_E (Fig 4C) were more difficult to compare with the experimental literature because GABA_E is usually indirectly measured by inhibitory postsynaptic currents and there exists a wide variety of shapes and magnitdes of the responses [41]. The obtained GABA_E dynamics were slower than those of glutamate transport, which was consistent with the literature [68].

A new representation of CBF changes: introducing both neuronal and astrocytic contributions

Glu_E and GABA_E uptakes by astrocytes contribute to a local increase in CBF (a phenomenon referred to as functional hyperemia) via a variety of vasoactive mediators (see [8, 69–71] for good reviews on the different mediators that could be involved). This increase is achieved through different mechanisms (S2 File). Interestingly, these mechanisms correspond to a slow indirect contribution of astrocytes and a rapid direct contribution of neurons [8, 72–74], represented in a parallel manner in our model (Fig 5A).

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Fig 5. Modeling cerebral blood flow (CBF) dynamics.

(A) CBF dynamics represented by the variable F_in consist of a neuronal contribution and an astrocytic contribution. (B) Model-data comparison was assessed by the relative error |F_peak,simu—F_peak,expe|/F_peak,expe, where F_peak,simu is the CBF magnitude collected on the simulations and F_peak,expe is the CBF magnitude collected on the laser Doppler recording (Materials and Methods). This relative error (in %, coded in grayscale with black for lower values and white for higher values) was represented as a function of the magnitude A_peak of the extracted discharges and the parameter set leading to the magnitude F_peak,simu. (C) Same as (B) for time t_peak of the main peak (Materials and Methods).

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To our knowledge, only one experimental demonstration [7] has concluded that when isoflurane concentration varied from high to low, astrocytic calcium response (to visual stimuli) was reduced by 77±14%, compared with a 16±8% reduction for neuronal calcium response. Therefore, we considered that CBF dynamics, linked to calcium dynamics, could be approximately explained by a 80% contribution of astrocytes (f_A) and a 20% contribution of neurons (f_N), which led to the equation: (19) where {f_N, f_A, f_in} are variables normalized to the flow baseline F₀, and F_in is the cerebral blood flow rate entering the voxel. In order to represent the neuronal contribution f_N, corresponding to fast and direct increase by synaptic projections through dinoprostone (PGE2, cyclooxygenase (COX-2), and nitric oxyde (NO) (S2 File), we chose the well-known model of [3] described by the equation: (20) where norm_u1 is the baseline value of EPSP_PC. Likewise, we described the astrocytic contribution f_A to the flow dynamics, representing a slow activity via cascades of mediators (including glutamate and GABA) such as NO and epoxyeicosatrienoic acids (EETs), by the equation: (21) where norm_u2 is the baseline value of the uptakes. In practice, norm_u1 and norm_u2 were computed on a slot of 30 s (initial value 1), setting the input characteristics to σ_p = 0 and G_i = 0 (see S1 Table for baseline values and S3 File for more details on the stationary state calculations). With these ultimate equations, the entire neuro-glio-vascular model (S3 Fig) is described by a system of ordinary differential equations (S1 Table).

Model-data comparison between the CBF part of the model and the LD recording corresponding to the discharges

To explore the capacity of the model to represent epileptic phenomena, we decided to manually tune (S2 Fig) the resulting simulations of the total inflow f_in to reproduce the isolated laser Doppler recordings, as we assumed f_in to be directly related to these recordings (no observation equation). Isolated LD events were extracted together with the isolated discharges from the continuous (LFP-LD) dataset (Materials and Methods, S1 Fig). As the laser Doppler recording was very noisy, we first filtered the isolated events and defined their local baseline in the trough before the principal peak. This allowed us to compare the normalized variable f_in with the normalized isolated events. The difficulty to define a baseline value for the CBF led us to assume a standard shape for CBF events i.e. with neither initial dip nor post-stimulus undershoot. Actually, we were only interested in the magnitude of the main peak, not in the entire shape of the CBF dynamics, because we sought to study its relationship to the magnitude of the epileptiform discharges. We thus manually tuned the parameters {ε_n, τ_sn, τ_fn, ε_a, τ_sa, τ_fa} describing the neuronal and astrocytic flow contributions f_N and f_A of Eqs 20 and 21 in order to obtain a magnitude of f_in as close as possible to that of the isolated and filtered laser Doppler recordings. For the highest and lowest level discharges, we obtained five parameters sets (configurations) matching well the extracted LD recordings. Obtained sets of parameters were S₁: {ε_n = 35, τ_sn = 1.3, τ_fn = 6.0, ε_a = 8, τ_sa = 1.6, τ_fa = 10.3}, S₂: {ε_n = 35, τ_sn = 1.2, τ_fn = 5.8, ε_a = 31, τ_sa = 1.3, τ_fa = 3.0}, S₃: {ε_n = 35, τ_sn = 1.2, τ_fn = 5.8, ε_a = 60, τ_sa = 0.8, τ_fa = 0.7}, S₄:{ε_n = 22, τ_sn = 1.6, τ_fn = 10.3, ε_a = 44, τ_sa = 0.4, τ_fa = 0.7} and S₅:{ε_n = 12, τ_sn = 1.0, τ_fn = 4.0, ε_a = 120, τ_sa = 1.9, τ_fa = 3.5}. Other configurations may lead to a good agreement between the simulations and the experimental flow events. Nevertheless, we did not analyze identification and uniqueness problems in the present study because as a first step, this model-data comparison was aimed at studying the capacity of the model to reproduce a single example of experimental data.

The different sets of parameters contributing to CBF magnitude depend on the discharge magnitude

So as to explore quantitatively this model-data comparison, we collected (Materials and Methods, S1 Fig) the magnitude values F_peak,simu of the simulated variable f_in and compared them with the magnitude values F_peak,expe of the LD recordings, for each one of the nine events and for each one of the five parameter sets (Fig 5B). We found that the sets that best matched (i.e. the relative error was nearly zero) the experimental recordings were {S₄, S₅} for the highest level discharges and {S₁, S₂, S₃} for the lowest level discharges. Likewise, we collected the instants of the flow peaks, called t_peak,simu for the simulated flow f_in and t_peak,expe for the LD recordings, respectively (Materials and Methods, S1 Fig). We also found a dependence between the adaptation of the parameter sets to F_peak,expe and the magnitude A_peak of the discharges (Fig 5C).

Nonlinear relationship between CBF magnitude and epileptiform discharge magnitude in isolated events

Parameter sets correspond to the values of parameters {ε_n, τ_sn, τ_fn, ε_a, τ_sa, τ_fa} of Eqs 20 and 21 representing both the neuronal and astrocytic contributions f_N and f_A to the total inflow. In order to quantify the balance between the neuronal and astrocytic contributions to f_in, without doing any specific study on these contributions, we defined an index Q based on the second-order differential equations describing f_N and f_A respectively (Materials and Methods). Depending on the parameter values, each set put more or less emphasis on either the neuronal contribution or the astrocytic contribution to f_in, with Q>1 for emphasis on neuronal contribution and Q <1 for emphasis on astrocytic contribution, respectively. We obtained Q = {3.86, 2.56, 2.15, 0.46, 0.41} for the five sets of parameters {S₁, S₂, S₃, S₄, S₅}, respectively. We therefore conclude that sets {S₁, S₂, S₃} put more emphasis on neuronal contribution and that sets {S₄, S₅} put more emphasis on astrocytic contribution. Consequently, this indicates that neuronal configurations {S₁, S₂, S₃} bore a close resemblance to the experimental LD recordings as the discharge magnitude A_peak was reduced. On the contrary, astrocytic configurations {S₄, S₅} seemed to better approximate the experimental recordings as A_peak increased. In physiological terms, it seems that for a low level discharge, the neuronal impact on the vessels is sufficient to elicit a flow response. However, when the discharge is high enough, astrocytic mechanisms are yet in action and lead to a more significant contribution of astrocytes to the flow. Interestingly, neuronal contribution was linear whereas astrocytic contribution was nonlinear (due to nonlinear uptake mechanisms). As a consequence, the relationship between neuronal activity (epileptiform discharges) and CBF dynamics (magnitude and timing of LD recordings) seems nonlinear for sufficiently high level discharges.

Discussion

We proposed a new computational model developed at the scale of populations corresponding to multimodal acquisitions such as sEEG-fMRI. The model represents the forward signaling chain from neuronal activity to CBF changes, involving neurotransmitter (glutamate and GABA) cycles via the astrocytes. Particular efforts were done to achieve simple equations while describing the main physiological principles found in the literature.

We present a comparison of the model with bimodal (simultaneous LFP and laser Doppler) data acquired in the context of epilepsy. This sequential model-data comparison showed a good agreement of the model with the LFP recordings on isolated epileptiform discharges. This was obtained by manually tuning the neuronal parameters to patho-physiological values. We collected from the experimental literature a physiological range for the parameters of the glutamate and GABA cycles. Although we did not study in details the capacities and limitations of our model concerning these cycles, we showed its ability to reproduce typical dynamics of the extracellular concentrations of glutamate and GABA when elicited by neuronal events. We reproduced the laser Doppler recordings corresponding to the same isolated events with different sets of parameters. Although this comparison showed a limitation of our model in terms of identification of the whole CBF dynamics, it nevertheless provided mechanistic insights about the relationship between magnitudes of the neuronal activity and that of CBF. In particular, we showed that this relationship was due to neuronal contribution for low level neuronal events and to astrocytic contribution for higher level ones. Although it has been recently shown that the large majority of astrocytes responded with a calcium elevation to ictal but not interictal discharges [75], we found that an astrocytic contribution was already present when the interictal discharge was sufficiently important. Since astrocytic contribution to CBF increase is nonlinear, this result implies that the relationship between neuronal activity and CBF can be nonlinear, at least as far as sufficiently high level events are considered. A study of the balance between the dynamics of the neuronal and astrocytic contributions, respectively, by comparing our NGV model with data such as recordings in [7], would allow us to go further in the understanding of the sources of CBF changes.

More generally, understanding the neuro-glio-vascular coupling that exists between neurons, glial cells and vessels remains a difficult issue, given the number of metabolite interactions and the complexity of these interactions. Consequently, many studies may be considered starting from the existing model to understand other relationships, or expanding/modifying this model in order to take other mechanisms into account. For instance, we will be able to study the impact of discharge frequency on the nonlinear relationship between LFP and CBF, which could be directly linked to the extracellular concentration of neurotransmitters. More generally speaking, as the model includes a simple version of the glutamate and GABA cycles, we could further study the role of astrocytes in the excitation/inhibition balance. Indeed, this balance is important in a number of pathologies such as epilepsy. Under neuronal hyperactivity, apart from a massive release of neurotransmitters, a high level of metabolism activity (via a large potassium increase) is also induced in astrocytes. A longer-term perspective of this work is thus an extension of the model to metabolic mechanisms such as oxygen supply, which are also directly linked to astrocytic activity and CBF changes.

Materials and Methods

All experimental protocols were approved by The Ethical Committee for Animal Experimentation of Marseille (approval number 30–03102012). The experimental protocol was performed in vivo on one Wistar-Han rat under general anesthesia (initially anesthetized with 5% and maintained under 2% isoflurane in 1 l/min of O₂) delivered as a constant stream. The animal was euthanized by uretan injection during anesthesia.

Simultaneous (LFP-LD) recordings in vivo

The animal was equipped with one tungsten electrode and one Doppler electrode, located above the somatosensory cortex. LFP were recorded by sharpened tungsten electrodes lowered to 500 μm into the cortex, close to the bicuculline injection site. CBF was recorded by a laser Doppler system (Perimed Periflux System 5000, Stockholm, Sweden; 0.03 s time constant, 780 nm laser). To measure CBF as locally as possible, we used a needle probe (Perimed probe 411) with a small separation (0.15 mm) between emitting and collecting light fibers [2]. To elicit epileptic discharges, bicuculline methochloride (2.5 mM, Abcam, UK) was infused at a rate of 200 nl/min during 5 min (1 μl total infusion) using a 5 μl microsyringe (Hamilton, 75RN neuro syringe) mounted to a micropump at a depth between 1000 and 1500 μm targeting the cortical layers III to VI. Epileptic discharges appeared about 7 s after the onset of the infusion.

Extracted isolated events and chosen characteristics used for model-data comparison

The continuous (LFP-LD) data set is composed of isolated interictal-like discharges and burst discharges for LFP recordings, together with the simultaneous LD recordings corresponding to the CBF variations. We decided to compare the nonlinear model with the isolated events extracted from this continuous data set. To this end, we defined an isolated event as an interictal-like discharge on the LFP recording, i.e. an event which was sufficiently apart from the previous and following ones so that the corresponding LD variation had enough time to return to its (local) baseline.

Isolated discharges were extracted directly from the continuous dataset (no post-processing) and the magnitudes A_peak of their peak (S1 Fig) were collected manually. Note that the DC component of the LFP recording was hardware-filtered. LD recordings were first filtered with the Matlab^® (The Mathworks, Inc.) eegfilt function (1500 points) with a cutoff frequency of 0.5 Hz in order to obtain a smoothed version of the CBF temporal dynamics (S1 Fig). The CBF characteristics, collected from this smoothed version of the LD recording, were its magnitude F_peak and its duration F_long relative to the baseline (about 3 mV). We obtained nine isolated events with F_long varying from 22.2 s to 36.4 s, which were typical durations for the CBF to return to its baseline. We observed decreasing values of A_peak as the bicuculline local concentration was washed out over time.

A simple but physiologically-relevant model

We proceeded by incorporating a number of intermediate variables and the main pathways involved in NGV coupling, from the physiological literature [46]. In order to reduce the subsequent identification problems and the global model complexity in terms of the resulting number of differential equations, a refinement work was done at the same time for both the selection of physiological variables and the selection of the pathways to be taken into account, from the cellular to the mesoscopic level. Likewise, we defined mathematically each relationship with the objective to obtain equations as simple as possible, while keeping their physiological meaning. If appropriate equations existed in the literature, then they were used directly. Otherwise, we adapted existing equations or defined new ones.

Methodology for model-data comparison

The goal of model-data comparison was to study the capacity of the model to reproduce the extracted isolated events. The objective was to find at least one set of parameter values leading to simulated variables with a magnitude as close as possible to that of the corresponding recordings. The simulated variables LFP and f_in are located at the extreme sides of the (neural mass, glutamate and GABA cycles and sum) chain that constitutes the model (S2 Fig). In practice, we took advantage of the forward property of this chain, from neuronal activity to CBF changes [76], to conduct the comparison in a sequential manner (Materials and Methods and S2 Fig) from parameters of the input noise p of Eqs 1 and 2 to parameters of the neuronal and astrocytic contributions of Eqs 20 and 21. We chose the parameter values of the model in order to reproduce on the one hand, the dynamics observed in the simultaneous data for the observed (neuronal and vascular) parts of the model; on the other hand, the experimental literature for the intermediate non-observed (neurotransmitters cycles) part of the model.

Definition of ratio Q

We defined the ratio Q according to the following considerations. The neuronal and astrocytic contributions to the total inflow f_in, f_N, and f_A respectively, are given by the equations: where u₁ and u₂ are the inputs defined in Eqs 20 and 21. For these second-order systems, the magnitudes of the impulse responses are directly linked to the efficacy parameters ε_n and ε_a. Likewise, the durations of their responses are related to the quantities τ_fn/τ_sn² and τ_fa/τ_sa². Therefore, the quantities S_neu = ε_n.τ_fn/τ_sn² and S_ast = ε_a. τ_fa/τ_sa² describe quantitatively the importance of the responses f_N and f_A on f_in, respectively. The ratio Q = S_neu/S_ast thus gives an idea of the impact of the neuronal contribution f_N on the total inflow f_in, compared with the astrocytic contribution f_A.

Supporting Information

S1 Dataset. Simultaneously recorded (LFP-LD) isolated events used in this study.

For each extracted event i, time samples are denoted by ti, LFP samples by lfpi and laser Doppler samples by fi.

https://doi.org/10.1371/journal.pone.0147292.s001

(MAT)

S1 Fig. Example of an isolated event extracted from the simultaneous (LFP-LD) recordings.

(A) The chosen characteristic collected on the LFP recording (in gray) was its peak magnitude A_peak from baseline (dashed line). (B) The chosen characteristics collected on a smoothed version (in black, see Materials and Methods) of the direct LD recording (in gray) were its peak magnitude F_peak from the (local) baseline, its duration F_long, and the time t_peak of the peak.

https://doi.org/10.1371/journal.pone.0147292.s002

(TIFF)

S2 Fig. Model-data comparison between the forward NGV model and isolated events from simultaneous (LFP-LD) recordings.

Isolated discharges were used to manually tune the neural mass part of the model and the input p; the experimental literature was used to set the parameters of the glutamate and GABA cycles with physiologically-relevant values; the smoothed versions (in black) of the isolated LD data (in gray) corresponding to the isolated discharges were used to manually tune the CBF part of the model leading to the output f_in. The symbol Σ corresponds to the sum of Eq 19.

https://doi.org/10.1371/journal.pone.0147292.s003

(TIFF)

S3 Fig. Proposed neuro-glio-vascular (NGV) model and its link to multimodal recordings.

Dynamical variables are encapsulated in squares (S1 Table). The model input is a noise p representing the influence of the environment (average density of afferent action potentials and bicuculline injection). Electrophysiological relationships (dotted lines) between the pyramidal cells compartment and the interneurons compartment lead to the neuronal activity measured by local field potential (LFP). Glutamate and GABA neurotransmitters are released in the extracellular space and recycled (solid lines) by both neuronal and astrocytes compartments. These activities lead to the cerebral blood flow (CBF) dynamics (dotted-dashed lines) represented in the vascular compartment and measured by laser Doppler (LD).

https://doi.org/10.1371/journal.pone.0147292.s004

(TIFF)

S1 File. Physiological literature leads to a simple version of the glutamate and GABA cycles.

https://doi.org/10.1371/journal.pone.0147292.s005

(DOC)

S2 File. Neuronal and astrocytic contributions to CBF changes explained by the physiological literature.

https://doi.org/10.1371/journal.pone.0147292.s006

(DOC)

S3 File. Stationary state calculations and deduced parameters.

https://doi.org/10.1371/journal.pone.0147292.s007

(DOC)

S1 Table. Ordinary differential equations (ODE) describing the proposed neuro-glio-vascular model.

State variables are specified, together with their initial value (stationary state). Parameter description and values are given in S1 Table. (°) Chosen from [54]. (°°) Chosen to be the average in the range 2070–2630 μM mentioned in [77]. (*) Values obtained by stationary state (baseline) calculation (S3 File). Input is p(t) of Eqs 1 and 2. The integration of this ODE system by numerical methods such as Runge-Kutta 4 leads to the simulation of the output of the model, the total cerebral blood inflow given by f_in (t) = 0.8f_A(t)+0.2f_N(t).

https://doi.org/10.1371/journal.pone.0147292.s008

(DOC)

Acknowledgments

The authors wish to thank Loïc Le Marrec from the IRMAR Laboratory (Rennes, France) for helpful discussions on dynamical systems.

Author Contributions

Conceived and designed the experiments: SS AI CGB. Performed the experiments: SS AI CGB. Analyzed the data: SB. Contributed reagents/materials/analysis tools: SB HB FW MPI PB. Wrote the paper: SB MPI SS.

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Figures

Abstract

Introduction

Results

Adaptation of a neural mass model to an animal acute model of epilepsy

Comparison between the neural mass model and experimental interictal-like discharges

A simple model of the neurotransmitter (glutamate and GABA) cycles

Comparison between the modeled neurotransmitter cycles and the experimental literature

Simulations of the neurotransmitter cycles agree with the phenomenological literature

A new representation of CBF changes: introducing both neuronal and astrocytic contributions

Model-data comparison between the CBF part of the model and the LD recording corresponding to the discharges

The different sets of parameters contributing to CBF magnitude depend on the discharge magnitude

Nonlinear relationship between CBF magnitude and epileptiform discharge magnitude in isolated events

Discussion

Materials and Methods

Simultaneous (LFP-LD) recordings in vivo

Extracted isolated events and chosen characteristics used for model-data comparison

A simple but physiologically-relevant model

Methodology for model-data comparison

Definition of ratio Q

Supporting Information

S1 Dataset. Simultaneously recorded (LFP-LD) isolated events used in this study.

S1 Fig. Example of an isolated event extracted from the simultaneous (LFP-LD) recordings.

S2 Fig. Model-data comparison between the forward NGV model and isolated events from simultaneous (LFP-LD) recordings.

S3 Fig. Proposed neuro-glio-vascular (NGV) model and its link to multimodal recordings.

S1 File. Physiological literature leads to a simple version of the glutamate and GABA cycles.

S2 File. Neuronal and astrocytic contributions to CBF changes explained by the physiological literature.

S3 File. Stationary state calculations and deduced parameters.

S1 Table. Ordinary differential equations (ODE) describing the proposed neuro-glio-vascular model.

Acknowledgments

Author Contributions

References