Florian Mormann

Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients

Abstract We apply the concept of phase synchronization of chaotic and/or noisy systems and the statistical distribution of the relative instantaneous phases to electroencephalograms (EEGs) recorded from patients with temporal lobe epilepsy. Using the mean phase coherence as a statistical measure for phase synchronization, we observe characteristic spatial and temporal shifts in synchronization that appear to be strongly related to pathological activity. In particular, we observe distinct differences in the degree of synchronization between recordings from seizure-free intervals and those before an impending seizure, indicating an altered state of brain dynamics prior to seizure activity.

On the predictability of epileptic seizures

OBJECTIVE An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches. METHODS We compared 30 measures in terms of their ability to distinguish between the interictal period and the pre-seizure period. After completely analyzing continuous inctracranial multi-channel recordings from five patients lasting over days, we used ROC curves to distinguish between the amplitude distributions of interictal and preictal time profiles calculated for the respective measures. We compared different evaluation schemes including channelwise and seizurewise analysis plus constant and adaptive reference levels. Particular emphasis was placed on statistical validity and significance. RESULTS Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhibited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 min before seizures. Linear measures were found to perform similar or better than non-linear measures. CONCLUSIONS Results provide statistically significant evidence for the existence of a preictal state. Based on our findings, the most promising approach for prospective seizure anticipation could be a combination of bivariate and univariate measures. SIGNIFICANCE Many measures reported capable of seizure prediction in earlier studies are found to be insignificant in performance, which underlines the need for statistical validation in this field.

Epileptic seizures are preceded by a decrease in synchronization

The exact mechanisms leading to the occurrence of epileptic seizures in humans are still poorly understood. It is widely accepted, however, that the process of seizure generation is closely associated with an abnormal synchronization of neurons. In order to investigate this process, we here measure phase synchronization between different regions of the brain using intracranial EEG recordings. Based on our preliminary finding of a preictal drop in synchronization, we investigate whether this phenomenon can be used as a sensitive and specific criterion to characterize a preseizure state and to distinguish this state from the interictal interval. Applying an automated technique for detecting decreased synchronization to EEG recordings from a group of 18 patients with focal epilepsy comprising a total of 117 h, we observe a characteristic decrease in synchronization prior to 26 out of 32 analyzed seizures at a very high specificity as tested on interictal recordings. The duration of this preictal state is found to range from several minutes up to a few hours. Investigation of the spatial distribution of preictal desynchronization indicates that the process of seizure generation in focal epilepsy is not necessarily confined to the focus itself but may instead involve more distant, even contralateral areas of the brain. Finally, we demonstrate an intrahemispheric asymmetry in the spatial dynamics of preictal desynchronization that is found in the majority of seizures and appears to be an immanent part of the mechanisms underlying the initiation of seizures in humans.

Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention.

Several recent studies emphasize the high value of nonlinear EEG analysis particularly for improved characterization of epileptic brain states. In this review the authors report their work to increase insight into the spatial and temporal dynamics of the epileptogenic process. Specifically, they discuss possibilities for seizure anticipation, which is one of the most challenging aspects of epileptology. Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available regarding how, when, or why a seizure occurs. Nonlinear EEG analysis now provides strong evidence that the interictal-ictal state transition is not an abrupt phenomenon. Rather, findings indicate that it is indeed possible to detect a preseizure phase. The unequivocal definition of such a state with a sufficient length would enable investigations of basic mechanisms leading to seizure initiation in humans, and development of adequate seizure prevention strategies.Summary: Several recent studies emphasize the high value of nonlinear EEG analysis particularly for improved characterization of epileptic brain states. In this review the authors report their work to increase insight into the spatial and temporal dynamics of the epileptogenic process. Specifically, they discuss possibilities for seizure anticipation, which is one of the most challenging aspects of epileptology. Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available regarding how, when, or why a seizure occurs. Nonlinear EEG analysis now provides strong evidence that the interictal‐ictal state transition is not an abrupt phenomenon. Rather, findings indicate that it is indeed possible to detect a preseizure phase. The unequivocal definition of such a state with a sufficient length would enable investigations of basic mechanisms leading to seizure initiation in humans, and development of adequate seizure prevention strategies.

Sustained Neural Activity Patterns during Working Memory in the Human Medial Temporal Lobe

In contrast to classical findings that the medial temporal lobe (MTL) specifically underlies long-term memory, previous data suggest that MTL structures may also contribute to working memory (WM). However, the neural mechanisms by which the MTL supports WM have remained unknown. Here, we exploit intracranial EEG to identify WM-specific sustained activity patterns with the highest temporal and spatial resolution currently available in humans. Using a serial Sternberg paradigm, we found a positive shift of the direct current (DC) potential and a long-lasting decrease in MTL gamma-band activity during maintenance of a single item, reflective of a sustained reduction in neural activity. Maintenance of an increasing number of items elicited an incrementally negative shift of the DC potential and an increase in MTL gamma-band activity. In addition, the paradigm was conducted in healthy control subjects using functional magnetic resonance imaging. This confirmed that our results were not caused by pathological processes within the MTL, and that this region was indeed specifically activated during the task. Our results thus provide direct evidence for sustained neural activity patterns during working memory maintenance in the MTL, and show that these patterns depend on WM load.

Seizure prediction by nonlinear EEG analysis

Attempting to increase insight into the spatio-temporal dynamics of the epileptogenic process to address one of the greatest challenges in epileptology. In the field of EEG analysis the search for the hidden information predictive of an impending seizure has a long history. The nonlinear EEG analysis techniques we have used in principle allow one to define a pre-ictal state and to characterize different temporal and spatial aspects of this state. The results obtained so far emphasize the high value of nonlinear EEG analysis techniques for the detection of a long-lasting pre-ictal state. Once given a sufficient sensitivity and specificity of seizure prediction techniques, more extensive clinical studies on a larger population of patients, either at home or in a clinical setting, can be envisaged.

Sepsis causes neuroinflammation and concomitant decrease of cerebral metabolism

BackgroundSeptic encephalopathy is a severe brain dysfunction caused by systemic inflammation in the absence of direct brain infection. Changes in cerebral blood flow, release of inflammatory molecules and metabolic alterations contribute to neuronal dysfunction and cell death.MethodsTo investigate the relation of electrophysiological, metabolic and morphological changes caused by SE, we simultaneously assessed systemic circulation, regional cerebral blood flow and cortical electroencephalography in rats exposed to bacterial lipopolysaccharide. Additionally, cerebral glucose uptake, astro- and microglial activation as well as changes of inflammatory gene transcription were examined by small animal PET using [18F]FDG, immunohistochemistry, and real time PCR.ResultsWhile the systemic hemodynamic did not change significantly, regional cerebral blood flow was decreased in the cortex paralleled by a decrease of alpha activity of the electroencephalography. Cerebral glucose uptake was reduced in all analyzed neocortical areas, but preserved in the caudate nucleus, the hippocampus and the thalamus. Sepsis enhanced the transcription of several pro- and anti-inflammatory cytokines and chemokines including tumor necrosis factor alpha, interleukin-1 beta, transforming growth factor beta, and monocot chemoattractant protein 1 in the cerebrum. Regional analysis of different brain regions revealed an increase in ED1-positive microglia in the cortex, while total and neuronal cell counts decreased in the cortex and the hippocampus.ConclusionTogether, the present study highlights the complexity of sepsis induced early impairment of neuronal metabolism and activity. Since our model uses techniques that determine parameters relevant to the clinical setting, it might be a useful tool to develop brain specific therapeutic strategies for human septic encephalopathy.

Neural correlates of consciousness

Consciousness is a puzzling, state-dependent property of certain types of complex, biological, adaptive, and highly interconnected systems. A science of consciousness must strive to explain the exact relationship between phenomenal, mental states and brain states. This is the heart of the classical mind-body problem: What is the nature of the relationship between the immaterial, conscious mind and its physical basis in the electrochemical interactions in the body? Brain scientists are exploiting a number of empirical approaches to shed light on the neural basis of consciousness. This article reviews these approaches and summarizes what has been learnt.

On-line, voluntary control of human temporal lobe neurons.

Daily life continually confronts us with an exuberance of external, sensory stimuli competing with a rich stream of internal deliberations, plans and ruminations. The brain must select one or more of these for further processing. How this competition is resolved across multiple sensory and cognitive regions is not known; nor is it clear how internal thoughts and attention regulate this competition. Recording from single neurons in patients implanted with intracranial electrodes for clinical reasons, here we demonstrate that humans can regulate the activity of their neurons in the medial temporal lobe (MTL) to alter the outcome of the contest between external images and their internal representation. Subjects looked at a hybrid superposition of two images representing familiar individuals, landmarks, objects or animals and had to enhance one image at the expense of the other, competing one. Simultaneously, the spiking activity of their MTL neurons in different subregions and hemispheres was decoded in real time to control the content of the hybrid. Subjects reliably regulated, often on the first trial, the firing rate of their neurons, increasing the rate of some while simultaneously decreasing the rate of others. They did so by focusing onto one image, which gradually became clearer on the computer screen in front of their eyes, and thereby overriding sensory input. On the basis of the firing of these MTL neurons, the dynamics of the competition between visual images in the subject’s mind was visualized on an external display.

Memory consolidation by replay of stimulus-specific neural activity.

Memory consolidation transforms initially labile memory traces into more stable representations. One putative mechanism for consolidation is the reactivation of memory traces after their initial encoding during subsequent sleep or waking state. However, it is still unknown whether consolidation of individual memory contents relies on reactivation of stimulus-specific neural representations in humans. Investigating stimulus-specific representations in humans is particularly difficult, but potentially feasible using multivariate pattern classification analysis (MVPA). Here, we show in healthy human participants that stimulus-specific activation patterns can indeed be identified with MVPA, that these patterns reoccur spontaneously during postlearning resting periods and sleep, and that the frequency of reactivation predicts subsequent memory for individual items. We conducted a paired-associate learning task with items and spatial positions and extracted stimulus-specific activity patterns by MVPA in a simultaneous electroencephalography and functional magnetic resonance imaging (fMRI) study. As a first step, we investigated the amount of fMRI volumes during rest that resembled either one of the items shown before or one of the items shown as a control after the resting period. Reactivations during both awake resting state and sleep predicted subsequent memory. These data are first evidence that spontaneous reactivation of stimulus-specific activity patterns during resting state can be investigated using MVPA. They show that reactivation occurs in humans and is behaviorally relevant for stabilizing memory traces against interference. They move beyond previous studies because replay was investigated on the level of individual stimuli and because reactivations were not evoked by sensory cues but occurred spontaneously.