Computational Modeling of Information Propagation during the Sleep–Waking Cycle

Non-threatening familiar sounds can go unnoticed during sleep despite the fact that they enter our brain by exciting the auditory nerves. Extracellular cortical recordings in the primary auditory cortex of rodents show that an increase in firing rate in response to pure tones during deep phases of sleep is comparable to those evoked during wakefulness. This result challenges the hypothesis that during sleep cortical responses are weakened through thalamic gating. An alternative explanation comes from the observation that the spatiotemporal spread of the evoked activity by transcranial magnetic stimulation in humans is reduced during non-rapid eye movement (NREM) sleep as compared to the wider propagation to other cortical regions during wakefulness. Thus, cortical responses during NREM sleep remain local and the stimulus only reaches nearby neuronal populations. We aim at understanding how this behavior emerges in the brain as it spontaneously shifts between NREM sleep and wakefulness. To do so, we have used a computational neural-mass model to reproduce the dynamics of the sensory auditory cortex and corresponding local field potentials in these two brain states. Following the synaptic homeostasis hypothesis, an increase in a single parameter, namely the excitatory conductance g¯AMPA, allows us to place the model from NREM sleep into wakefulness. In agreement with the experimental results, the endogenous dynamics during NREM sleep produces a comparable, even higher, response to excitatory inputs to the ones during wakefulness. We have extended the model to two bidirectionally connected cortical columns and have quantified the propagation of an excitatory input as a function of their coupling. We have found that the general increase in all conductances of the cortical excitatory synapses that drive the system from NREM sleep to wakefulness does not boost the effective connectivity between cortical columns. Instead, it is the inter-/intra-conductance ratio of cortical excitatory synapses that should raise to facilitate information propagation across the brain.

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Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

Indian Journal of Signal Processing ◽

10.54105/ijsp.c1008.081321 ◽

2021 ◽

pp. 1-7

Author(s):

Sheikh Md. Rabiul Islam ◽

◽

Md. Shakibul Islam ◽

Keyword(s):

Signal Generation ◽

Neural Mass Model ◽

Pathophysiological Mechanism ◽

Eeg Signal ◽

Eeg Signals ◽

Mass Model ◽

Detailed Model ◽

Simulation Based ◽

Neural Mass ◽

The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

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Controllability Analysis of the Neural Mass Model with Dynamic Parameters

Neural Computation ◽

10.1162/neco_a_00925 ◽

2017 ◽

Vol 29 (2) ◽

pp. 485-501 ◽

Cited By ~ 2

Author(s):

Xian Liu ◽

Jing Gao ◽

Guan Wang ◽

Zhi-Wang Chen

Keyword(s):

Neuronal Model ◽

Condition Numbers ◽

Neural Mass Model ◽

Potential Significance ◽

Mass Model ◽

Neural Mass ◽

Nervous Disorder ◽

Dynamical Parameters ◽

Quantitative Indicators ◽

The Brain

The development of control technology for the brain is of potential significance to the prevention and treatment of neuropsychiatric disorders and the improvement of humans’ mental health. A controllability analysis of the brain is necessary to ensure the feasibility of the brain control. In this letter, we investigate the influences of dynamical parameters on the controllability in the neural mass model by using controllability indices as quantitative indicators. The indices are obtained by computing Lie brackets and condition numbers of the system model. We show how controllability changes with important parameters of our dynamical (neuronal) model. Our results suggest that the underlying dynamical parameters have certain ranges with better controllability. We hope it can play potential roles in therapy for brain nervous disorder disease.

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BINDING AND SEGMENTATION VIA A NEURAL MASS MODEL TRAINED WITH HEBBIAN AND ANTI-HEBBIAN MECHANISMS

International Journal of Neural Systems ◽

10.1142/s0129065712500037 ◽

2012 ◽

Vol 22 (02) ◽

pp. 1250003 ◽

Cited By ~ 6

Author(s):

FILIPPO CONA ◽

MELISSA ZAVAGLIA ◽

MAURO URSINO

Keyword(s):

Pyramidal Neurons ◽

Memory Task ◽

External Input ◽

Gamma Band ◽

Neural Mass Model ◽

Excitatory Synapses ◽

Multiple Objects ◽

Mass Model ◽

Gamma Range ◽

Neural Mass

Synchronization of neural activity in the gamma band, modulated by a slower theta rhythm, is assumed to play a significant role in binding and segmentation of multiple objects. In the present work, a recent neural mass model of a single cortical column is used to analyze the synaptic mechanisms which can warrant synchronization and desynchronization of cortical columns, during an autoassociation memory task. The model considers two distinct layers communicating via feedforward connections. The first layer receives the external input and works as an autoassociative network in the theta band, to recover a previously memorized object from incomplete information. The second realizes segmentation of different objects in the gamma band. To this end, units within both layers are connected with synapses trained on the basis of previous experience to store objects. The main model assumptions are: (i) recovery of incomplete objects is realized by excitatory synapses from pyramidal to pyramidal neurons in the same object; (ii) binding in the gamma range is realized by excitatory synapses from pyramidal neurons to fast inhibitory interneurons in the same object. These synapses (both at points i and ii) have a few ms dynamics and are trained with a Hebbian mechanism. (iii) Segmentation is realized with faster AMPA synapses, with rise times smaller than 1 ms, trained with an anti-Hebbian mechanism. Results show that the model, with the previous assumptions, can correctly reconstruct and segment three simultaneous objects, starting from incomplete knowledge. Segmentation of more objects is possible but requires an increased ratio between the theta and gamma periods.

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Modelling neural entrainment and its persistence: influence of frequency of stimulation and phase at the stimulus offset

10.1101/2021.09.10.459802 ◽

2021 ◽

Author(s):

Mónica Otero ◽

Caroline Lea-Carnall ◽

Pavel Prado ◽

María-José Escobar ◽

Wael El-Deredy

Keyword(s):

Resonance Frequency ◽

Coupling Strength ◽

Oscillatory Behavior ◽

Neural Mass Model ◽

Neural Population ◽

Mass Model ◽

Sensory Stimuli ◽

Cortical Columns ◽

Neural Entrainment ◽

Stimulus Offset

AbstractThe entrainment (synchronization) of brain oscillations to the frequency of sensory stimuli is a key mechanism that shapes perceptual and cognitive processes, such that atypical neural entrainment leads to neuro-psychological deficits.ObjectiveWe investigated the dynamic of neural entrainment. Particular attention was paid to the oscillatory behavior that succeed the end of the stimulation, since the persistence (reverberation) of neural entrainment may condition future sensory representations based on predictions about stimulus rhythmicity.ApproachA modified Jansen-Rit neural mass model of coupled cortical columns generated a time series whose frequency spectrum resembled that of the electroencephalogram. We evaluated spectro-temporal features of entrainment, during and after rhythmic stimulation of different frequencies, as a function of the resonance frequency of the neural population and the coupling strength between cortical columns. We tested if the duration of the entrainment persistence depended on the state of the neural network at the time the stimulus ends.Main ResultsThe entrainment of the column that received the stimulation was maximum when the frequency of the entrainer was within a narrow range around the resonance frequency of the column. When this occurred, entrainment persisted for several cycles after the stimulus terminated, and the propagation of the entrainment to other columns was facilitated. Propagation depended on the resonance frequency of the second column, and the coupling strength between columns. The duration of the persistence of the entrainment depended on the phase of the neural oscillation at the time the entrainer terminated, such that falling phases (from π/2 to 3π/2 in a sine function) led to longer persistence than rising phases (from 0 to π/2 and 3π/2 to 2π).SignificanceThe study bridges between models of neural oscillations and empirical electrophysiology, and provides insights to the use of rhythmic sensory stimulation for neuroenhancement.

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The critical role of spiny stellate cells in seizure onset based on dynamic analysis of a neural mass model

10.1101/2021.07.19.452876 ◽

2021 ◽

Author(s):

Saba Tabatabaee ◽

Fariba Bahrami ◽

Mahyar Janahmadi

Keyword(s):

Bifurcation Analysis ◽

Epileptic Seizure ◽

Critical Role ◽

Stellate Cells ◽

Neural Mass Model ◽

Seizure Onset ◽

Mass Model ◽

Neural Mass ◽

The Brain

Increasing evidence has shown that excitatory neurons in the brain play a significant role in seizure generation. However, spiny stellate cells are cortical excitatory non-pyramidal neurons in the brain which their basic role in seizure occurrence is not well understood. In the present research, we study the critical role of spiny stellate cells or the excitatory interneurons (EI), for the first time, in epileptic seizure generation using an extended neural mass model introduced originally by Taylor and colleagues in 2014. Applying bifurcation analysis on this modified model, we investigated the rich dynamics corresponding to the epileptic seizure onset and transition between interictal and ictal states due to the EI. Our results indicate that the transition is described by a supercritical Hopf bifurcation which shapes the preictal activity in the model and suggests why before seizure onset, the amplitude and frequency of neural activities increase gradually. Moreover, we showed that 1) the altered function of GABAergic and glutamatergic receptors of EI can cause seizure, and 2) the pathway between the thalamic relay nucleus and EI facilitates the transition from interictal to the ictal activity by decreasing the preictal period. Thereafter, we considered both sensory and cortical periodic inputs to drive the model responses to various harmonic stimulations. Our results from the bifurcation analysis of the model suggest that the initial stage of the brain might be the main cause for the transition between interictal and ictal states as the stimulus frequency changes. The extended thalamocortical model shows also that the amplitude jump phenomenon and nonlinear resonance behavior result from the preictal stage of the brain. These results can be considered as a step forward to a deeper understanding of the mechanisms underlying the transition from brain normal activities to epileptic activities.

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Extracranial estimation of neural mass model parameters using the Unscented Kalman Filter

10.1101/171207 ◽

2017 ◽

Author(s):

Lara Escuain-Poole ◽

Jordi Garcia-Ojalvo ◽

Antonio J. Pons

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Neural Mass Model ◽

Model Parameters ◽

Mass Model ◽

Imperfect Data ◽

Non Invasive ◽

Neural Mass ◽

Eeg Recordings ◽

The Brain

AbstractData assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary’s model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra- or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method.Author SummaryTo completely understand brain function, we will need to integrate experimental information into a consistent theoretical framework. Invasive techniques as EcoG recordings, together with models that describe the brain at the mesoscale, provide valuable information about the brain state and its dynamical evolution when combined with techniques coming from control theory, such as the Kalman filter. This method, which is specifically designed to deal with systems with noisy or imperfect data, combines experimental data with theoretical models assuming Bayesian inference. So far, implementations of the Kalman filter have not been suited for non-invasive measures like EEG. Here we attempt to overcome this situation by introducing a model of the head that allows to transfer the intracranial signals produced by a mesoscopic model to the scalp in the form of EEG recordings. Our results show the advantages of using multichannel EEG recordings, which are extended in space and allow to discriminate signals produced by the interaction of coupled columns. The extension of the Kalman method presented here can be expected to expand the applicability of the technique to all situations where EEG recordings are used, including the routine monitoring of illnesses or rehabilitation tasks, brain-computer interface protocols, and transcranial stimulation.

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Cross frequency coupling in next generation inhibitory neural mass models

10.1101/745828 ◽

2019 ◽

Cited By ~ 1

Author(s):

Andrea Ceni ◽

Simona Olmi ◽

Alessandro Torcini ◽

David Angulo-Garcia

Keyword(s):

Information Processing ◽

Cognitive Processes ◽

Neural Mass Model ◽

Neural Population ◽

Mass Model ◽

Collective Behaviors ◽

Neural Mass ◽

Frequency Coupling ◽

Cross Frequency Coupling ◽

The Brain

Coupling among neural rhythms is one of the most important mechanisms at the basis of cognitive processes in the brain. In this study we consider a neural mass model, rigorously obtained from the microscopic dynamics of an inhibitory spiking network with exponential synapses, able to autonomously generate collective oscillations (COs). These oscillations emerge via a super-critical Hopf bifurcation, and their frequencies are controlled by the synaptic time scale, the synaptic coupling and the excitability of the neural population. Furthermore, we show that two inhibitory populations in a master-slave configuration with different synaptic time scales can display various collective dynamical regimes: namely, damped oscillations towards a stable focus, periodic and quasi-periodic oscillations, and chaos. Finally, when bidirectionally coupled the two inhibitory populations can exhibit different types of θ-γ cross-frequency couplings (CFCs): namely, phase-phase and phase-amplitude CFC. The coupling between θ and γ COs is enhanced in presence of a external θ forcing, reminiscent of the type of modulation induced in Hippocampal and Cortex circuits via optogenetic drive.In healthy conditions, the brain’s activity reveals a series of intermingled oscillations, generated by large ensembles of neurons, which provide a functional substrate for information processing. How single neuron properties influence neuronal population dynamics is an unsolved question, whose solution could help in the understanding of the emergent collective behaviors arising during cognitive processes. Here we consider a neural mass model, which reproduces exactly the macroscopic activity of a network of spiking neurons. This mean-field model is employed to shade some light on an important and ubiquitous neural mechanism underlying information processing in the brain: the θ-γ cross-frequency coupling. In particular, we will explore in detail the conditions under which two coupled inhibitory neural populations can generate these functionally relevant coupled rhythms.

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Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink

Indian Journal of Signal Processing ◽

10.35940/ijsp.c1008.081321 ◽

2021 ◽

Vol 1 (3) ◽

pp. 1-7

Author(s):

Sheikh Md. Rabiul Islam ◽

◽

Md. Shakibul Islam ◽

Keyword(s):

Signal Generation ◽

Neural Mass Model ◽

Pathophysiological Mechanism ◽

Eeg Signal ◽

Eeg Signals ◽

Mass Model ◽

Detailed Model ◽

Simulation Based ◽

Neural Mass ◽

The Brain

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Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure

10.1101/312561 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anatoly Buchin ◽

Cliff C. Kerr ◽

Gilles Huberfeld ◽

Richard Miles ◽

Boris Gutkin

Keyword(s):

Resting State ◽

Single Neuron ◽

Field Potential ◽

Epileptic Seizures ◽

Intrinsic Excitability ◽

Neural Mass Model ◽

Mass Model ◽

Pharmacoresistant Epilepsy ◽

Neural Mass ◽

The Brain

AbstractPharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.Significance statementEpileptic seizures are commonly thought to arise from a pathology of inhibition in the brain circuits. Theoretical models aiming to explain epileptic oscillations usually describe the neural activity solely in terms of inhibition and excitation. Single neuron adaptation properties are usually assumed to have only a limited contribution to seizure dynamics. To explore this issue, we developed a novel neural mass model with adaption in the excitatory population. By including adaptation and intrinsic excitability together with inhibition in this model, we were able to account for several experimentally observed properties of seizures, resting state dynamics, and pre-ictal oscillations, leading to improved understanding of epileptic seizures.

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