On the discrimination of patho-physiological states in epilepsy by means of dynamical measures

Raiesdana, Somayeh; Golpayegani, Seyed Mohammad Reza Hashemi; Firoozabadi, Seyed Mohammad; Habibabadi, Jafar Mehvari

doi:10.1016/j.compbiomed.2009.09.001

Cited by 10 publications

(5 citation statements)

References 44 publications

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“…The intensity that best fits our experimental data is located in the upper portion of the effective range for exciting inhibitory interneurons. Since the largest Lyapunov exponent reflects fundamental properties of the current dynamic regime of a system (as evidenced, for example, by its sensitivity to pathological states of the brain, such as epilepsy, see [72], [73], [74]), the fact that our model predicts its dependence on the most important stimulus parameter (frequency) corroborates the validity of the model.…”

Section: Discussionsupporting

confidence: 67%

“…The largest Lyapunov exponent measures the exponential separation or convergence of nearby trajectories. It thereby quantifies the predictability or, at the other extreme, the chaoticity of the behavior of the system and has been demonstrated to be an important marker for pathologically altered brain dynamics, especially in epilepsy [72], [73], [74]. In this way, we show that the NMM is a suitable model for the dynamics of brain resonance phenomena at the cortical level and demonstrate that useful predictions concerning the parameter choice of entrainment experiments can be derived.…”

Section: Introductionmentioning

confidence: 71%

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Modeling Brain Resonance Phenomena Using a Neural Mass Model

et al. 2011

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Stimulation with rhythmic light flicker (photic driving) plays an important role in the diagnosis of schizophrenia, mood disorder, migraine, and epilepsy. In particular, the adjustment of spontaneous brain rhythms to the stimulus frequency (entrainment) is used to assess the functional flexibility of the brain. We aim to gain deeper understanding of the mechanisms underlying this technique and to predict the effects of stimulus frequency and intensity. For this purpose, a modified Jansen and Rit neural mass model (NMM) of a cortical circuit is used. This mean field model has been designed to strike a balance between mathematical simplicity and biological plausibility. We reproduced the entrainment phenomenon observed in EEG during a photic driving experiment. More generally, we demonstrate that such a single area model can already yield very complex dynamics, including chaos, for biologically plausible parameter ranges. We chart the entire parameter space by means of characteristic Lyapunov spectra and Kaplan-Yorke dimension as well as time series and power spectra. Rhythmic and chaotic brain states were found virtually next to each other, such that small parameter changes can give rise to switching from one to another. Strikingly, this characteristic pattern of unpredictability generated by the model was matched to the experimental data with reasonable accuracy. These findings confirm that the NMM is a useful model of brain dynamics during photic driving. In this context, it can be used to study the mechanisms of, for example, perception and epileptic seizure generation. In particular, it enabled us to make predictions regarding the stimulus amplitude in further experiments for improving the entrainment effect.

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Section: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 71%

Modeling Brain Resonance Phenomena Using a Neural Mass Model

et al. 2011

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“…11). Such reduction in entropy levels were previously only reported during seizures with the use of multi-electrode arrays (Jiruska et al 2010;Raiesdana et al 2009). In the present study, when ISO was added, network entropy levels were increased, suggesting an overall decrease in hippocampal CA3-CA1 synchronization (Fig.…”

Section: Fast Optical Imaging Of Spontaneous Bursts In the Hippocampamentioning

confidence: 82%

β-Adrenergic modulation of spontaneous spatiotemporal activity patterns and synchrony in hyperexcitable hippocampal circuits

Hazra

Rosenbaum

Bodmann

et al. 2012

Journal of Neurophysiology

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Hazra A, Rosenbaum R, Bodmann B, Cao S, Josić K, Žiburkus J. ␤-Adrenergic modulation of spontaneous spatiotemporal activity patterns and synchrony in hyperexcitable hippocampal circuits. J Neurophysiol 108: 658 -671, 2012. First published April 11, 2012 doi:10.1152/jn.00708.2011.-A description of healthy and pathological brain dynamics requires an understanding of spatiotemporal patterns of neural activity and characteristics of its propagation between interconnected circuits. However, the structure and modulation of the neural activation maps underlying these patterns and their propagation remain elusive. We investigated effects of ␤-adrenergic receptor (␤-AR) stimulation on the spatiotemporal characteristics of emergent activity in rat hippocampal circuits. Synchronized epileptiform-like activity, such as interictal bursts (IBs) and ictal-like events (ILEs), were evoked by 4-aminopyridine (4-AP), and their dynamics were studied using a combination of electrophysiology and fast voltage-sensitive dye imaging. Dynamic characterization of the spontaneous IBs showed that they originated in dentate gyrus/CA3 border and propagated toward CA1. To determine how ␤-AR modulates spatiotemporal characteristics of the emergent IBs, we used the ␤-AR agonist isoproterenol (ISO). ISO significantly reduced the spatiotemporal extent and propagation velocity of the IBs and significantly altered network activity in the 1-to 20-Hz range. Dual whole cell recordings of the IBs in CA3/CA1 pyramidal cells and optical analysis of those regions showed that ISO application reduced interpyramidal and interregional synchrony during the IBs. In addition, ISO significantly reduced duration not only of the shorter duration IBs but also the prolonged ILEs in 4-AP. To test whether the decrease in ILE duration was model dependent, we used a different hyperexcitability model, zero magnesium (0 Mg 2ϩ ). Prolonged ILEs were readily formed in 0 Mg 2ϩ , and addition of ISO significantly reduced their durations. Taken together, these novel results provide evidence that ␤-AR activation dynamically reshapes the spatiotemporal activity patterns in hyperexcitable circuits by altering network rhythmogenesis, propagation velocity, and intercellular/regional synchronization. Although not a sole effect, VNS in patients and in animals elevates levels of norepinephrine (NE) in the brain. This additional NE is thought to be anticonvulsive (Giorgi et al. 2004;Krahl et al. 1998;Roosevelt et al. 2006;Szot et al. 1999Szot et al. , 2001Weinshenker and Szot 2002).In the brain, noradrenergic fibers densely innervate polymorphic hippocampal, neocortical, and cerebellar tissues (Cox et al. 2008;Giorgi et al. 2004;Grant and Redmond 1981;Milner et al. 2000;Weinshenker and Szot 2002). Actions of NE are mediated by ␣-adrenergic (␣-AR) and ␤-adrenergic receptors (␤-AR) and their respective subtypes (Mueller and Dunwiddie 1983;Mueller et al. 1981Mueller et al. , 1982Weinshenker and Szot 2002). ␣-AR mostly has been shown to be anticonvulsive, but a number of studies have descri...

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“…Importantly, no mathematical assumptions regarding the data and the generating systems are made so this tool is particularly suitable for the analysis of physiological signals which are often non-stationary [ 24 ]. RPs have been applied in the analysis and characterization of EEG signals in epileptic subjects [ 25 , 26 ]. However, to the best of our knowledge, none has applied RQA for the characterization and prediction of seizures using RR series.…”

Section: Introductionmentioning

confidence: 99%

Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis

et al. 2018

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Epilepsy is often associated with modifications in autonomic nervous system, which usually precede the onset of seizures of several minutes. Thus, there is a great interest in identifying these modifications enough time in advance to prevent a dangerous effect and to intervene. In addition, these changes can be a risk factor for epileptic patients and can increase the possibility of death. Notably autonomic changes associated to seizures are highly depended of seizure type, localization and lateralization. The aim of this study was to develop a patient-specific approach to predict seizures using electrocardiogram (ECG) features. Specifically, from the RR series, both time and frequency variables and features obtained by the recurrence quantification analysis were used. The algorithm was applied in a dataset of 15 patients with 38 different types of seizures. A feature selection step, was used to identify those features that were more significant in discriminating preictal and interictal phases. A preictal interval of 15 minutes was selected. A support vector machine (SVM) classifier was then built to classify preictal and interictal phases. First, a classifier was set up to classify preictal and interictal segments of each patient and an average sensibility of 89.06% was obtained, with a number of false positive per hour (FP/h) of 0.41. Then, in those patients who had at least 3 seizures, a double-cross-validation approach was used to predict unseen seizures on the basis of a training on previous ones. The results were quite variable according to seizure type, achieving the best performance in patients with more stereotypical seizure. The results of the proposed approach show that it is feasible to predict seizure in advance, considering patient-specific, and possible seizure specific, characteristics.

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On the discrimination of patho-physiological states in epilepsy by means of dynamical measures

Cited by 10 publications

References 44 publications

Modeling Brain Resonance Phenomena Using a Neural Mass Model

Modeling Brain Resonance Phenomena Using a Neural Mass Model

β-Adrenergic modulation of spontaneous spatiotemporal activity patterns and synchrony in hyperexcitable hippocampal circuits

Patient-specific seizure prediction based on heart rate variability and recurrence quantification analysis

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