Inhibitory Coupling Research Articles

Analyzing multicomponent permeation and coupling effects in pervaporation of Acetone-Butanol-Ethanol solutions using Maxwell-Stefan based modeling

The state-of-the-art research in pervaporation lacks a comprehensive understanding of the selective membrane permeation in real and multicomponent mixtures, a factor critical for its widespread commercial adoption. This work elucidates the contribution of various interaction effects, known as coupling phenomena, on the pervaporation of multicomponent Acetone-Butanol-Ethanol (ABE) solutions through a commercial PDMS membrane. Maxwell-Stefan (MS) diffusion formulations, combined with the UNIQUAC thermodynamic model, mostly restricted in literature to binary solutions permeating through a polymer membrane, are extended to multicomponent feed solutions. The thermodynamic correction factor [Γ] and correlation [B]−1 matrices are analyzed to resolve coupling into its thermodynamic and kinetic (diffusive) constituents.A generalized and explicit analytical derivation for calculation of [Γ] by UNIQUAC model is proposed, allowing estimation for any number of permeants. Subsequently, a significant inhibitory coupling of the component fluxes to the driving forces of its accompanying penetrants is revealed. In addition to the non-ideality arising from diagonal elements Γii, the substantial negative off-diagonal elements Γij in the thermodynamic correction matrices anticipate a decline in effective driving forces, butanol fluxes and permeation selectivity as we transition from binary butanol-water to multicomponent ABE mixtures.Following this, a detailed hit-and-trial based strategy is implemented to evaluate the relevance of membrane plasticization, cluster formation and interspecies frictional interactions towards the MS self and cross diffusivities within the [B]−1 matrix. Existing semi-empirical expressions for self-diffusivities in binary alcohol-water systems incorporating plasticization and clustering constants are reformulated and compared for their ability to accurately describe fluxes in binary and ternary ABE solutions. Furthermore, the optimal model fit was found to be insensitive to variation in MS cross-diffusivities. This suggests that the contribution of correlation effects may be neglected altogether, provided the expressions for self-diffusivities are adequately tailored for membrane plasticization and clustering. Notably, while only butanol clustering suffices for binary mixtures, additional aggregates involving butanol-water, water-water, and butanol-acetone/ethanol are suggested to play a crucial role in steering diffusivities in ternary solutions.Consequently, the proposed models exhibit exceptional agreement with experimental data, achieving R2 values of approximately 0.96, 0.95, and 0.98 for butanol flux in binary butanol-water and ternary solutions containing acetone and ethanol, respectively. Besides providing a more cohesive fit to the overall experimental permeation data, the usefulness of this approach lies in significantly simplifying model computations, thus easing the potential modeling of even more complex mixtures.

Photosensitive Control and Network Synchronization of Chemical Oscillators.

The Belousov-Zhabotinsky (BZ) reaction has long been a paradigmatic system for studying chemical oscillations. Here, we experimentally studied the synchronization control within photochemically coupled star networks of BZ oscillators. Experiments were carried out in wells performed in soda-lime glass constructed using novel laser technologies. Utilizing the inherent oscillatory nature of the BZ reaction, we engineered a star network of oscillators interconnected through photochemical inhibitory coupling. Furthermore, the experimental setup presented here could be extrapolated to more complex network architectures with both excitatory and inhibitory couplings, contributing to the fundamental understanding of synchronization in complex systems.

Synchronization of two non-identical Chialvo neurons

We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, and the coupling strength between the neurons. We propose the simplest neural network for study, as its analysis is more straightforward and does not compromise generality. Within this network, two non-identical neurons are electrically coupled. In order to understand whether specific behaviors affect the global behavior of the system, we consider different cases related to the behavior of the neurons (chaotic or periodic). Furthermore, we study how variations in model parameters affect the firing frequency in each case. Additionally, we consider that the two neurons have both excitatory and inhibitory couplings. Consequently, we identify critical values of noise and mismatch for achieving satisfactory synchronization between the neurons in each case. Finally, we propose that the results have general applicability across various neuron models.

Abstract 6324: Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity

Abstract Chimeric Antigen Receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies. However, the rarity of cell surface protein targets that are specific to cancerous but not vital healthy tissue has hindered its broad application to solid tumor treatment. While new logic-gated CAR designs have shown reduced toxicity against healthy tissues, the generalizability of such approaches across tumors remains unclear. Here, we harness a universal characteristic of endogenous T cell receptors (TCRs), their ability to discriminate between self and non-self ligands through inhibition of response against self (weak) antigens, to develop a broadly applicable method of enhancing immunotherapeutic precision. We hypothesized that this discriminatory mechanism, known as antagonism, would apply across receptors, allowing for a transfer of specificity from TCRs onto CARs. We therefore systematically mapped out the responses of CAR T cells to joint TCR and CAR stimulations. We first engineered murine T cells with an ovalbumin-specific TCR to express a CAR targeting murine CD19 and discovered that the expression of a strong TCR antigen on CD19+ leukemia enhanced CAR T killing. Importantly though, the presence of a weak TCR antigen antagonized CAR T responses, assessed by in vitro multiplexed dynamic profiling as well as in vivo cytotoxicity. We developed a mathematical model based on cross-receptor inhibitory coupling that accurately predicted the extent of TCR/CAR antagonism across a wide range of immunological settings. This model was validated in a CD19+ B16 mouse melanoma model showing that TCR/CAR antagonism decreased the infiltration of a tumor-reactive T cell cluster, while TCR/CAR agonism enhanced infiltration of this T cell cluster. We then applied our quantitative knowledge of TCR/CAR crosstalk to design an Antagonism-Enforced Braking System (AEBS) for CAR T cell therapy. This was assessed in a model system using a CAR targeting the tyrosine-protein kinase erbB-2 (HER2) together with a hedgehog acyltransferase (HHAT) peptide-specific TCR that binds strongly to mutated tumor neoantigen while retaining weak affinity for the wild-type self-antigen on healthy tissue. We established a humanized in vivo model of CAR T function and found that AEBS CAR T cells maintained high anti-tumor activity against a human lung adenocarcinoma (PC9) but notably, their anti-tissue cytotoxicity against human bronchial epithelial cells (BEAS-2B) was minimized. AEBS CAR T cells therefore sharpen the discriminatory power of synthetic anti-tumor lymphocytes. Our work highlights a novel mechanism by which TCRs can enforce CAR T cell specificity, with practical implications for the rational design of future anti-leukemia immunotherapies. Citation Format: Taisuke Kondo, François X. Bourassa, Sooraj Achar, Justyn DuSold, Pablo Cespedes, Madison Wahlsten, Audun Kvalvaag, Guillaume Gaud, Paul Love, Michael Dustin, Gregoire Altan-Bonnet, Paul François, Naomi Taylor. Antagonism-enforced braking system to enhance CAR T cell therapeutic specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6324.

Noise-induced synchronization and regularity in feed-forward-loop motifs

This study explores the impacts of multiple factors (noise, intra-motif coupling, and critical bifurcation parameter) on noise-induced motif synchrony and output regularity in three-node feed-forward-loops (FFLs), distinguishing between coherent FFLs with purely excitatory connections and incoherent FFLs formed by transitioning the intermediate layer to inhibitory connections. Our model utilizes the normal form of Hopf bifurcation (HB), which captures the generic structure of excitability observed in real systems. We find that the addition of noise can optimize motif synchrony and output regularity at the intermediate noise intensities. Our results also suggest that transitioning the excitatory coupling between the intermediate and output layers of the FFL to inhibitory coupling—i.e., moving from the coherent to the incoherent FFL—enhances output regularity but diminishes motif synchrony. This shift towards inhibitory connectivity highlights a trade-off between motif synchrony and output regularity and suggests that the structure of the intermediate layer plays a pivotal role in determining the motif’s overall dynamics. Surprisingly, we also discover that both motifs achieve their best output regularity at a moderate level of intra-motif coupling, challenging the common assumption that stronger coupling, especially of the excitatory type, results in improved regularity. Our study provides valuable insights into functional differences in network motifs and offers a direct perspective relevant to the field of complex systems as we consider a normal-form model that pertains to a vast number of individual models experiencing HB.

Intra-ripple frequency accommodation in an inhibitory network model for hippocampal ripple oscillations.

Hippocampal ripple oscillations have been implicated in important cognitive functions such as memory consolidation and planning. Multiple computational models have been proposed to explain the emergence of ripple oscillations, relying either on excitation or inhibition as the main pacemaker. Nevertheless, the generating mechanism of ripples remains unclear. An interesting dynamical feature of experimentally measured ripples, which may advance model selection, is intra-ripple frequency accommodation (IFA): a decay of the instantaneous ripple frequency over the course of a ripple event. So far, only a feedback-based inhibition-first model, which relies on delayed inhibitory synaptic coupling, has been shown to reproduce IFA. Here we use an analytical mean-field approach and numerical simulations of a leaky integrate-and-fire spiking network to explain the mechanism of IFA. We develop a drift-based approximation for the oscillation dynamics of the population rate and the mean membrane potential of interneurons under strong excitatory drive and strong inhibitory coupling. For IFA, the speed at which the excitatory drive changes is critical. We demonstrate that IFA arises due to a speed-dependent hysteresis effect in the dynamics of the mean membrane potential, when the interneurons receive transient, sharp wave-associated excitation. We thus predict that the IFA asymmetry vanishes in the limit of slowly changing drive, but is otherwise a robust feature of the feedback-based inhibition-first ripple model.

Uncovering the Neural Mechanisms of Inter-Hemispheric Balance Restoration in Chronic Stroke through EMG-Driven Robot Hand Training: Insights from Dynamic Causal Modeling.

EMG-driven robot hand training can facilitate motor recovery in chronic stroke patients by restoring the interhemispheric balance between motor networks. However, the underlying mechanisms of reorganization between interhemispheric regions remain unclear. This study investigated the effective connectivity (EC) between the ventral premotor cortex (PMv), supplementary motor area (SMA), and primary motor cortex (M1) using Dynamic Causal Modeling (DCM) during motor tasks with the paretic hand. Nineteen chronic stroke subjects underwent 20 sessions of EMG-driven robot hand training, and their Action Reach Arm Test (ARAT) showed significant improvement (β=3.56, p<0.001). The improvement was correlated with the reduction of inhibitory coupling from the contralesional M1 to the ipsilesional M1 (r=0.58, p=0.014). An increase in the laterality index was only observed in homotopic M1, but not in the premotor area. Additionally, we identified an increase in resting-state functional connectivity (FC) between bilateral M1 (β=0.11, p=0.01). Inter-M1 FC demonstrated marginal positive relationships with ARAT scores (r=0.402, p=0.110), but its changes did not correlate with ARAT improvements. These findings suggest that the improvement of hand functions brought about by EMG-driven robot hand training was driven explicitly by task-specific reorganization of motor networks. Particularly, the restoration of interhemispheric balance was induced by a reduction in interhemispheric inhibition from the contralesional M1 during motor tasks of the paretic hand. This finding sheds light on the mechanistic understanding of interhemispheric balance and functional recovery induced by EMG-driven robot training.

Phase-locked states in a spiking neural network model with a context-dependent connectivity

The investigation of nonlinear processes in brain systems based on the oscillatory-wave approach is currently one of the most consequential domains of researching signal generation and processing mechanisms in the brain. These investigations may offer insights into various phenomena, such as the mechanism of associative memory, based on network models of biologically relevant neurons. A neural network architecture is proposed in this paper to solve applied problems, such as signal filtering, processing, and recognition of information images. Mechanisms for incomplete fragments were implemented, enabling the extraction and complete restoration of objects from memory. Each neuron in this neural network comprises a Hodgkin–Huxley biophysical model with a Mainen modification, the dynamics of which most plausibly reproduce the processes in the neural cells of the brain. In the research on individual neurons, a constant external current with varying amplitudes was applied. The study determined the average periods of self-oscillations and identified parameter values for the Andronov–Hopf bifurcation. In particular, a stable limit cycle is created and destroyed depending on different scenarios when the bias current is increased or decreased. A stable limit cycle arises via the Andronov–Hopf bifurcation and is terminated via the saddle-node bifurcation on the cycle. The study identified parameter regions whereby two neurons with excitatory and inhibitory synaptic connections synchronize. The neurons were all in self-oscillating mode and exhibited a stable limit cycle in the phase space. A three-layer neural network was constructed, comprising of a reference neuron, sensory, control, and interneurons. The control and intermediate layer connections are arranged based on the Hebbian learning rule. Areas of excitation for various types of neurons were found. Experiments were conducted using the neural network architecture to distinguish binary information patterns encoded through signal phase. Excitatory and inhibitory synaptic connections were employed to encode the images stored in memory. The dynamic patterns are determined through the in-phase or anti-phase mode of phase locking, i.e., phase synchronization relative to the global rhythm. Before reaching the output layer of neurons, the signals are summed on the interneuron layer with the signal of the reference neuron, resulting in the filtering out of a certain information segment (in-phase or anti-phase). The network accurately recognized the patterns after parameter adjustments were made. Areas of neuron phase synchronization were discovered, enabling control of network activity modes. The confirmation of the viability of phase capture and pattern recognition modes using Hodgkin–Huxley–Mainen neurons was achieved. In total, as a result of the implementation of the spike neural network modeling project: 1) two-parameter diagrams of regions of neuronal excitation in various modes are calculated; 2) the effect of excitatory and inhibitory coupling in synaptic currents is used to represent the input signal; 3) an abstract mathematical algorithm for calculating the Hebbian matrix has been transferred to the implementation of synaptic currents between layers of neurons; 4) the effect of phase clusters is used to represent the output pattern; 5) the neurons’ topology in the visual-cerebral department served as a working model for recognizing graphic images through the Hopfield network on a spike neural network, using the Hebbian rule.

A human cortical adaptive mutual inhibition circuit underlying competition for perceptual decision and repetition suppression reversal

A model based on inhibitory coupling has been proposed to explain perceptual oscillations. This 'adapting reciprocal inhibition' model postulates that it is the strength of inhibitory coupling that determines the fate of competition between percepts. Here, we used an fMRI-based adaptation technique to reveal the influence of neighboring neuronal populations, such as reciprocal inhibition, in motion-selective hMT+/V5. If reciprocal inhibition exists in this region, the following predictions should hold: 1. stimulus-driven response would not simply decrease, as predicted by simple repetition-suppression of neuronal populations, but instead, increase due to the activity from adjacent populations; 2. perceptual decision involving competing representations, should reflect decreased reciprocal inhibition by adaptation; 3. neural activity for the competing percept should also later on increase upon adaptation. Our results confirm these three predictions, showing that a model of perceptual decision based on adapting reciprocal inhibition holds true. Finally, they also show that the net effect of the well-known repetition suppression phenomenon can be reversed by this mechanism.

Harnessing TCR/CAR Antagonism to Enhance Immunotherapeutic Precision

Chimeric Antigen Receptor (CAR) T cell immunotherapy represents a conceptual breakthrough in the treatment of hematological malignancies. However, the rarity of cell surface protein targets specific to cancerous but not vital tissue has hindered its broad application to solid tumor treatment. While new logic-gated CAR designs have shown reduced toxicity against healthy tissues, the generalizability of such approaches across tumors remains unclear. Here, we harness a universal characteristic of endogenous T cell receptors (TCRs), their ability to discriminate between self and non-self ligands through inhibition of response against self (weak) antigens, to develop a broadly applicable method of enhancing immunotherapeutic precision. We hypothesized that this discriminatory mechanism, known as antagonism, would apply across receptors, allowing for a transfer of specificityfrom TCRs onto CARs. We therefore systematically mapped out the responses of CAR T cells to joint TCR and CAR stimulations. We transduced ovalbumin-specific TCR T cells with mouse CD19 CAR to produce T cells expressing both TCR and CAR and evaluated the response of TCR/CAR T cells using in vivo and in vitro leukemia models ( Figure 1A). We discovered that strong TCR antigen enhanced CAR T killing of CD19 + leukemia, while weak TCR antigen antagonized CAR T responses as assessed in vivo cytotoxicity and in vitro multiplexed dynamic profiling ( Figure 1B). We developed a mathematical model based on cross-receptor inhibitory coupling that accurately predicted the extent of TCR/CAR antagonism across a wide range of immunological settings. This model was validated in a CD19 + B16 mouse melanoma model showing that TCR/CAR antagonism decreased the infiltration of a tumor-reactive T cell cluster (cluster 1), while TCR/CAR agonism enhanced infiltration of this cluster1 ( Figure 1C). We then applied our quantitative knowledge of TCR/CAR crosstalk to design an Antagonism-Enforced Braking System (AEBS) for CAR T cell therapy. This was assessed in a model system using a CAR targeting the tyrosine-protein kinase erbB-2 (HER2), expressed on a subset of patients with both B-ALL and AML together with a hedgehog acyltransferase (HHAT) specific TCR, which responds strongly to mutated peptides presented on tumor cells and weakly to wild-type peptides presented on healthy tissue. Consistent with our discovery of the TCR/CAR antagonism, TCR signals against healthy cells expressing wild-type HHAT peptide antigen antagonized HER2 CAR T cell responses, minimizing on-target/off-tumor cytotoxicity against healthy cells. Notably though, AEBS-CAR T cells exhibited high anti-tumor cytotoxicity against tumor cells expressing HER2 and mutated HHAT peptides ( Figure 1D). AEBS CAR T cells sharpen the discriminatory power of synthetic anti-tumor lymphocytes, laying the groundwork for future studies to engineer complex logic into cells with minimal numbers of receptors ( Figure 1E). Our work highlights a novel mechanism by which TCRs can enforce CAR T cell specificity, with practical implications for the rational design of future anti-leukemia immunotherapies.

Propagation Effect of Epileptic Seizures in a Coupled Thalamocortical Network

Epileptic seizures have spatial features related to the propagation of seizure waves. As the main characteristic of absence seizures, 2–4[Formula: see text]Hz spike-wave discharges (SWDs) originate from the cortices and are maintained by the thalamus. In this study, we explore the onset and propagation effect of absence seizures based on a thalamocortical model. First, we develop a two-compartment model and consider the autapse of the thalamic reticular nucleus as a crucial parameter to investigate transition behaviors. Moreover, we present dynamical mechanisms through bifurcation analysis. Simulation results show that the absence seizures can be induced and advanced as the coupling strength increases. Second, we investigate excitatory and inhibitory coupling functions in a three-compartment model. Our research indicates that the excitatory coupling function can lead to SWDs when all the compartments are initially saturated. In the process of propagation, excitatory coupling also gives rise to SWDs in normal compartments, whereas inhibitory coupling plays a limited role. Finally, we reproduce the above results in a 10-compartment model and verify the robustness against the variation of the number of modules. This work may shed new light on the field of seizure propagation and provide potential dynamical mechanisms.

Bimodular continuous attractor neural networks with static and moving stimuli.

We investigated the dynamical behaviors of bimodular continuous attractor neural networks, each processing a modality of sensory input and interacting with each other. We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory. When one intermodular coupling is excitatory while another is moderately inhibitory, temporally modulated population spikes can be generated. On further increase of the inhibitory coupling, momentary spikes will emerge. In the regime of bump coexistence, bump heights are primarily strengthened by excitatory intermodular couplings, but there is a lesser weakening effect due to a bump being displaced from the direct input. When bimodular networks serve as decoders of multisensory integration, we extend the Bayesian framework to show that excitatory and inhibitory couplings encode attractive and repulsive priors, respectively. At low disparity, the bump positions decode the posterior means in the Bayesian framework, whereas at high disparity, multiple steady states exist. In the regime of multiple steady states, the less stable state can be accessed if the input causing the more stable state arrives after a sufficiently long delay. When one input is moving, the bump in the corresponding module is pinned when the moving stimulus is weak, unpinned at intermediate stimulus strength, and tracks the input at strong stimulus strength, and the stimulus strengths for these transitions increase with the velocity of the moving stimulus. These results are important to understanding multisensory integration of static and dynamic stimuli.

In-phase and anti-phase bursting dynamics and synchronisation scenario in neural network by varying coupling phase.

We have analysed the synchronisation scenario and the rich spatiotemporal patterns in the network of Hindmarsh-Rose neurons under the influence of self, mixed and cross coupling of state variables which are realised by varying coupling phase. We have introduced a coupling matrix in the model to vary coupling phase. The excitatory and inhibitory couplings in the membrane potential induce in-phase and anti-phase bursting dynamics, respectively, in the two coupled system. When the off-diagonal elements of the matrix are zero, the system shows self coupling of the three variables, which helps to attain synchrony. The off-diagonal elements give cross interactions between the variables, which reduces synchrony. The stability of the synchrony attained is analysed using Lyapunov function approach. In our study, we found that self coupling in three variables is sufficient to induce chimera states in non-local coupling. The strength of incoherence and discontinuity measure validates the existence of chimera and multichimera states. The inhibitor self coupling in local interaction induces interesting patterns like Mixed Oscillatory State and clusters. The results may help in understanding the spatiotemporal communications of the brain, within the limitations of the size of the network analysed in this study.

The possible mechanism of direct feedback projections from basal ganglia to cortex in beta oscillations of Parkinson’s disease: A theoretical evidence in the competing resonance model

In this paper, we use a cortex-basal ganglia resonance network to explore the possible Hopf bifurcation mechanism of beta oscillation. Different from traditional viewpoints, this model contains a direct inhibitory projection from the subthalamic nucleus (STN) to the cortex excitatory nuclei (EXN). First, we obtain the oscillation critical conditions of the delay by Hopf bifurcation analysis. Then, we explore the effects of some key parameters on Hopf bifurcation points. Delay is an indispensable factor in this model, oscillations will occur only when delay increases to a certain extent. Interestingly, the amplitude of oscillation increases with an increase in delay, but the trend of frequency is opposite; and heterogeneous delays have different effects on amplitude and frequency. The inhibitory coupling weight in the pathway “STN→EXN” can reduce the firing activation level of cortical circuit, and the increase of coupling weights in the closed loop of the STN and globus pallidus external (GPe) can disinhibition this effect. On the stable boundary curve, the quantitative relationship of coupling weights between the direct and indirect projection from the basal ganglia to cortex is monotonously increasing. An increase in some critical coupling weights in the model, the bidirectional Hopf bifurcations (supercritical and subcritical) occur alternately, which can uniformly explain the origin mechanism of oscillations. In the inhibition-excitation closed loops, the excitatory and inhibitory connection weights have different effects on oscillations, the quantitative relationship of which is monotonically decreasing under Hopf bifurcation critical conditions. In the oscillation region, we observe that the amplitude is the smallest near bifurcation points and gradually increases with the evolution of oscillations. Finally, we find that the direct inhibitory projection from the STN to the EXN is a key pathway for external stimuli to control cortical oscillations. All theoretical analysis results compares well to numerical simulations, we hope that theoretical mechanisms obtained in this paper can inspire experimental research in Parkinson’s disease.

Video-based pooled screening yields improved far-red genetically encoded voltage indicators.

Video-based screening of pooled libraries is a powerful approach for directed evolution of biosensors because it enables selection along multiple dimensions simultaneously from large libraries. Here we develop a screening platform, Photopick, which achieves precise phenotype-activated photoselection over a large field of view (2.3 × 2.3 mm, containing >103 cells, per shot). We used the Photopick platform to evolve archaerhodopsin-derived genetically encoded voltage indicators (GEVIs) with improved signal-to-noise ratio (QuasAr6a) and kinetics (QuasAr6b). These GEVIs gave improved signals in cultured neurons and in live mouse brains. By combining targeted in vivo optogenetic stimulation with high-precision voltage imaging, we characterized inhibitory synaptic coupling between individual cortical NDNF (neuron-derived neurotrophic factor) interneurons, and excitatory electrical synapses between individual hippocampal parvalbumin neurons. The QuasAr6 GEVIs are powerful tools for all-optical electrophysiology and the Photopick approach could be adapted to evolve a broad range of biosensors.

Regulating memristive neuronal dynamical properties via excitatory or inhibitory magnetic field coupling

Regulating memristive neuronal dynamical properties via excitatory or inhibitory magnetic field coupling

A personalizable autonomous neural mass model of epileptic seizures

Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input into the pyramidal cell population, the model dynamics are autonomous. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods based on NMMs.

Activity Patterns of a Two-Timescale Neuronal Ring Model with Voltage-Dependent, Piecewise Smooth Inhibitory Coupling

Activity Patterns of a Two-Timescale Neuronal Ring Model with Voltage-Dependent, Piecewise Smooth Inhibitory Coupling

A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing

BackgroundNeuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients.MethodsTo simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline.ResultsAll but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD.ConclusionsUsing a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD.

Multi-Omics Integrative Analysis Coupled to Control Theory and Computational Simulation of a Genome-Scale metabolic Model Reveal Controlling Biological Switches in Human Astrocytes Under Palmitic Acid-Induced Lipotoxicity

Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatory pathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies, with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β-oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation in metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.