387 research outputs found
Implementing the cellular mechanisms of synaptic transmission in a neural mass model of the thalamocortical circuitry
A novel direction to existing neural mass modeling technique is proposed where
the commonly used “alpha function” for representing synaptic transmission is
replaced by a kinetic framework of neurotransmitter and receptor dynamics.
The aim is to underpin neuro-transmission dynamics associated with abnormal
brain rhythms commonly observed in neurological and psychiatric disorders. An
existing thalamocortical neural mass model is modified by using the kinetic
Q1 framework for modeling synaptic transmission mediated by glutamatergic and GABA
(gamma-aminobutyric-acid)-ergic receptors. The model output is compared qualitatively
with existing literature on in vitro experimental studies of ferret thalamic slices, as
well as on single-neuron-level model based studies of neuro-receptor and transmitter
dynamics in the thalamocortical tissue. The results are consistent with these studies:
the activation of ligand-gated GABA receptors is essential for generation of spindle
waves in the model, while blocking this pathway leads to low-frequency synchronized
oscillations such as observed in slow-wave sleep; the frequency of spindle oscillations
increase with increased levels of post-synaptic membrane conductance for AMPA
(alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic-acid) receptors, and blocking this
pathway effects a quiescent model output. In terms of computational efficiency, the
simulation time is improved by a factor of 10 compared to a similar neural mass model
based on alpha functions. This implies a dramatic improvement in computational resources
for large-scale network simulation using this model. Thus, the model provides a platform
for correlating high-level brain oscillatory activity with low-level synaptic attributes, and
makes a significant contribution toward advancements in current neural mass modeling
paradigm as a potential computational tool to better the understanding of brain oscillations
in sickness and in health
Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model
Oscillations are ubiquitous phenomena in the animal and human brain. Among
them, the alpha rhythm in human EEG is one of the most prominent examples.
However, its precise mechanisms of generation are still poorly understood. It
was mainly this lack of knowledge that motivated a number of simultaneous
electroencephalography (EEG) – functional magnetic resonance imaging (fMRI)
studies. This approach revealed how oscillatory neuronal signatures such as
the alpha rhythm are paralleled by changes of the blood oxygenation level
dependent (BOLD) signal. Several such studies revealed a negative correlation
between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and
a positive correlation in the thalamus. In this study we explore the potential
generative mechanisms that lead to those observations. We use a bursting
capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide
repertoire of prominent features of local neuronal-population dynamics. We
construct a thalamo-cortical network of coupled SJ3D nodes considering
excitatory and inhibitory directed connections. The model suggests that an
inverse correlation between cortical multi-unit activity, i.e. the firing of
neuronal populations, and narrow band local field potential oscillations in
the alpha band underlies the empirically observed negative correlation between
alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model
suggests that the interplay between tonic and bursting mode in thalamus and
cortex is critical for this relation. This demonstrates how biophysically
meaningful modelling can generate precise and testable hypotheses about the
underpinnings of large-scale neuroimaging signals
Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model
International audienceOscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) – functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations , and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals
A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation
Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12–15 Hz), slow oscillations (<1 Hz) and K-complexes, as well as their distinct temporal relations, and response to auditory stimuli. We show that with the inclusion of detailed calcium currents, the thalamic neural mass model is able to generate different firing modes, and validate the model with EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols
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Anesthetic action on the transmission delay between cortex and thalamus explains the beta-buzz observed under propofol anesthesia
In recent years, more and more surgeries under general anesthesia have been performed with the assistance of electroencephalogram (EEG) monitors. An increase in anesthetic concentration leads to characteristic changes in the power spectra of the EEG. Although tracking the anesthetic-induced changes in EEG rhythms can be employed to estimate the depth of anesthesia, their precise underlying mechanisms are still unknown. A prominent feature in the EEG of some patients is the emergence of a strong power peak in the β–frequency band, which moves to the α–frequency band while increasing the anesthetic concentration. This feature is called the beta-buzz. In the present study, we use a thalamo-cortical neural population feedback model to reproduce observed characteristic features in frontal EEG power obtained experimentally during propofol general anesthesia, such as this beta-buzz. First, we find that the spectral power peak in the α– and δ–frequency ranges depend on the decay rate constant of excitatory and inhibitory synapses, but the anesthetic action on synapses does not explain the beta-buzz. Moreover, considering the action of propofol on the transmission delay between cortex and thalamus, the model reveals that the beta-buzz may result from a prolongation of the transmission delay by increasing propofol concentration. A corresponding relationship between transmission delay and anesthetic blood concentration is derived. Finally, an analytical stability study demonstrates that increasing propofol concentration moves the systems resting state towards its stability threshold
Neural Field Models: A mathematical overview and unifying framework
Rhythmic electrical activity in the brain emerges from regular non-trivial
interactions between millions of neurons. Neurons are intricate cellular
structures that transmit excitatory (or inhibitory) signals to other neurons,
often non-locally, depending on the graded input from other neurons. Often this
requires extensive detail to model mathematically, which poses several issues
in modelling large systems beyond clusters of neurons, such as the whole brain.
Approaching large populations of neurons with interconnected constituent
single-neuron models results in an accumulation of exponentially many
complexities, rendering a realistic simulation that does not permit
mathematical tractability and obfuscates the primary interactions required for
emergent electrodynamical patterns in brain rhythms. A statistical mechanics
approach with non-local interactions may circumvent these issues while
maintaining mathematically tractability. Neural field theory is a
population-level approach to modelling large sections of neural tissue based on
these principles. Herein we provide a review of key stages of the history and
development of neural field theory and contemporary uses of this branch of
mathematical neuroscience. We elucidate a mathematical framework in which
neural field models can be derived, highlighting the many significant inherited
assumptions that exist in the current literature, so that their validity may be
considered in light of further developments in both mathematical and
experimental neuroscience.Comment: 55 pages, 10 figures, 2 table
From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study.
International audienceBoth biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations
Simulating human sleep spindle MEG and EEG from ion channel and circuit level dynamics
Although they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.We present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.The simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.Previous models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales.This multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses
Causal role of thalamic interneurons in brain state transitions: a study using a neural mass model implementing synaptic kinetics
Experimental studies on the Lateral Geniculate Nucleus (LGN) of mammals and rodents show that the inhibitory interneurons (IN) receive around 47.1% of their afferents from the retinal spiking neurons, and constitute around 20–25% of the LGN cell population. However, there is a definite gap in knowledge about the role and impact of IN on thalamocortical dynamics in both experimental and model-based research. We use a neural mass computational model of the LGN with three neural populations viz. IN, thalamocortical relay (TCR), thalamic reticular nucleus (TRN), to study the causality of IN on LGN oscillations and state-transitions. The synaptic information transmission in the model is implemented with kinetic modeling, facilitating the linking of low-level cellular attributes with high-level population dynamics. The model is parameterized and tuned to simulate alpha (8–13 Hz) rhythm that is dominant in both Local Field Potential (LFP) of LGN and electroencephalogram (EEG) of visual cortex in an awake resting state with eyes closed. The results show that: First, the response of the TRN is suppressed in the presence of IN in the circuit; disconnecting the IN from the circuit effects a dramatic change in the model output, displaying high amplitude synchronous oscillations within the alpha band in both TCR and TRN. These observations conform to experimental reports implicating the IN as the primary inhibitory modulator of LGN dynamics in a cognitive state, and that reduced cognition is achieved by suppressing the TRN response. Second, the model validates steady state visually evoked potential response in humans corresponding to periodic input stimuli; however, when the IN is disconnected from the circuit, the output power spectra do not reflect the input frequency. This agrees with experimental reports underpinning the role of IN in efficient retino-geniculate information transmission. Third, a smooth transition from alpha to theta band is observed by progressive decrease of neurotransmitter concentrations in the synaptic clefts; however, the transition is abrupt with removal of the IN circuitry in the model. The results imply a role of IN toward maintaining homeostasis in the LGN by suppressing any instability that may arise due to anomalous synaptic attributes
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