Tonotopic organization of the auditory cortex: pitch versus frequency representation.

According to the place principles of the classical hearing theory, the physical entity frequency is encoded in the auditory periphery as place information (tonotopic representation), which is decoded in more central parts of the auditory system to form the subjective entity pitch. However, this relation is true only for pure-tone signals (spectral pitch); it can be quite different in the case of complex auditory stimuli (virtual pitch), thus requiring a multistage process for pitch formation. Neuromagnetic measurements showed that the tonotopic organization of the primary auditory cortex reflects the pitch rather than the frequency of the stimulus; that is, the pitch formation process must take place in subcortical regions.

Real-time tracking of memory formation in the human rhinal cortex and hippocampus.

A fundamental question about human memory is which brain structures are involved, and when, in transforming experiences into memories. This experiment sought to identify neural correlates of memory formation with the use of intracerebral electrodes implanted in the brains of patients with temporal lobe epilepsy. Event-related potentials (ERPs) were recorded directly from the medial temporal lobe (MTL) as the patients studied single words. ERPs elicited by words subsequently recalled in a memory test were contrasted with ERPs elicited by unrecalled words. Memory formation was associated with distinct but interrelated ERP differences within the rhinal cortex and the hippocampus, which arose after about 300 and 500 milliseconds, respectively. These findings suggest that declarative memory formation is dissociable into subprocesses and sequentially organized within the MTL.

Human memory formation is accompanied by rhinal-hippocampal coupling and decoupling.

In humans, distinct processes within the hippocampus and rhinal cortex support declarative memory formation. But do these medial temporal lobe (MTL) substructures directly cooperate in encoding new memories? Phase synchronization of gamma-band electroencephalogram (EEG) oscillations (around 40 Hz) is a general mechanism of transiently connecting neural assemblies. We recorded depth-EEG from within the MTL of epilepsy patients performing a memorization task. Successful as opposed to unsuccessful memory formation was accompanied by an initial elevation of rhinal-hippocampal gamma synchronization followed by a later desynchronization, suggesting that effective declarative memory formation is accompanied by a direct and temporarily limited cooperation between both MTL substructures.

Internetwork and intranetwork communications during bursting dynamics: applications to seizure prediction.

We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing.

Human temporal lobe potentials in verbal learning and memory processes.

Animal experiments and lesion studies have shown the importance of temporal lobe structures for language and memory. We recorded intracranial cognitive potentials from the human lateral and medial temporal lobe in 26 patients with temporal lobe epilepsy undergoing presurgical evaluation, using a word- and a picture-recognition paradigm. Neuropsychological testing included word fluency, verbal reasoning, sustained attention and a verbal learning memory test (VLMT), which was an adapted version of the Rey auditory verbal learning test. Word-specific N400-potentials elicited in the middle temporal gyrus of the dominant left hemisphere (LTL-N400) predicted immediate recall performance after learning, whereas N400s, elicited by words but not pictures in the left anterior medial temporal lobe (AMTL-N400), predicted delayed recall. The number of words that were learned but forgotten after a 30-min delay correlated only with N400s elicited by words in the left anterior medial temporal lobe. Thus, intracranial recordings indicated that different electrophysiological responses in different temporal lobe structures were linked to memory scores from specific neuropsychological tests.

Limbic ERPs predict verbal memory after left-sided hippocampectomy.

Surgical removal of the dominant medial temporal lobe regions runs a considerable risk of verbal memory deficits which may be compensated for postoperatively by corresponding regions in the non-dominant medial temporal lobe. We examined this possibility by recording event-related potentials (ERPs) to words from the medial temporal lobes of patients with left-sided temporal lobe epilepsy (TLE) undergoing presurgical evaluation. N400 amplitudes in the right anterior medial temporal lobe predicted the postoperative verbal recall performance of individual patients with surprising accuracy, indicating that intracranial recordings can be used to quantify the functional capacities of the right hemisphere that can compensate for the verbal memory deficits after loss of medial temporal lobe structures in the left hemisphere.

Reduced signal complexity of intracellular recordings: a precursor for epileptiform activity?

Recent studies have shown that time windowed extraction of nonlinear parameters like an effective correlation dimension from intracranially recorded EEG of epileptic patients often allows to detect and identify an unequivocal "pre-ictal phase" preceding an epileptic seizure. In another study, however, such an anticipation could not be made. These conflicting findings may indicate that observed changes in nonlinear parameters probably depend on the type of elementary mechanisms underlying epileptic processes and/or the spatial distribution of neurons primarily involved in generation of epileptiform discharges. To test the existence of such dependencies, the transition from normal to epileptiform activity (EA) of CA3-neurons in hippocampal slices was analyzed in four epilepsy models, using a time windowed computation of an effective correlation dimension. Indeed, in xanthine and penicillin models, signal complexity in intracellular recordings was reduced before manifestation of paroxysmal depolarization shifts (PDS), whereas a preceding loss of complexity was missing in low-magnesium and veratridine models. These findings indicate that interictal-like EA is predictable only in some epilepsy models.

Single trial analysis of event related potentials: non-linear de-noising with wavelets.

We present and apply a method for single trial analysis of event related potentials (ERPs) that combines techniques from non-linear time series analysis with the wavelet transform. For this method, convincing results have already been achieved for simulated data as well as for intracranially recorded ERPs (Physica D 140 (2000) 257). However, ERPs are affected by a variety of external and internal experimental parameters, which makes the appropriate configuration of single trial analysis difficult. Thus, several pitfalls may occur in realistic applications. In this paper, we applied the method of non-linear de-noising with wavelets to both intra- and extracranially recorded ERPs, and show examples of how and where this single trial analysis can be used to obtain additional information on dynamic neural processes.

MEG measurements with SQUID as a diagnostic tool for epileptic patients.

In experimental studies with a SQUID (Super-conducting QUantum Interference Device) second order gradiometer, we recently registered the magnetoencephalogram (MEG) from different subjects under different physiological and psychological conditions from which we will determine normal and abnormal function of the human brain. Thus with our first measurements using the MEG spectra, we have succeeded in identifying the exact location of the abnormality in the human brain as shown in several individuals.

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.

Objective evidence of tinnitus in auditory evoked magnetic fields.

The waveforms of the auditory evoked magnetic field in normal-hearing individuals and patients suffering from tinnitus are distinctly different. In tinnitus patients, the magnetic wave M200 (corresponding to the electric wave P200, or P2) is delayed and only poorly developed or even completely missing, while the amplitude of the magnetic wave M100 (corresponding to the electric wave N100, or N1) is significally augmented. A very characteristic feature turned out to be the amplitude ratio of the two waves M200 and M100. Below the age of 50, the amplitude ratio M200/M100 represents a clear-cut criterion to distinguish between tinnitus patients and individuals without tinnitus. In tinnitus patients, the ratio is less than 0.5, independent of age, whereas, in young and middle-aged normal-hearing individuals, it is greater than 0.5. Since in normal-hearing individuals the average amplitude ratio decreases linearly with age, the clusters of amplitude ratios of the two groups begin to overlap beyond the age of 50. The hypothesis is put forward that the decrease of the average amplitude ratio in normal-hearing individuals reflects a degenerative process, probably initiated by multiple exogenous and endogenous factors, which leads to sustained neural activity in the generators of wave M200 and eventually gives rise to the sensation of tinnitus. The absence or poor development of wave M200 is a concomitant phenomenon, resulting from the involved generators being less responsive to external stimuli.

Tinnitus remission objectified by neuromagnetic measurements.

In a previous paper of ours (Hoke et al., 1989a) the hypothesis was put forward that the amplitude ratio of the two major waves of the auditory evoked magnetic field (AEF), M200/M100, is an objective measure which allows to discriminate between individuals suffering from tinnitus (ratio less than 0.5) and individuals without tinnitus (ratio greater than 0.5). We have now been able to trace the process of tinnitus remission in one exemplary case during a period of 256 days after acute onset of tinnitus (due to a noise trauma), in which the amplitude ratio recovered from 0 to a normal value of approximately 1. This very first objectification of tinnitus remission strongly supports our hypothesis and indicates that AEF may become an indispensable, invaluable tool in both tinnitus research and management.

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.

Nonlinear noise reduction using reference data.

We introduce a method to clean uncorrelated deterministic and stochastic noise components from time series. It combines recently developed techniques for nonlinear projection with properties of the wavelet transform to extract noise in state space. The method requires that time series are generated by a dynamical system which is at least approximately deterministic and that they are recorded together with a reference signal. Its efficiency was tested on both simulated signals and measured magnetic fields of the heart. Convincing results are obtained even at low signal-to-noise ratios.

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.

Is it possible to anticipate seizure onset by non-linear analysis of intracerebral EEG in human partial epilepsies?

Detection of electrophysiological features preceding and indicative of imminent seizures in patients with epilepsy would be a major breakthrough with immense scientific and clinical implications. The definition of a "pre-ictal state" several minutes prior to seizure onset would open a new time window for studying mechanisms of seizure generation as well as for possible therapeutic interventions. In this review we present recent findings from nonlinear time series analysis of intracranially recorded EEG that may allow to forecast seizure onset in patients with partial epilepsy.

Using bivariate signal analysis to characterize the epileptic focus: the benefit of surrogates.

The disease epilepsy is related to hypersynchronous activity of networks of neurons. While acute epileptic seizures are the most extreme manifestation of this hypersynchronous activity, an elevated level of interdependence of neuronal dynamics is thought to persist also during the seizure-free interval. In multichannel recordings from brain areas involved in the epileptic process, this interdependence can be reflected in an increased linear cross correlation but also in signal properties of higher order. Bivariate time series analysis comprises a variety of approaches, each with different degrees of sensitivity and specificity for interdependencies reflected in lower- or higher-order properties of pairs of simultaneously recorded signals. Here we investigate which approach is best suited to detect putatively elevated interdependence levels in signals recorded from brain areas involved in the epileptic process. For this purpose, we use the linear cross correlation that is sensitive to lower-order signatures of interdependence, a nonlinear interdependence measure that integrates both lower- and higher-order properties, and a surrogate-corrected nonlinear interdependence measure that aims to specifically characterize higher-order properties. We analyze intracranial electroencephalographic recordings of the seizure-free interval from 29 patients with an epileptic focus located in the medial temporal lobe. Our results show that all three approaches detect higher levels of interdependence for signals recorded from the brain hemisphere containing the epileptic focus as compared to signals recorded from the opposite hemisphere. For the linear cross correlation, however, these differences are not significant. For the nonlinear interdependence measure, results are significant but only of moderate accuracy with regard to the discriminative power for the focal and nonfocal hemispheres. The highest significance and accuracy is obtained for the surrogate-corrected nonlinear interdependence measure.