Search

Over the last two decades, the evidence has been growing that in addition to epileptic spikes high frequency oscillations (HFOs) are important biomarkers of epileptogenic tissue. New methods of artificial intelligence such as deep learning neural networks can provide additional tools for automated analysis of EEG. Here we present a Long Short-Term Memory neural network for detection of spikes, ripples and ripples-on-spikes (RonS). We used intracranial EEG (iEEG) from two independent datasets. First dataset (7 patients) was used for network training and testing. The second dataset (5 patients) was used for cross-institutional validation. 1000 events of each class (spike, RonS, ripple and baseline) were selected from the candidates initially found using a novel threshold method. Network training was performed using random selections of 50-500 events (per class) from all patients from the 1 st dataset. This 'global' network was then tested on other events for each patient from both datasets. The network was able to detect events with a good generalisability namely, with total accuracy and specificity for each class exceeding 90% in all cases, and sensitivity less than 86% in only two cases (82.5% for spikes in one patient and 81.9% for ripples in another patient). The deep learning networks can significantly accelerate the analysis of iEEG data and increase their diagnostic value which may improve surgical outcome in patients with localization-related intractable epilepsy.

Anterior and posterior brain areas are involved in the storage and retrieval of semantic representations, but it is not known how these areas dynamically interact during semantic processing. We hypothesized that long-range theta-band coherence would reflect coupling of these areas and examined the oscillatory dynamics of lexical-semantic processing using a semantic priming paradigm with a delayed letter-search task while recording subjects' EEG. Time-frequency analysis revealed facilitation of semantic processing for Related compared to Unrelated conditions, which resulted in a reduced N400 and reduced gamma power from 150 to 450ms. Moreover, we observed greater anterior-posterior theta coherence for Unrelated compared to Related conditions over the time windows 150-425ms and 600-900ms. We suggest that while gamma power reflects activation of local functional networks supporting semantic representations, theta coherence indicates dynamic coupling of anterior and posterior areas for retrieval and post-retrieval processing and possibly an interaction between semantic relatedness and working memory., (Copyright © 2013 Elsevier Inc. All rights reserved.)

As epilepsy remains a refractory condition in about 30% of patients with complex partial seizures, electrical stimulation of the brain has recently shown potential for additive seizure control therapy. Previously, we applied noninvasive transcranial focal stimulation via novel tripolar concentric ring electrodes (TCREs) on the scalp of rats after inducing seizures with pentylenetetrazole (PTZ). We developed a close-loop system to detect seizures and automatically trigger the stimulation and evaluated its effect on the electrographic activity recorded by TCREs in rats. In our previous work the detectors of seizure onset were based on seizure-induced changes in signal power in the frequency range up to 100 Hz, while in this preliminary study we assess the feasibility of recording high frequency oscillations (HFOs) in the range up to 300 Hz noninvasively with scalp TCREs during PTZ-induced seizures. Grand average power spectral density estimate and generalized likelihood ratio tests were used to compare power of electrographic activity at different stages of seizure development in a group of rats (n= 8). The results suggest that TCREs have the ability to record HFOs from the scalp as well as that scalp-recorded HFOs can potentially be used as features for seizure onset detection.

Even though recent studies have suggested that seizures do not occur suddenly and that before a seizure there is a period with an increased probability of seizure occurrence, neurophysiological mechanisms of interictal and pre-seizure states are unknown. The ability of mathematical methods to provide much more sensitive tools for the detection of subtle changes in the electrical activity of the brain gives promise that electrophysiological markers of enhanced seizure susceptibility can be found even during interictal periods when EEG of epilepsy patients often looks 'normal'. Previously, we demonstrated in animals that hippocampal and neocortical gamma-band rhythms (30-100 Hz) intensify long before seizures caused by systemic infusion of kainic acid. Other studies in recent years have also drawn attention to the fast activity (>30 Hz) as a possible marker of epileptogenic tissue. The current study quantified gamma-band activity during interictal periods and seizures in intracranial EEG (iEEG) in 5 patients implanted with subdural grids/intracranial electrodes during their pre-surgical evaluation. In all our patients, we found distinctive (abnormal) bursts of gamma activity with a 3 to 100 fold increase in power at gamma frequencies with respect to selected by clinicians, quiescent, artifact-free, 7-20 min "normal" background (interictal) iEEG epochs 1 to 14 hours prior to seizures. Increases in gamma activity were largest in those channels which later displayed the most intensive electrographic seizure discharges. Moreover, location of gamma-band bursts correlated (with high specificity, 96.4% and sensitivity, 83.8%) with seizure onset zone (SOZ) determined by clinicians. Spatial localization of interictal gamma rhythms within SOZ suggests that the persistent presence of abnormally intensified gamma rhythms in the EEG may be an important tool for focus localization and possibly a determinant of epileptogenesis.

Epilepsy is a neurological disorder that affects approximately one percent of the world population. Anti-epileptic drugs are ineffective in 25~30% of cases. Electrical stimulation to control seizures may be an additive therapy. We applied noninvasive transcutaneous focal electrical stimulation (TFES) via concentric ring electrodes on the scalp of rats after inducing seizures with pentylenetetrazole. We found a significant increase in synchrony within the beta-gamma bands during seizures and that TFES significantly reduced the synchrony of the beta-gamma activity and increased synchrony in the delta band.

Fast (20-100 Hz) rhythms of electrical activity of the brain have been suggested to be important for perception and cognition providing a mechanism for temporal binding of neural activities underlying mental representations. Also, fast rhythms often precede epileptiform discharges in patients and some experimental models. Generalized slow (2-3 Hz) spike activity after systemic kainic acid (KA) in the rat has been shown to be preceded by intense gamma activity. A relationship between the intensified gamma rhythms and the subsequent spike activity was studied during kainate-induced acute epileptogenesis. Power, multiple coherence and phase were analyzed at frequencies 1-100 Hz in the EEG recorded from the hippocampal-neocortical structures of the rat. Gamma rhythms, extremely intense and highly coherent at the onset of discharges, were followed by a slow rhythm of epileptiform spikes/sharp waves. During this spike activity and immediately afterwards, the gamma power and coherence were significantly decreased. These data show an antagonism between gamma rhythms and spike activity and ability of the latter to desynchronize and suppress the former. They are supportive to the hypothesis that epileptiform spike activity may result from the extreme activation of the "anti-binding" mechanism controlling temporal binding at high frequencies. It is suggested that when fast activity is abnormally intensified, "over-binding" with global synchrony of gamma rhythms can occur in the neural networks. It may lead to inadequate synaptic modifications. To prevent this process, epileptiform discharge develops as a protective mechanism suppressing fast activity. This proposal has implications for our understanding of temporal binding in the brain and how its excessive activation may precipitate the development of pathological states., (Copyright 2002 Elsevier Science Inc.)

Fast (beta-gamma band 20-100 Hz) rhythms of electrical activity of the brain have been suggested to play an important role in perception, cognition and consciousness providing temporal binding of neural activities and allowing the formation of mental representations. The recent advances in the concept of temporal binding and their relation to the theory of neural networks (connectionism) are reviewed here as well as some experimental results concerning the intensified gamma rhythms and epilepsy. The hippocampal-neocortical gamma rhythms are extremely intense and hypersynchronous at onset of the epileptiform discharges induced by systemic kainic acid in the rat. Those gamma rhythms are followed by a slow rhythm of epileptiform spikes/sharp waves or spike-wave complexes ('spike-wave' activity). During spike-wave activity, gamma synchronisation is significantly decreased. A novel unifying concept is proposed which relates the associative principle of neural networks to the mechanism of temporal binding at high frequencies. It suggests that for each memory stored in an associative network there is a corresponding quasi-stable state of synchronous oscillation at some frequency within the gamma band. It also suggests that excessive temporal binding ("over-binding") occurs at seizure onset when abnormally intensified and globally synchronous fast activity is often observed. "Over-binding" may cause the undesirable formation of false associations due to inadequate synaptic modifications. To prevent this process, spike-wave discharge develops as an extreme activation of the mechanism capable to desynchronise and eventually suppress fast activity and erase the spurious modes of activity associated with hypersynchronous gamma rhythms. Thus, spike-wave activity is suggested to be the "anti-binding" mechanism. This mechanism is also related to the spikes/sharp waves normally occurring in the brain mostly in sleep. It is qualitatively similar to the "unlearning" mechanism of Crick and Mitchison presumably associated with the PGO spikes of the REM sleep.

This article discusses the role of some pathogenetical factors in the main morphological variants of vascular dementia and their correlation with certain clinical parameters. On the basis of computer-tomographical (CT) studies of 62 patients with dementia, it was possible to distinguish three groups of patients, according to the type of parenchymatous brain lesions: (1) 14 patients with "pure" leukoaraiosis (LA); (2) 23 patients with ischemic foci (IF), and (3) 25 patients with a combination of LA and IF. A comparative study of these groups confirmed the concept that in the development of LA and dementia related to it, a special role is being attributed to such factors as arterial hypertension and aging.

Computed tomography was used to study the prevalence rates of various types of intracranial pathology, hydrocephalus (HDC) and cortical atrophy (CA) in patients with late dementia (LD) and to comparatively assess the informative value of tomographic methods of cerebral morphometry. Computed tomographic data were obtained from 432 patients with LD. Despite the type of dementia, the authors revealed intracranial abnormalities of various etiology in 24.5%, postischemic foci being prevalent. Tumors, arachnoidal cysts, and chronic subdural hematomas were more infrequently diagnosed (in 2.6% of cases). HDC and CA were detected in 81.5 and 82.2% of patients with LD, respectively.

Spectral signs of genetically conditioned convulsibility of rats of the Krushinskiĭ-Molodkina (KM) strain were determined when studying the background electrical brain activity. These signs were: an increase of the delta-activity power in motor and visual cortical zones; considerable increase of the hippocampal potential power; weakening of the caudate nucleus potential power. Spectra of biopotentials being approximated by theoretical spectral function, model parameters simulating physiological characteristics of neural networks were estimated (an inverse problem of modelling). Spectral signs of convulsibility were shown to reflect changes in properties of the neural network. On the basis of calculations it is supposed that genetically determined convulsibility of KM rats results from weakening of inhibitory functions of the caudate nucleus as well as from increased excitability (reactivity) of hippocampal and neocortical neurons.

Selective attention was studied by registering cortical evoked potentials to acoustic stimuli in mental patients and mentally healthy subjects of old age. Disorder of selective attention in the patients was manifested in an impaired ability of the higher portions of the central nervous system to differentiate between essential and non-essential external stimuli as well as in a weakened inhibition of irrelevant information.

The authors investigated the impact of single CT investigation of senile dementia patients. CT was assessed visually. Investigated were 214 patients (23 with senile dementia, 29 with Alzheimer disease, 14 with Pick disease, 90 with vasculogenic dementia, 58 with other forms of dementia). The overall high efficiency of CT was shown in diagnosis of senile dementia. Depending on peculiar clinico-morphological relations different patterns of CT contribution to dementia diagnosis are determined: its verification, detection of concomitant (clinically latent) pathology, establishment of an alternative diagnosis, reduction of the number of diagnostic hypotheses, correction of a diagnostic error.

Desynchronous (low voltage fast activity), synchronous (high voltage slow waves) as well as convulsive brain activities were stimulated by a computer model of neuronal population. Network excitatory and inhibitory elements possessed fundamental dynamic properties of real neurones. Being independent both of the excitability of elements and of external influence efficacy, synchronous (desynchronous) network activity resulted from the increase (decrease) of the average power of "neuronal" interconnections which imitated mutual and recurrent excitation and inhibition. The inhibition efficacy being reduced as compared with excitation, synchronization of elements became intensified. As a consequence, the rhythmic activity amplitude increased and the appearance of self-sustained oscillations simulating convulsive activity was facilitated. The probable mechanism of EEG activation by virtue of the reduction of mutual and recurrent excitation and inhibition efficacy as well as the significance of inhibitory mechanism deficiency for epileptogenesis are discussed.

On the basis of neuronal network a linearized model of power spectrum of the network stable activity was calculated with the approximation of external input by "white" noise. The power spectrum function obtained was used for approximation of the power spectra of the rats motor cortex potentials by the least squares method. As a result the network parameters modelling excitatory and inhibitory cellular and synaptic mechanisms were calculated for two rat strains differing in seizure readiness. As a result of calculations genetically predisposed to seizures KM rats were assumed to differ from unpredisposed to seizures Wistar rats in the increase of efficacy of neuronal interactions (excitatory and inhibitory) as a consequence of the enhanced neuronal "reactivity".

Brain rhythmic activity model on the basis of space uniform neuronal network consisting of excitatory and inhibitory elements was examined. Network activity description by "lump approximation" was compared with simulation model computation. Lump approximation parameters dependence on the properties of neurons and synapses as well as conditions of the transition between network activity types which imitate desynchronous (low voltage fast activity) and synchronous (high voltage slow waves) brain activities were obtained.