Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy.

What is the real shape of extracellular spikes?

We show that the standard filters used for on-line spike detection in most hardware acquisition systems introduce distortions in the recorded spike shapes. This is because on-line spike detection is done after band pass filtering the data with causal filters. As illustrated with three clusters of spike shapes from a real single cell recording in a human subject, causal filtering can create a spurious negative rebound and a smooth looking appearance of the spikes. We also show that these filtering distortions can make artifacts look similar to real spikes.

Precise timing accounts for posttraining sleep-dependent enhancements of the auditory mismatch negativity.

Memory consolidation is a long-lasting dynamic process by which new acquired information is transformed at different analysis levels, from molecules to cognition, without additional practice. Results from a previous study on event-related potentials (ERPs) suggest that part of the neural events promoting changes in the electrophysiological correlates of enhanced automatization in a sound discrimination task occur during sleep. These data were reanalyzed in the present study at the single-trial level, and results indicated that the first night of sleep succeeding training is absolutely required to improve the timing consistency of cortical neural assemblies involved in automatic sound-change detection, as revealed by a significant reduction in the latency-jitter of the MMN response across trials. This change in the regularity of the brain response to previously trained sounds facilitated involuntary switch of attention towards the same sounds when they were task irrelevant, as reflected by the P3a emergence after posttraining sleep. Both responses were, however, prevented in subjects deprived of sleep the night following training in the sound discrimination task. We hypothesize that the reduction in the MMN latency-jitter, which, in turn, triggered an automatic shift of attention, might result from a change in synaptic efficacy and/or neural excitability, rather than from changes in firing synchronization and/or size of representation.

Enhanced re-habituation of the orienting response of the human event-related potential.

Previous studies found the amplitude of the orienting response (OR) of the human event-related potential to decrease with repeated stimulus presentations. This decrease has been suggested to reflect short-term habituation and/or long-term habituation, both of which are learning processes. However, this earlier research failed to provide direct evidence supporting this claim. The present study attempted to show that the OR pattern shares one important feature of habituation: an enhanced response decrement across stimulus-presentation blocks (enhanced re-habituation). Participants received four blocks of 25 auditory stimulus presentations and showed an OR decrement both within (short-term habituation) and across (long-term habituation) blocks. Importantly, the OR decreased more rapidly during later than initial trial blocks, suggesting enhanced re-habituation. The latter result supports the notion that the amplitude decrement reflects an elementary learning process.

Single-trial event-related potentials with wavelet denoising.

The application of a recently proposed denoising implementation for obtaining event-related potentials (ERPs) at the single-trial level is shown. We study its performance in simulated data as well as in visual and auditory ERPs. For the simulated data, the method gives a significantly better reconstruction of the single-trial event-related responses in comparison with the original data and also in comparison with a reconstruction based on conventional Wiener filtering. Moreover, with wavelet denoising we obtain a significantly better estimation of the amplitudes and latencies of the simulated ERPs. For the real data, the method clearly improves the visualization of both visual and auditory single-trial ERPs. This allows the calculation of better averages as well as the study of systematic or unsystematic variations between trials. Since the method is fast and parameter free, it could complement the conventional analysis of ERPs.

Event synchronization: a simple and fast method to measure synchronicity and time delay patterns.

We propose a simple method to measure synchronization and time-delay patterns between signals. It is based on the relative timings of events in the time series, defined, e.g., as local maxima. The degree of synchronization is obtained from the number of quasisimultaneous appearances of events, and the delay is calculated from the precedence of events in one signal with respect to the other. Moreover, we can easily visualize the time evolution of the delay and synchronization level with an excellent resolution. We apply the algorithm to short rat electroencephalogram (EEG) signals, some of them containing spikes. We also apply it to an intracranial human EEG recording containing an epileptic seizure, and we propose that the method might be useful for the detection of epileptic foci. It can be easily extended to other types of data and it is very simple and fast, thus being suitable for on-line implementations.

Performance of different synchronization measures in real data: a case study on electroencephalographic signals.

We study the synchronization between left and right hemisphere rat electroencephalographic (EEG) channels by using various synchronization measures, namely nonlinear interdependences, phase synchronizations, mutual information, cross correlation, and the coherence function. In passing we show a close relation between two recently proposed phase synchronization measures and we extend the definition of one of them. In three typical examples we observe that except mutual information, all these measures give a useful quantification that is hard to be guessed beforehand from the raw data. Despite their differences, results are qualitatively the same. Therefore, we claim that the applied measures are valuable for the study of synchronization in real data. Moreover, in the particular case of EEG signals their use as complementary variables could be of clinical relevance.

Habituation and sensitization in rat auditory evoked potentials: a single-trial analysis with wavelet denoising.

In this work, systematic changes of single-trial auditory evoked potentials elicited in rats were studied. Single-trial evoked potentials were obtained with the help of wavelet denoising, a very recently proposed method that has already been shown to be useful in the analysis of scalp human evoked potentials. For the evoked components in the 13-24-ms range (i.e. P13, N18, P20 and N24), it was possible to identify slow exponential decreases in the peak amplitudes, most likely related to a slow habituation process, while for N18, an initial increase in amplitude was also found. On the contrary, the slower components (N38 and N52) habituated within a few trials, and we therefore propose that they are related to a different functional process. The outcomes of the present study show that wavelet denoising is a useful technique for analyzing evoked potentials in rats at the single-trial level. In fact, in the present study it was possible to obtain more information than the one described in previous related works. This allows the study of other forms of learning processes in rats with the aid of evoked potentials. Finally, the outcomes of this study may have some relevance for the comparison of human and rat evoked potentials.

Wavelet Transform in the analysis of the frequency composition of evoked potentials.

This technical paper deals with the application of the Wavelet Transform to the study of evoked potentials. In particular, Wavelet Transform gives an optimal time-dependent frequency decomposition of the evoked responses, something difficult to be achieved with previous methods such as the Fourier Transform. We describe in detail the protocol for implementing the decomposition based on the Wavelet Transform and apply it to two different types of evoked potentials. In the first case we study alpha responses in pattern visual evoked potentials and in the second case, we study gamma responses to bimodal (auditory and visual) stimulation. Although in this study we focus on methodological issues, we briefly discuss physiological implications of the present time-frequency analysis. Furthermore, we show examples of the better performance of the wavelet decomposition in comparison with Fourier-based methods.

Searching for hidden information with Gabor Transform in generalized tonic-clonic seizures.

The analysis of generalized tonic clonic seizures is usually difficult with scalp EEG due to muscle artifact. We applied Gabor Transform to evaluate 20 seizures from 8 consecutive patients admitted for video-EEG monitoring. We studied the relative intensity ratios of alpha, theta and delta bands over time. In 14/20 events we found a significant decremental activity in the delta band at the onset of the seizure indicating that this is dominated by theta and alpha bands. We conclude that GT is a useful auxiliary tool in the analysis of ictal activity that sheds light on the underlying pathophysiological mechanisms.