7,773 research outputs found
Understanding Epileptiform After-Discharges as Rhythmic Oscillatory Transients
Electro-cortical activity in patients with epilepsy may show abnormal
rhythmic transients in response to stimulation. Even when using the same
stimulation parameters in the same patient, wide variability in the duration of
transient response has been reported. These transients have long been
considered important for the mapping of the excitability levels in the
epileptic brain but their dynamic mechanism is still not well understood.
To understand the occurrence of abnormal transients dynamically, we use a
thalamo-cortical neural population model of epileptic spike-wave activity and
study the interaction between slow and fast subsystems.
In a reduced version of the thalamo-cortical model, slow wave oscillations
arise from a fold of cycles (FoC) bifurcation. This marks the onset of a region
of bistability between a high amplitude oscillatory rhythm and the background
state. In vicinity of the bistability in parameter space, the model has
excitable dynamics, showing prolonged rhythmic transients in response to
suprathreshold pulse stimulation. We analyse the state space geometry of the
bistable and excitable states, and find that the rhythmic transient arises when
the impending FoC bifurcation deforms the state space and creates an area of
locally reduced attraction to the fixed point. This area essentially allows
trajectories to dwell there before escaping to the stable steady state, thus
creating rhythmic transients. In the full thalamo-cortical model, we find a
similar FoC bifurcation structure.
Based on the analysis, we propose an explanation of why stimulation induced
epileptiform activity may vary between trials, and predict how the variability
could be related to ongoing oscillatory background activity.Comment: http://journal.frontiersin.org/article/10.3389/fncom.2017.00025/ful
Multimodal imaging of human brain activity: rational, biophysical aspects and modes of integration
Until relatively recently the vast majority of imaging and electrophysiological studies of human brain activity have relied on single-modality measurements usually correlated with readily observable or experimentally modified behavioural or brain state patterns. Multi-modal imaging is the concept of bringing together observations or measurements from different instruments. We discuss the aims of multi-modal imaging and the ways in which it can be accomplished using representative applications. Given the importance of haemodynamic and electrophysiological signals in current multi-modal imaging applications, we also review some of the basic physiology relevant to understanding their relationship
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
Network perspectives on epilepsy using EEG/MEG source connectivity
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience
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
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