The harmonic N=3 superspace with the even part M4×[SU(3)/U(1)×U(1)] is used to build up an unconstrained off-shell superfield formulation of N=3 super Yang-Mills theory. It is defined in an analytic subspace of this N=3 superspace and is... more
The harmonic N=3 superspace with the even part M4×[SU(3)/U(1)×U(1)] is used to build up an unconstrained off-shell superfield formulation of N=3 super Yang-Mills theory. It is defined in an analytic subspace of this N=3 superspace and is described by three analytic gauge connections entering into the harmonic derivatives. Jumping over the “N=3 barrier” becomes possible due to the presence of
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Early seizure-prediction paradigms were based on detecting electroencephalographic (EEG) features, but recent approaches are based on dynamic systems theory. Methods that attempted to detect predictive features during the preictal period... more
Early seizure-prediction paradigms were based on detecting electroencephalographic (EEG) features, but recent approaches are based on dynamic systems theory. Methods that attempted to detect predictive features during the preictal period proved difficult to validate in practice. Brain systems can display bistability (both normal and epileptic states can coexist), and the transitions between states may be initiated by external or internal dynamic factors. In the former case prediction is impossible, but in the latter case prediction is conceivable, leading to the hypothesis that as seizure onset approaches, the excitability of the underlying neuronal networks tends to increase. This assumption is being explored using not only the ongoing EEG but also active probes, applying appropriate stimuli to brain areas to estimate the excitability of the neuronal populations. Experimental results support this assumption, suggesting that it may be possible to develop paradigms to estimate the risk of an impending transition to an epileptic state.
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Scale space approach provides a tool for studying a given image at all scales simultaneously. Features that can be detected at large scales can provide clues for tracing more detailed information at fine scales. For this ideology to be... more
Scale space approach provides a tool for studying a given image at all scales simultaneously. Features that can be detected at large scales can provide clues for tracing more detailed information at fine scales. For this ideology to be constructive, one needs to investigate which local properties (or local operators) are appropriate for quantifying the desired features and how these properties are changing from scale to scale. In various applications (Kass et al., 1987; Koenderink, 1990), specially those concerned with oriented structures, singular points are of particular interest. Singular points, or simply singularities below, are those points in a grayscale image, where the gradient vector field vanishes (Lindeberg, 1992; Johansen, 1994). Examples of such points in two dimensional images are extrema, saddle points, “monkey” saddles etc. Singular points can be characterized by their order. The order of a singular point is the lowest non-vanishing power in the Taylor expansion of the image field L(x 1, x 2, ..., x d ) around this point. Obviously the order must be greater than 1, since the gradient vector L i = ∂L(x 1, x 2, ..., x d ) is equal to zero in this point.
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In previous studies we showed that autonomous absence seizure generation and termination can be explained by realistic neuronal models eliciting bi-stable dynamics. In these models epileptic seizures are triggered either by external... more
In previous studies we showed that autonomous absence seizure generation and termination can be explained by realistic neuronal models eliciting bi-stable dynamics. In these models epileptic seizures are triggered either by external stimuli (reflex epilepsies) or by internal fluctuations. This scenario predicts exponential distributions of the duration of the seizures and of the inter-ictal intervals. These predictions were validated in rat models of absence epilepsy, as well as in a few human cases. Nonetheless, deviations from the predictions with respect to seizure duration distributions remained unexplained. The objective of the present work is to implement a simple but realistic computational model of a neuronal network including synaptic plasticity and ionic current dynamics and to explore the dynamics of the model with special emphasis on the distributions of seizure and inter-ictal period durations. We use as a basis our lumped model of cortical neuronal circuits. Here we introduce 'activity dependent' parameters, namely post-synaptic voltage-dependent plasticity, as well as a voltage-dependent hyperpolarization-activated current driven by slow and fast activation conductances. We examine the distributions of the durations of the seizure-like model activity and the normal activity, described respectively by the limit cycle and the steady state in the dynamics. We use a parametric γ-distribution fit as a quantifier. Our results show that autonomous, activity-dependent membrane processes can account for experimentally obtained statistical distributions of seizure durations, which were not explainable using the previous model. The activity-dependent membrane processes that display the strongest effect in accounting for these distributions are the hyperpolarization-dependent cationic (I(h)) current and the GABAa plastic dynamics. Plastic synapses (NMDA-type) in the interneuron population show only a minor effect. The inter-ictal statistics retain their consistency with the experimental data and the previous model.
Research Interests: Neuroscience, Algorithms, Biomedical Engineering, Long Term Potentiation, Medicine, and 15 moreNeural Engineering, Neuronal Network, Absence epilepsy, Computer Model, Animals, Neuronal Plasticity, Clinical Sciences, Ictal, Exponential distribution, Parametric Statistics, Experimental Data, Neurosciences, Gamma Distribution, Limit Cycle, and Cortical neurons
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Page 1. Harmonic superspaces of extended supersymmetry. I. The calculus of harmonic variables This article has been downloaded from IOPscience. Please scroll down to see the full text article. 1985 J. Phys. A: Math. Gen. 18 3433 ...
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A phenomenological neural network model with bi-stable oscillatory units is used to model up- and down-states. These states have been observed in vivo in biological neuronal systems and feature oscillatory, limit cycle type of behavior in... more
A phenomenological neural network model with bi-stable oscillatory units is used to model up- and down-states. These states have been observed in vivo in biological neuronal systems and feature oscillatory, limit cycle type of behavior in the up-states. A network is formed by a set of interconnected units. Two different types of network layouts are considered in this work: networks with hierarchical connections and hubs and networks with random connections. The phase coherence between the different units is analyzed and compared to the connectivity distance between nodes. In addition the connectivity degree of a node is associated to the average phase coherence with all other units. The results show that we may be able to identify the set of hubs in a network based on the phase coherence estimates between the different nodes. If the network is very dense or randomly connected, the underlying network structure, however, can not be derived uniquely from the phase coherence.
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A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an... more
A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, allowing interaction between the image primitives. Instead of defining the state of lowest energy as the grouping result, the mean state of the system is taken. In this way, instabilities caused by multiple minima in the energy are being avoided. The means of the spins are taken as the a posteriori probabilities for the grouping result. In the paper, it is shown how the energy functional can be learned from example data. The energy functional is defined in such a way that, in case of no interactions between the elements, the means of the spins equal the a priori local probabilities. The grouping process enables the fusion of the a priori local and bilocal probabilities into the a posteriori probabilities. The method is illustrated both on grouping of line elements in synthetic images and on vessel detection in retinal fundus images.
Research Interests: Computer Science, Algorithms, Artificial Intelligence, Image Processing, Data Mining, and 15 moreImage Analysis, Face, Distributed System, Image segmentation, Image Classification, Computer Simulation, Group Process, Digital Images, Bayesian methods, Cluster Analysis, Information Storage and Retrieval, Image Sensor, Electrical And Electronic Engineering, Energy function, and a priori and a posteriori
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An overview of the pathophysiology of absence seizures is given, focusing on computational modelling where recent neurophysiological experimental evidence is incorporated. The main question addressed is what is the dynamical process by... more
An overview of the pathophysiology of absence seizures is given, focusing on computational modelling where recent neurophysiological experimental evidence is incorporated. The main question addressed is what is the dynamical process by which the same brain can produce sustained bursts of synchronous spike-and-wave discharges (SWDs) and normal, largely desynchronized brain activity, i.e. to display bistability. This generic concept, tested on an updated neural mass computational model of absence seizures, predicts certain properties of the probability distributions of inter-ictal intervals and of the durations of ictal events. A critical analysis of the distributions predicted by the model and those found in reality led to adjustments of the model with respect to the control of the duration of ictal events. Another prediction derived from the bistable dynamics, the possibility of aborting absence seizures by means of counter-controlled electrical stimulation, is also discussed in the light of current experimental studies. Finally the most recent update of the model was carried out to account for the particular properties of the cortical “driver” of SWDs, and the underlying putative role of the persistent Na+ current of cortical neurons in this process.
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Research Interests: Chemistry, Electrophysiology, Medicine, Interneuron, Brain, and 15 moreHumans, Computational Models, Female, Animals, Male, Brain Stimulation, Adult, Local Field Potential, Interneurons, GABAergic Neurons, Hippocampal formation, Gabaergic, Cortical neurons, Medical and Health Sciences, and Electric stimulation
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Rationale. Automated monitoring and alerting for adverse events in patients with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently we explored the relation between... more
Rationale. Automated monitoring and alerting for adverse events in patients with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently we explored the relation between clonic slowing at the end of a convulsive seizure and the occurrence and duration of a subsequent period of post-ictal generalized EEG suppression (PGES). We found that prolonged periods of PGES can be predicted by the amount of progressive increase of inter-clonic intervals (ICI) during the seizure. PGES was previously linked to SUDEP The purpose of the present study is to develop an automated, remote video sensing based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES and which may eventually help preventing sudden unexpected death in epilepsy (SUDEP).
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Objectives. The goal of the present work is to apply a modified version previously developed method for detection of areas involved in epileptogenic circuits and the assessment of the therapeutic effects of Low Frequency Intracranial... more
Objectives. The goal of the present work is to apply a modified version previously developed method for detection of areas involved in epileptogenic circuits and the assessment of the therapeutic effects of Low Frequency Intracranial Theta- burst Electric Neuronal Stimulation (LFITNES). Methods. We use a novel paradigm for stimulation of the brain with 20Hz bi- phasic cyclic alternating polarity (BiCAP)
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Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear.... more
Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear. Computational models provide a powerful tool to explore the underlying mechanisms of such transitions, with the purpose of eventually finding therapeutic interventions for this debilitating condition. In this study, the possibility to postpone seizures elicited by a decrease of inhibition is investigated by using external stimulation in a realistic bistable neuronal model consisting of two interconnected neuronal populations representing pyramidal cells and interneurons. In the simulations, seizures are induced by slowly decreasing the conductivity of GABA[Formula: see text] synaptic channels over time. Since the model is bistable, the system will change state from the initial steady state (SS) to the limit cycle (LS) state because of internal noise, when the inhibition falls below a certain threshold. Several state-independent stimulations paradigms are simulated. Their effectiveness is analyzed for various stimulation frequencies and intensities in combination with periodic and random stimulation sequences. The distributions of the time to first seizure in the presence of stimulation are compared with the situation without stimulation. In addition, stimulation protocols targeted to specific subsystems are applied with the objective of counteracting the baseline shift due to decreased inhibition in the system. Furthermore, an analytical model is used to investigate the effects of random noise. The relation between the strength of random noise stimulation, the control parameter of the system and the transitions between steady state and limit cycle are investigated. The study shows that it is possible to postpone epileptic activity by targeted stimulation in a realistic neuronal model featuring bistability and that it is possible to stop seizures by random noise in an analytical model.
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Research Interests: Computer Science, Algorithms, Artificial Intelligence, Biomedical Engineering, Independent Component Analysis, and 15 moreElectroencephalography, Medicine, Brain, Humans, Computer Simulation, Female, Electromyography, Electroencephalogram, Indexation, Dynamic System, Electrical And Electronic Engineering, Clinical Application, Electromyogram, Dynamic Range, and Child preschool
Research Interests: Neuroscience, Psychology, Nonlinear dynamics, Epilepsy, Electroencephalography, and 14 moreMedicine, Brain, Humans, Computer Simulation, Computer Model, Female, Young Adult, Diagnostic Imaging, Clinical Sciences, Stimulation, Predictive value of tests, State dependence, attractor, and Electric stimulation
Paroxysmal Cerebral Disorder
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Research Interests: Information Systems, Mathematics, Differential Topology, Computer Science, Artificial Intelligence, and 11 moreImage Analysis, Image segmentation, Image Analysis (Mathematics), Scale, Scale Space, Eigenvalues, Critical Point, Electrical And Electronic Engineering, Topological Properties, Computational Method, and Ridges
Research Interests: Engineering, Algorithms, Electrophysiology, Magnetic Resonance Imaging, Epilepsy, and 15 moreLocalization, Electroencephalography, Humans, GABA receptors, Clinical, Benzodiazepines, GAP, Clinical Neurophysiology, Ictal, Lesion, Benzodiazepine, Diazepam, Epilepsy Surgery, Beta Rhythm, and GABA receptor
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Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real‐time video‐based algorithm for detection of nocturnal convulsive seizures in adults with... more
Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real‐time video‐based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video‐sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3‐17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%‐100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0‐0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior‐related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real‐time use.
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We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable... more
We aim to derive fully autonomous seizure suppression paradigms based on reactive control of neuronal dynamics. A previously derived computational model of seizure generation describing collective degrees of freedom and featuring bistable dynamics is used. A novel technique for real-time control of epileptogenicity is introduced. The reactive control reduces practically all seizures in the model. The study indicates which parameters provide the maximal seizure reduction with minimal intervention. An adaptive scheme is proposed that optimizes the stimulation parameters in nonstationary situations.
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In this study, we investigate the correspondence between dynamic patterns of behavior in two types of computational models of neuronal activity. The first model type is the realistic neuronal model; the second model type is the... more
In this study, we investigate the correspondence between dynamic patterns of behavior in two types of computational models of neuronal activity. The first model type is the realistic neuronal model; the second model type is the phenomenological or analytical model. In the simplest model set-up of two interconnected units, we define a parameter space for both types of systems where their behavior is similar. Next we expand the analytical model to two sets of 90 fully interconnected units with some overlap, which can display multi-stable behavior. This system can be in three classes of states: (i) a class consisting of a single resting state, where all units of a set are in steady state, (ii) a class consisting of multiple preserving states, where subsets of the units of a set participate in limit cycle, and (iii) a class consisting of a single saturated state, where all units of a set are recruited in a global limit cycle. In the third and final part of the work, we demonstrate that phase synchronization of units can be detected by a single output unit.
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Research Interests: Neuroscience, Psychology, Psychophysiology, Rhythm, Attention, and 13 moreElectroencephalography, Medicine, Network Dynamics, Humans, Thalamus, Cerebral Cortex, Computer Model, Oscillations, Simulation Study, Alpha Rhythm, Psychology and Cognitive Sciences, Event-Related Desynchronization, and Medical and Health Sciences
Epilepsy is a debilitating neurological condition that affects approximately 1% of the population. In most cases, it is treatable by anti-epileptic drugs (AED) but still about 30% of the patients do not respond sufficiently to medication... more
Epilepsy is a debilitating neurological condition that affects approximately 1% of the population. In most cases, it is treatable by anti-epileptic drugs (AED) but still about 30% of the patients do not respond sufficiently to medication and continue suffering from seizures. Even for those who respond to AED treatment, determining the optimal dose can require lengthy periods of trial, error and adjustments. To address these challenges, the main objective of the present study is to find a biomarker for quantification of the level of responsiveness of people with epilepsy to anti-epileptic drugs on a personal level. We use a computational model of connected bistable units to generate and validate "in silico" a robust biomarker hypothesis. Next we applied the biomarker to EEG from a cohort of patients with known reaction to medication. The model showed that the aggregated functional connectivity is a critically important observable that reflects the state of epilepsy. Applied to the clinical data we were able to derive a criterion for pharmacological responsiveness as well as a paradigm for assessing the optimal medication dose.
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Paroxysmal Cerebral Disorder
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Objective: Photosensitive epilepsy (PSE) is the most common form of reflex epilepsy. Usually, to find out whether a patient is sensitive, he/she is stimulated visually with, e.g. a stroboscopic light stimulus at variable frequency and... more
Objective: Photosensitive epilepsy (PSE) is the most common form of reflex epilepsy. Usually, to find out whether a patient is sensitive, he/she is stimulated visually with, e.g. a stroboscopic light stimulus at variable frequency and intensity until a photo paroxysmal response (PPR) occurs. The research described in this work aims to find whether photosensitivity can be detected without provoking a PPR.
Methods: Twenty-two subjects, 15 with known photosensitivity, were stimulated with visual stimuli that did not provoke a PPR. Using an ‘‘evoked response representation’’, 18 features were analytically derived from EEG signals. Single- and multi-feature classification paradigms were applied to extract those features that separate best subjects with PSE from controls.
Results: Two variables in the ‘‘evoked response representation’’, a frequency term and a goodness of fit term to a particular template, appeared to be best suited to make a prediction about the photosensitivity of a subject.
Conclusions: Evoked responses appear to carry information about potential PSE.
Significance: This result can be useful for screening patients for photosensitivity and it may also help to assess in a quantitative way the effectiveness of medical therapy.
Keywords: Epilepsy; Photosensitivity; Visual evoked potentials; EEG
Methods: Twenty-two subjects, 15 with known photosensitivity, were stimulated with visual stimuli that did not provoke a PPR. Using an ‘‘evoked response representation’’, 18 features were analytically derived from EEG signals. Single- and multi-feature classification paradigms were applied to extract those features that separate best subjects with PSE from controls.
Results: Two variables in the ‘‘evoked response representation’’, a frequency term and a goodness of fit term to a particular template, appeared to be best suited to make a prediction about the photosensitivity of a subject.
Conclusions: Evoked responses appear to carry information about potential PSE.
Significance: This result can be useful for screening patients for photosensitivity and it may also help to assess in a quantitative way the effectiveness of medical therapy.
Keywords: Epilepsy; Photosensitivity; Visual evoked potentials; EEG
Research Interests: Engineering, Electrophysiology, Prediction, Electroencephalography, Adolescent, and 15 moreRepresentation, Humans, Child, Visual Evoked Potential, Female, Classification, Male, Photosensitivity, Clinical Neurophysiology, Middle Aged, Adult, Reflex, photic stimulation, Psychology and Cognitive Sciences, and Medical and Health Sciences
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ABSTRACT In the present work the problem of reconstructing locations of events in an arbitrary scene, or "world" (for example 3D scene in real applications) from an over-complete set of measurements, typically of lower... more
ABSTRACT In the present work the problem of reconstructing locations of events in an arbitrary scene, or "world" (for example 3D scene in real applications) from an over-complete set of measurements, typically of lower dimension each (such as multiple 2D images obtained from different cameras) is addressed. A non-parametric approach based on Principal Component Analysis (PCA) has been developed where a training set of points with known world-coordinates is used to reconstruct the forward and inverse transformation matrices from the scene to the measurement space. The technique does not use any a priori information about the data acquisition geometry or its parameters and can also identify degenerate cases where either the training or the measurement sets are insufficient to perform robust reconstruction. As a first illustration a simple test-set of objects and their 2D images has been presented in order to illustrate the validity of the method. The next realistic application involves a data set of explosions recorded from two high-speed cameras. The method successfully reconstructs the positions of the explosions and in addition their intensities were also quantified.