283 research outputs found
Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy
Patients with idiopathic generalised epilepsy (IGE) typically have normal
conventional magnetic resonance imaging (MRI), hence MRI based diagnosis is
challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are
unclear and their relation to the pathomechanisms of epileptogenesis is poorly
understood. In this study, we applied connectometry, an advanced quantitative
neuroimaging technique for investigating localised changes in white-matter
tissue. Analysing white matter structures of 32 subjects we incorporated our
findings in a computational model of seizure dynamics to suggest a plausible
mechanism of epileptogenesis. Patients with IGE have significant bilateral
alterations in major white-matter fascicles. In the cingulum, fornix, and
superior longitudinal fasciculus, tract integrity is compromised, whereas in
specific parts of tracts between thalamus and the precentral gyrus, tract
integrity is enhanced in patients. Combining these alterations in a logistic
regression model, we computed the decision boundary that discriminated patients
and controls. The computational model, informed with the findings on the tract
abnormalities, specifically highlighted the importance of enhanced
cortico-reticular connections along with impaired cortico-cortical connections
in inducing pathological seizure-like dynamics. We emphasise taking
directionality of brain connectivity into consideration towards understanding
the pathological mechanisms; this is possible by combining neuroimaging and
computational modelling. Our imaging evidence of structural alterations suggest
the loss of cortico-cortical and enhancement of cortico-thalamic fibre
integrity in IGE. We further suggest that impaired connectivity from cortical
regions to the thalamic reticular nucleus offers a therapeutic target for
selectively modifying the brain circuit for reversing the mechanisms leading to
epileptogenesis
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
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The spatiotemporal dynamics of human focal seizures
Spontaneous human focal seizures can present with a plethora of behavioral manifestations that vary according to the affected cortical regions; however, several key features have been consistently observed. During my doctoral studies, I applied both theoretical and experimental methods to study mechanisms underpinning these consistently seen dynamics. I first analyzed human intracranial EEG recordings, describing statistical methods for measuring their electrophysiological signatures. I next proposed several neurophysiological hypotheses that could explain seizure dynamics and verified them in rodent seizure models. Finally, a computational model was developed, successfully explaining how the complex spatiotemporal evolution of focal seizures emerges from simple neurophysiological principles.
In Chapter 1, the long-standing behavioral manifestations and the most up-to-date electrophysiology findings are reviewed. This section details the inspiration for the studies reported in the subsequent chapters.
In Chapter 2, I describe several statistical methods for estimating traveling wave velocities. I show most ictal discharges can be described as traveling waves whose velocities contain rich information about the stages of seizure evolution. I compare performance of various statistical methods and propose a robust approach to boost the quality of each method’s estimation results.
In Chapter 3, I show how inhibition modulates seizure propagation patterns. Surround inhibition spatially restrains focal seizures and masks excitatory projections of ictal activities. When compromised, two patterns of seizure propagation emerge according to the position of inhibition defects relative to the ictal focus. I show that two distant ictal foci can communicate via physiological connectivity without any chronic rewiring processes – confirming the existence of long-range propagation pathways that could lead to epileptic network formation.
In Chapter 4, I show that thalamic inputs might be necessary for interictal epileptiform discharges (IEDs). The relative positions between IEDs and ictal foci indicate that surround inhibition, shown in the previous chapter, can be exhausted by repetitive exposure to ictal projections.
In Chapter 5, I propose a neural network model that can explain both long-standing behavioral observations of seizures and account for the most up-to-date electrophysiological recordings of spontaneous human focal seizures. The model relies on few assumptions, all of which are proved or supported in earlier chapters of this thesis. The model explains phasic evolution of seizure dynamics – how the commonly observed patterns arise from simple neurophysiological principles, as well as seizure onset subtypes, traveling wave directions and speeds. The model also predicts how spontaneous seizures might arise from synaptic plasticity. The chapter ends with a discussion of the model’s implications and future work.
The thesis is organized in a way that each chapter can be read independently, with Chapter 5 summarizing the central theory spanning the whole study. Each chapter is also tightly linked to a clinically relevant question. In sum, the dissertation’s goal is to provide an in-principle understanding of focal seizure dynamics. With rapid advancement of clinical and experimental tools, I believe this work provides a roadmap for future therapies for epilepsy patients
Experimental treatment options in absence epilepsy
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182124.pdf (preprint version ) (Open Access)Background: The benign character of absence epilepsy compared to other genetic generalized epilepsy syndromes has often hampered the search for new treatment options. Absence epilepsy is most often treated with ethosuximide or valproic acid. However, both drugs are not always well tolerated or fail, and seizure freedom for a larger proportion of patients remains to be achieved. The availability of genuine animal models of epilepsy does allow to search for new treatment options not only for absence epilepsy perse but also for other genetic - previously called idiopathic - forms of epilepsy. The recent discovery of a highly excitable cortical zone in these models is considered as a new therapeutic target area. Methods: Here, we provide an overview regarding the search for new therapeutical options as has been investigated in the genetic rodent models (mainly WAG/Rij and GAERS) including drugs and whether antiepileptogenesis can be achieved, various types of electrical and optogenetical invasive stimulations, different types of non-invasive stimulation and finally whether absence seizures can be predicted and prevented. Results: Many factors determine either the cortical and or thalamic excitability or the interaction between cortex and thalamus and offer new possibilities for new anti-absence drugs, among others metabotropic glutamatergic positive and negative allosteric modulators. The inhibition of epileptogenesis by various drugs with its widespread consequences seems feasible, although its mechanisms remain obscure and seems different from the anti-absence action. Surgical intervention on the cortical zone initiating seizures, either with radiosurgery using synchrotron-generated microbeams, or ablation techniques might reduce spike-and-wave discharges in the rodent models. High frequency electrical subcortical or cortical stimulation might be a good way to abort ongoing spike-and-wave discharges. In addition, possibilities for prevention with real-time EEG analyses in combination with electrical stimulation could also be a way to fully control these seizures. Conclusion: Although it is obvious that some of these treatment possibilities will not be used for absence epilepsy and/or need to be further developed, all can be considered as proof of principle and provide clear directives for further developments
Metabifurcation analysis of a mean field model of the cortex
Mean field models (MFMs) of cortical tissue incorporate salient features of
neural masses to model activity at the population level. One of the common
aspects of MFM descriptions is the presence of a high dimensional parameter
space capturing neurobiological attributes relevant to brain dynamics. We study
the physiological parameter space of a MFM of electrocortical activity and
discover robust correlations between physiological attributes of the model
cortex and its dynamical features. These correlations are revealed by the study
of bifurcation plots, which show that the model responses to changes in
inhibition belong to two families. After investigating and characterizing
these, we discuss their essential differences in terms of four important
aspects: power responses with respect to the modeled action of anesthetics,
reaction to exogenous stimuli, distribution of model parameters and oscillatory
repertoires when inhibition is enhanced. Furthermore, while the complexity of
sustained periodic orbits differs significantly between families, we are able
to show how metamorphoses between the families can be brought about by
exogenous stimuli. We unveil links between measurable physiological attributes
of the brain and dynamical patterns that are not accessible by linear methods.
They emerge when the parameter space is partitioned according to bifurcation
responses. This partitioning cannot be achieved by the investigation of only a
small number of parameter sets, but is the result of an automated bifurcation
analysis of a representative sample of 73,454 physiologically admissible sets.
Our approach generalizes straightforwardly and is well suited to probing the
dynamics of other models with large and complex parameter spaces
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