Neuronal complexity loss in interictal EEG recorded with foramen ovale electrodes predicts side of primary epileptogenic area in temporal lobe epilepsy: a replication study

Neuronal complexity tracks changes of epileptic activity and identifies epilepsy patients independent of interictal epileptiform discharges

OBJECTIVE

To date, the identification of objective biomarkers of neural epileptic activity (EA) remains challenging. We therefore investigated whether neuronal complexity could serve as an interictal electroencephalographic measure of EA, independent of interictal epileptiform discharges (IEDs). By tapering anti-seizure medication (ASM) during video-EEG (electroencephalography) monitoring (VEM), we studied whether changes in neuronal complexity could reliably indicate the increase in EA and identify patients with epilepsy.

METHODS

The study included 27 patients with unilateral mesial temporal lobe epilepsy (TLE) and 24 control patients with non-epileptic episodes (NEEs) only, each undergoing ASM reduction during VEM. Thirteen additional patients undergoing intracranial recordings during VEM were included to study the relation of surface EEG complexity to intracranial IED. Neuronal complexity was quantified using sample entropy. Delta power served as a control parameter. Receiver-operating characteristic (ROC) analysis was used to evaluate diagnostic performance.

RESULTS

As ASM was reduced, patients with epilepsy showed a significant decrease in neuronal complexity over consecutive days (p = .0008). In contrast, patients with NEE showed no significant change in neuronal complexity (p = .78). Delta power in contrast increased and did not differ significantly between patients with TLE and patients with NEE (p = 1). ROC analysis demonstrated that neuronal complexity effectively distinguished between patients with epilepsy and patients with NEE (area under the curve [AUC] = .76), whereas delta power performed at chance level (AUC = .5). Analysis of simultaneously recorded surface and intracranial EEG showed that hippocampal IEDs are followed by an increase in surface EEG delta power (p = 1.8 × 10-18) without any significant change in complexity (p = .39).

SIGNIFICANCE

An increase in EA caused by ASM reduction resulted in a loss of neuronal complexity in surface EEG recordings of patients with epilepsy, independent of IEDs. These findings suggest that neuronal complexity could serve as a potential biomarker to differentiate between epilepsy patients and those with NEEs only. This holds promise for improving the clinical evaluation of EA in epilepsy, addressing the limitations of seizure frequency and IED identification.

Seizure Onset Zone Lateralization Using a Non-linear Analysis of Micro vs. Macro Electroencephalographic Recordings During Seizure-Free Stages of the Sleep-Wake Cycle From Epilepsy Patients

The application of non-linear signal analysis techniques to biomedical data is key to improve our knowledge about complex physiological and pathological processes. In particular, the use of non-linear techniques to study electroencephalographic (EEG) recordings can provide an advanced characterization of brain dynamics. In epilepsy these dynamics are altered at different spatial scales of neuronal organization. We therefore apply non-linear signal analysis to EEG recordings from epilepsy patients derived with intracranial hybrid electrodes, which are composed of classical macro contacts and micro wires. Thereby, these electrodes record EEG at two different spatial scales. Our aim is to test the degree to which the analysis of the EEG recorded at these different scales allows us to characterize the neuronal dynamics affected by epilepsy. For this purpose, we retrospectively analyzed long-term recordings performed during five nights in three patients during which no seizures took place. As a benchmark we used the accuracy with which this analysis allows determining the hemisphere that contains the seizure onset zone, which is the brain area where clinical seizures originate. We applied the surrogate-corrected non-linear predictability score (ψ), a non-linear signal analysis technique which was shown previously to be useful for the lateralization of the seizure onset zone from classical intracranial EEG macro contact recordings. Higher values of ψ were found predominantly for signals recorded from the hemisphere containing the seizure onset zone as compared to signals recorded from the opposite hemisphere. These differences were found not only for the EEG signals recorded with macro contacts, but also for those recorded with micro wires. In conclusion, the information obtained from the analysis of classical macro EEG contacts can be complemented by the one of micro wire EEG recordings. This combined approach may therefore help to further improve the degree to which quantitative EEG analysis can contribute to the diagnostics in epilepsy patients.

Impact of type of intracranial EEG sensors on link strengths of evolving functional brain networks

Objective and Approach: Investigating properties of evolving functional brain networks has become a valuable tool to characterize the complex dynamics of the epileptic brain. Such networks are usually derived from electroencephalograms (EEG) recorded with sensors implanted chronically into deeper structures of the brain and/or placed onto the cortex. It is still unclear, however, whether the use of different sensors for an identification of network nodes affects properties of functional brain networks. We address this question by investigating properties of links of such networks that we characterize by assessing interactions in multi-sensor, multi-day EEG data recorded from 49 epilepsy patients during presurgical evaluation. These data allow us to study the impact of different types of sensors together with the impact of various physiologic and pathophysiologic activities on the properties of links.

MAIN RESULTS

We observe that different types of sensors differently impact on spatial means and temporal fluctuations of link strengths. Moreover, the impact depends on the relative anatomical location of sensors with respect to location and extent of sources of the prevailing activities.

SIGNIFICANCE

Type and location of sensors should be considered when constructing networks.

Automated Seizure Onset Zone Approximation Based on Nonharmonic High-Frequency Oscillations in Human Interictal Intracranial EEGs

A novel automated algorithm is proposed to approximate the seizure onset zone (SOZ), while providing reproducible output. The SOZ, a surrogate marker for the epileptogenic zone (EZ), was approximated from intracranial electroencephalograms (iEEG) of nine people with temporal lobe epilepsy (TLE), using three methods: (1) Total ripple length (TRL): Manually segmented high-frequency oscillations, (2) Rippleness (R): Area under the curve (AUC) of the autocorrelation functions envelope, and (3) Autoregressive model residual variation (ARR, novel algorithm): Time-variation of residuals from autoregressive models of iEEG windows. TRL, R, and ARR results were compared in terms of separability, using Kolmogorov–Smirnov tests, and performance, using receiver operating characteristic (ROC) curves, to the gold standard for SOZ delineation: visual observation of ictal video-iEEGs. TRL, R, and ARR can distinguish signals from iEEG channels located within the SOZ from those outside it (p < 0.01). The ROC AUC was 0.82 for ARR, while it was 0.79 for TRL, and 0.64 for R. ARR outperforms TRL and R, and may be applied to identify channels in the SOZ automatically in interictal iEEGs of people with TLE. ARR, interpreted as evidence for nonharmonicity of high-frequency EEG components, could provide a new way to delineate the EZ, thus contributing to presurgical workup.

Functional connectivity estimated from intracranial EEG predicts surgical outcome in intractable temporal lobe epilepsy

This project aimed to determine if a correlation-based measure of functional connectivity can identify epileptogenic zones from intracranial EEG signals, as well as to investigate the prognostic significance of such a measure on seizure outcome following temporal lobe lobectomy. To this end, we retrospectively analyzed 23 adult patients with intractable temporal lobe epilepsy (TLE) who underwent an invasive stereo-EEG (SEEG) evaluation between January 2009 year and January 2012. A follow-up of at least one year was required. The primary outcome measure was complete seizure-freedom at last follow-up. Functional connectivity between two areas in the temporal lobe that were sampled by two SEEG electrode contacts was defined as Pearson's correlation coefficient of interictal activity between those areas. SEEG signals were filtered between 5 and 50 Hz prior to computing this correlation. The mean and standard deviation of the off diagonal elements in the connectivity matrix were also calculated. Analysis of the mean and standard deviation of the functional connections for each patient reveals that 90% of the patients who had weak and homogenous connections were seizure free one year after temporal lobectomy, whereas 85% of the patients who had stronger and more heterogeneous connections within the temporal lobe had recurrence of seizures. This suggests that temporal lobectomy is ineffective in preventing seizure recurrence for patients in whom the temporal lobe is characterized by weakly connected, homogenous networks. This pilot study shows promising potential of a simple measure of functional brain connectivity to identify epileptogenicity and predict the outcome of epilepsy surgery.

Impaired mesial synchronization in temporal lobe epilepsy

OBJECTIVE

Temporal lobe epilepsy is commonly associated with synchronous, hyper-synchronous and des-synchronous activity. The aim of the present work is to explore synchronization activity in both mesial areas in temporal lobe epileptic patients during the interictal state.

METHODS

Using a cluster technique, we analyzed 17 temporal lobe epilepsy patients' records of foramen ovale electrodes activity during the inter-ictal state.

RESULTS

There exists a clear tendency in the mesial area of the epileptic side to be organized as isolated clusters of electrical activity as compared with the contra-lateral side, which is organized in the form of large clusters of synchronous activity. The number of desynchronized areas is larger in the epileptic side than in the contra-lateral side in 16 out of 17 temporal lobe epileptic patients.

CONCLUSIONS

The mesial area responsible for the seizures is less synchronous than the contra-lateral; the different kind of synchronous organization accounts for a lower synchronization activity at the epileptic side, suggesting that this lack of synchronous cluster organization would favour the appearance of seizures.

SIGNIFICANCE

Our results shed new light regarding synchronization issues in temporal lobe epilepsy and also it would help in reducing drastically the time of study.

Estimating short-run and long-run interaction mechanisms in interictal state

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.

Epilepsy and nonlinear dynamics

This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.

Improved spatial characterization of the epileptic brain by focusing on nonlinearity

An advanced characterization of the complicated dynamical system brain is one of science's biggest challenges. Nonlinear time series analysis allows characterizing nonlinear dynamical systems in which low-dimensional nonlinearity gives rise to complex and irregular behavior. While several studies indicate that nonlinear methods can extract valuable information from neuronal dynamics, others doubt their necessity and conjecture that the same information can be obtained using classical linear techniques. To address this issue, we compared these two concepts, but included furthermore a combination of nonlinear measures with surrogates, an approach that has been designed to specifically focus on nonlinearity. As a benchmark we used the discriminative power to detect the seizure-generating hemisphere in medically intractable mesial temporal lobe epilepsy. We analyzed intracranial electroencephalographic recordings from the seizure-free interval of 29 patients. While the performance of both linear and nonlinear measures was weak, if not insignificant, a very high performance was obtained by the use of surrogate-corrected measures. Focusing on nonlinearity by using a combination of nonlinear measures with surrogates appears as the key to a successful characterization of the spatial distribution of the epileptic process.

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field

Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.

Impulse response function based on multivariate AR model can differentiate focal hemisphere in temporal lobe epilepsy

The purpose of this study is to propose and investigate a new approach for discriminating between focal and non-focal hemispheres in intractable temporal lobe epilepsy, based on applying multivariate time series analysis to the discharge-free background brain activity observed in nocturnal electrocorticogram (ECoG) time series. Five unilateral focal patients and one bilateral focal patient were studied. In order to detect the location of epileptic foci, linear multivariate autoregressive (MAR) models were fitted to the ECoG data; as a new approach for the purpose of summarizing these models in a single relevant parameter, the behavior of the corresponding impulse response functions was studied and described by an attenuation coefficient. In the majority of unilateral focal patients, the averaged attenuation coefficient was found to be almost always significantly larger in the focal hemisphere, as compared to the non-focal hemisphere. Also the amplitude of the fluctuations of the attenuation coefficient was significantly larger in the focal hemisphere. Moreover, in one patient showing a typical regular sleep cycle, the attenuation coefficient in the focal hemisphere tended to be larger during REM sleep and smaller during Non-REM sleep. In the bilateral focal patient, no statistically significant distinction between the hemispheres was found. This study provides encouraging results for new investigations of brain dynamics by multivariate parametric modeling. It opens up the possibility of relating diseases like epilepsy to the properties of inconspicuous background brain dynamics, without the need to record and analyze epileptic seizures or other evidently pathological waveforms.

Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings

The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (>>30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.

Future aspects of epilepsy research

This contribution in honour of Prof. Gerhard Pendl first reviews some recent studies on resected tissue, migrational disorders, and Rasmussen's Syndrome. These areas of basic research profit from recent advances of molecular biology and genetics. On the clinical side, some studies dealing with proton magnetic resonance spectroscopy are reviewed. In order to highlight the progress in clinical epilepsy research using modern methods of structural and functional imaging, functional outcome prediction is also reviewed. This kind of advanced clinical research is dealt with by discussing risk factor assessment associated with postsurgical decrements in memory. With regard to motor functions, we compare the yield of functional MR and intraoperative cortical stimulation in patients with lesions in or close to the Rolandic cortex. Progress in the field of advanced EEG analysis is reviewed in the context of "seizure prediction" and cognitive event-related potentials. Finally some of the new epilepsy treatment options, such as Gamma Knife treatment, where Prof. Pendl's group made pioneering contributions, are dealt with.

Prediction of epileptic seizures

For almost 40 years, neuroscientists thought that epileptic seizures began abruptly, just a few seconds before clinical attacks. There is now mounting evidence that seizures develop minutes to hours before clinical onset. This change in thinking is based on quantitative studies of long digital intracranial electroencephalographic (EEG) recordings from patients being evaluated for epilepsy surgery. Evidence that seizures can be predicted is spread over diverse sources in medical, engineering, and patent publications. Techniques used to forecast seizures include frequency-based methods, statistical analysis of EEG signals, non-linear dynamics (chaos), and intelligent engineered systems. Advances in seizure prediction promise to give rise to implantable devices able to warn of impending seizures and to trigger therapy to prevent clinical epileptic attacks. Treatments such as electrical stimulation or focal drug infusion could be given on demand and might eliminate side-effects in some patients taking antiepileptic drugs long term. Whether closed-loop seizure-prediction and treatment devices will have the profound clinical effect of their cardiological predecessors will depend on our ability to perfect these techniques. Their clinical efficacy must be validated in large-scale, prospective, controlled trials.

Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state

We compare dynamical properties of brain electrical activity from different recording regions and from different physiological and pathological brain states. Using the nonlinear prediction error and an estimate of an effective correlation dimension in combination with the method of iterative amplitude adjusted surrogate data, we analyze sets of electroencephalographic (EEG) time series: surface EEG recordings from healthy volunteers with eyes closed and eyes open, and intracranial EEG recordings from epilepsy patients during the seizure free interval from within and from outside the seizure generating area as well as intracranial EEG recordings of epileptic seizures. As a preanalysis step an inclusion criterion of weak stationarity was applied. Surface EEG recordings with eyes open were compatible with the surrogates' null hypothesis of a Gaussian linear stochastic process. Strongest indications of nonlinear deterministic dynamics were found for seizure activity. Results of the other sets were found to be inbetween these two extremes.

Chaos in the brain: a short review alluding to epilepsy, depression, exercise and lateralization

Electroencephalograms (EEGs) reflect the electrical activity of the brain. Even when they are analyzed from healthy individuals, they manifest chaos in the nervous system. EEGs are likely to be produced by a nonlinear system, since a nonlinear system with at least 3 degrees of freedom (or state variables) may exhibit chaotic behavior. Furthermore, such systems can have multiple stable states governed by "chaotic" ("strange") attractors. A key feature of chaotic systems is the presence of an infinite number of unstable periodic fixed points, which are found in spontaneously active neuronal networks (e.g., epilepsy). The brain has chemicals called neurotransmitters that convey the information through the 10(16) synapses residing there. However, each of these neurotransmitters acts through various receptors and their numerous subtypes, thereby exhibiting complex interactions. Albeit in epilepsy the role of chaos and EEG findings are well proven, in another condition, i.e., depression, the role of chaos is slowly gaining ground. The multifarious roles of exercise, neurotransmitters and (cerebral) hemispheric lateralization, in the case of depression, are also being established. The common point of reference could be nonlinear dynamics. The purpose of this review is to study those nonlinear/chaotic interactions and point towards new theoretical models incorporating the oscillation caused by the same neurotransmitter acting on its different receptor subtypes. This may lead to a better understanding of brain neurodynamics in health and disease.

The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy

The theory of deterministic chaos addresses simple deterministic dynamics in which nonlinearity gives rise to complex temporal behavior. Although biological neuronal networks such as the brain are highly complicated, a number of studies provide growing evidence that nonlinear time series analysis of brain electrical activity in patients with epilepsy is capable of providing potentially useful diagnostic information. In the present study, this analysis framework was extended by introducing a new measure xi, designed to discriminate between nonlinear deterministic and linear stochastic dynamics. For the evaluation of its discriminative power, xi was extracted from intracranial multi-channel EEGs recorded during the interictal state in 25 patients with unilateral mesial temporal lobe epilepsy. Strong indications of nonlinear determinism were found in recordings from within the epileptogenic zone, while EEG signals from other sites mainly resembled linear stochastic dynamics. In all investigated cases, this differentiation allowed to retrospectively determine the side of the epileptogenic zone in full agreement with results of the presurgical workup.

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.

Epileptic seizures are characterized by changing signal complexity

OBJECTIVE

Epileptic seizures are brief episodic events resulting from abnormal synchronous discharges from cerebral neuronal networks. The traditional methods of signal analysis are limited by the rapidly changing nature of the EEG signal during a seizure. Time-frequency analyses, however, such as those produced by the matching pursuit (MP) method can provide continuous decompositions of recorded seizure activity. These accurate decompositions can allow for more detailed analyses of the changes in complexity of the signal that may accompany seizure evolution.

METHODS

The MP algorithm was applied to provide time-frequency decompositions of entire seizures recorded from depth electrode contacts in patients with intractable complex partial seizures of mesial temporal onset. The results of these analyses were compared with signals generated from the Duffing equation that represented both limit cycle and chaotic behavior.

RESULTS

Seventeen seizures from 12 different patients were analyzed. These analyses reveal that early in the seizure, the most organized, rhythmic seizure activity is more complex than limit cycle behavior, and that signal complexity increases further later in the seizure.

CONCLUSIONS

Increasing complexity routinely precedes seizure termination. This may reflect progressive desynchronization.

Nonlinear EEG analysis and its potential role in epileptology

Deterministic chaos offers a striking explanation for apparently irregular behavior of the brain that is evidenced in the EEG. Recent developments in the physical-mathematical framework of the theory of nonlinear dynamics (colloquially often termed chaos theory) provide new concepts and powerful algorithms to analyze such time series. Because of its high versatility, nonlinear time series analysis has already gone beyond the physical sciences and, at present, is being successfully applied in a variety of disciplines, including cardiology, neurology, psychiatry, and epileptology. However, it is well known that different influencing factors limit the use of nonlinear measures to characterize EEG dynamics in a strict sense. Nevertheless, when interpreted with care, relative estimates of, e.g., the correlation dimension or the Lyapunov exponents, can reliably characterize different states of normal and pathologic brain function. In epileptology, extraction of nonlinear measures from the intracranially recorded EEG promises to be important for clinical practice. In addition to an immense reduction of information content of long-lasting EEG recordings, previous studies have shown that these measures enable (a) localization of the primary epileptogenic area in different cerebral regions during the interictal state, (b) investigations of antiepileptic drug effects, (c) analyses of spatio-temporal interactions between the epileptogenic zone and other brain areas, and (d) detection of features predictive of imminent seizure activity. Nonlinear time series analysis provides new and supplementary information about the epileptogenic process and thus contributes to an improvement in presurgical evaluation.

Spatial distribution of neuronal complexity loss in neocortical lesional epilepsies

PURPOSE

Nonlinear EEG analysis is valuable in characterizing the spatiotemporal dynamics of the epileptogenic process in mesial temporal lobe epilepsy. We examined the ability of the measure neuronal complexity loss (L*) to characterize the primary epileptogenic area of neocortical lesional epilepsies during the interictal state.

METHODS

Spatial distribution of L* (L* map) was extracted from electrocorticograms (n = 52) recorded during presurgical assessment via subdural 64-contact grid electrodes covering lesions in either frontal, parietal, or temporal neocortex in 15 patients. The exact location of recording contacts on the brain surface was identified by matching a postimplant lateral x-ray of the skull with a postoperatively obtained sagittal MRI scan. Reprojecting L* maps onto the subject's brain surface allowed us to compare the spatial distribution of L* with the resection range of the extended lesionectomy.

RESULTS

In each of the six patients who became seizure-free, maximum values of L* were restricted to recording sites coinciding with the area of resection. In contrast, L* maps of most patients who had no benefit from the resection indicated a more widespread extent or the existence of additional, probably autonomous, foci. The mean of L* values obtained from recording sites outside the area of resection correctly distinguished 13 patients (86.7 %) with respect to seizure outcome.

CONCLUSIONS

Relevant information obtained from long-lasting interictal electrocorticographic recordings can be compressed to a single L* map that contributes to a spatial characterization of the primary epileptogenic area. In neocortical lesional epilepsies, L* allows for identification and characterization of epileptogenic activity and thus provides an additional diagnostic tool for presurgical assessment.

Linear inverse filtering improves spatial separation of nonlinear brain dynamics: a simulation study

We examined topographic variations in nonlinear measures based on scalp voltages, which were generated by two simulated current dipoles each placed in a different hemisphere of a spherical volume conductor (three-shell model). Dipole dynamics were that of a three-torus and the x-component of the Lorenz-system and scalp voltage were calculated for a configuration of 29 electrode positions. Although estimates for correlation dimension D2 and Lyapunov exponent L1 were close to the theoretical values for the original time series, the simulated scalp voltage data showed almost no topographic resolution of dipole positions. In order to enhance topographic differentiation, we constructed linear inverse filters, to focus on brain activity from a specified brain region. It turned out that the nonlinear measures for the inversely filtered time series were much closer to the expected values (with respect to the location of the dipoles used in the simulation) than when using unfiltered data. Our preliminary results indicate that inverse filtering can improve the topographic resolution of nonlinear scalp EEG estimates.

Non-linear time series analysis of intracranial EEG recordings in patients with epilepsy--an overview

Deterministic chaos offers a striking explanation for apparently irregular behavior, a characteristic feature of brain electrical activity. The framework of the theory of non-linear dynamics provides new concepts and powerful algorithms to analyze such time series. However, different influencing factors render the use of non-linear measures in a strict sense problematic. Nevertheless, if interpreted with care, particularly the correlation dimension or the Lyapunov-exponents provide a means to reliably characterize different states of normal and pathological brain function. This overview summarizes recent findings applying this concept in the field of epileptology that promise to be important for clinical practice. Non-linear measures extracted from the intra-cranially recorded EEG allow (a) localization of epileptogenic areas in different cerebral regions even during seizure-free intervals, (b) investigation of the influence of anticonvulsive drugs and (c) detection of features predictive of imminent seizure activity. Moreover, particularly the dimensional complexity proves a valuable parameter reflecting spatially distributed neuronal activity during verbal learning and memory processes. Specific changes in time of this non-linear measure allow the prediction of memory performance and, in addition, represent an estimate of the recruitment potency in the anterior mesial temporal lobes. Thus, the application of non-linear time series analysis to brain electrical activity offers new information about the dynamics of the underlying neuronal networks.

Invasive EEG monitoring in children: when, where, and what?

With rapid advances in noninvasive technology, the need for chronic intracranial monitoring to define the epileptogenic region has diminished significantly. Its role in presurgical evaluation has come under scrutiny particularly in adults with lesional epilepsy. With the shift in surgical candidacy toward the younger age groups, however, invasive monitoring has regained its utility especially in children with normal imaging studies and cortical dysplasia. This review critically evaluates its continuing role, attempting to assess cost-benefit under specific clinical scenarios and proposes how the findings can be incorporated into the challenging task of surgical planning in intractable childhood epilepsy.

Long-term inpatient audiovisual scalp EEG monitoring

Long-term audiovisual scalp EEG monitoring is an essential diagnostic tool for the evaluation of paroxysmal disorders. The definitive classification of both nonepileptic and epileptic events is often possible only with the use of this technique. Assessment of response to treatment and the noninvasive presurgical localization of seizure foci are other important uses. The optimization of both clinical semiology and electrophysiologic data obtained from such studies is the subject of significant research efforts. Outcomes studies and advanced EEG analysis research should ultimately serve to minimize the cost of this valuable technique as well as maximizing its utility.