Despiking SEEG signals reveals dynamics of gamma band preictal activity

Transitional pattern as a potential marker of epileptogenic zone in focal epilepsy - Clinical observations from intracerebral recordings

OBJECTIVES

To investigate the characteristics of transition from interictal to ictal phase in intracranial recordings and further to determine the potential marker of epileptogenic zone.

METHODS

Eighteen patients with drug-refractory epilepsy who underwent stereo-electroencephalography (SEEG) evaluation and subsequent resective surgery were included. All patients were seizure-free post-operatively. The recorded seizures were retrospectively reviewed and time episodes including 5 min before electrographic onset were selected for further analysis to verify the presence of a transitional pattern in the transitional phase, which was distinct from interictal background and ictal onset. Besides, the components of transitional patterns which characterized by different pathological waveforms were identified by visual analysis and time-frequency analysis. The prevalence of transitional patterns between resection and non-resection, lesion and non-lesion sites were compared. In addition, the association between transitional patterns and types of epilepsy was explored.

RESULTS

Six transitional patterns characterized by different combinations of multiple pathological waveforms by visual analysis combined with time-frequency analysis were identified: spike/spike-waves/polyspikes; spike superimposed by HFOs; spike superimposed by gamma oscillations; spike followed by suppression; spike superimposed by HFOs and followed by suppression; and spike superimposed by gamma oscillations and followed by suppression. A higher prevalence of transitional patterns in resection than non-resection (p < 0.001) and in lesion than non-lesion contacts (p < 0.001). The pattern characterized by spike superimposed by HFOs and followed by suppression was more prevalent in resection than non-resection sites (p = 0.004). Further, there was an association between the complexity of transitional patterns and the location of contacts. Patterns with higher degree of complexity were more likely to be inside the resection area (p = 0.035). Besides, we found the pattern with spike superimposed by HFOs was associated more with limbic epilepsy than neocortical epilepsy (p < 0.001), whereas another 3 patterns, spike superimposed by gamma oscillation, spike followed by suppression and spike combined with HFOs and suppression, were observed more frequently in neocortical epilepsy than limbic epilepsy (p = 0.018, 0.011 and < 0.001, respectively).

CONCLUSION

Transitional patterns from interictal to ictal state were characterized by different combinations of multiple pathological waveforms, which may be a potential marker of epileptogenic zone. Our findings support that the interaction of different neuronal oscillations or waveforms generated by different neuronal populations may be the potential mechanism of seizure generation.

Using centrality measures to extract core pattern of brain dynamics during the resting state

The patterns of brain dynamics were studied during resting state on a macroscopic scale for control subjects and multiple sclerosis patients. Macroscopic brain dynamics is defined after successive coarse-grainings and selection of significant patterns and transitions based on Markov representation of brain activity. The resulting networks show that control dynamics is merely organized according to a single principal pattern whereas patients dynamics depict more variable patterns. Centrality measures are used to extract core dynamical pattern in brain dynamics and classification technique allow to define MS dynamics with relevant error rate.

High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes

OBJECTIVE

High-frequency oscillations (HFOs) in intracerebral EEG (stereoelectroencephalography; SEEG) are considered as better biomarkers of epileptogenic tissues than spikes. How this can be applied at the patient level remains poorly understood. We investigated how well HFOs and spikes can predict epileptogenic regions with a large spatial sampling at the patient level.

METHODS

We analyzed non-REM sleep SEEG recordings sampled at 2,048Hz of 30 patients. Ripples (Rs; 80-250Hz), fast ripples (FRs; 250-500Hz), and spikes were automatically detected. Rates of these markers and several combinations-spikes co-occurring with HFOs or FRs and cross-rate (Spk⊗HFO)-were compared to a quantified measure of the seizure onset zone (SOZ) by performing a receiver operating characteristic analysis for each patient individually. We used a Wilcoxon signed-rank test corrected for false-discovery rate to assess whether a marker was better than the others for predicting the SOZ.

RESULTS

A total of 2,930 channels was analyzed (median of 100 channels per patient). The HFOs or any of its variants were not statistically better than spikes. Only one feature, the cross-rate, was better than all the other markers. Moreover, fast ripples, even though very specific, were not delineating all epileptogenic tissues.

INTERPRETATION

At the patient level, the performance of HFOs is weakened by the presence of strong physiological HFO generators. Fast ripples are not sensitive enough to be the unique biomarker of epileptogenicity. Nevertheless, combining HFOs and spikes using our proposed measure-the cross-rate-is a better strategy than using only one marker. Ann Neurol 2018;83:84-97.

Integration of stationary wavelet transform on a dynamic partial reconfiguration for recognition of pre-ictal gamma oscillations

To define the neural networks responsible of an epileptic seizure, it is useful to perform advanced signal processing techniques. In this context, electrophysiological signals present three types of waves: oscillations, spikes, and a mixture of both. Recent studies show that spikes and oscillations should be separated properly in order to define the accurate neural connectivity during the pre-ictal, seizure and inter-ictal states. Retrieving oscillatory activity is a sensitive task due to the frequency overlap between oscillations and transient activities. Advanced filtering techniques have been proposed to ensure a good separation between oscillations and spikes. It would be interesting to apply them in real time for instantaneous monitoring, seizure warning or neurofeedback systems. This requires improving execution time. This constraint can be overcome using embedded systems that combine hardware and software in an optimized architecture.

We propose here to implement a stationary wavelet transform (SWT) as an adaptive filtering technique retaining only pre-ictal gamma oscillations, as validated in previous work, on a partial dynamic configuration. Then, the same architecture is used with further modifications to integrate spatio temporal mapping for an early recognition of seizure build-up.

Data that contains transient, pre-ictal gamma oscillations and a seizure was simulated. the method on real intracerebral signals was also tested. The SWT was integrated on an embedded architecture. This architecture permits a spatio temporal mapping to detect the accurate time and localization of seizure build-up, while reducing computation time by a factor of around 40. Embedded systems are a promising venue for real-time applications in clinical systems for epilepsy.

What are the assets and weaknesses of HFO detectors? A benchmark framework based on realistic simulations

High-frequency oscillations (HFO) have been suggested as biomarkers of epileptic tissues. While visual marking of these short and small oscillations is tedious and time-consuming, automatic HFO detectors have not yet met a large consensus. Even though detectors have been shown to perform well when validated against visual marking, the large number of false detections due to their lack of robustness hinder their clinical application. In this study, we developed a validation framework based on realistic and controlled simulations to quantify precisely the assets and weaknesses of current detectors. We constructed a dictionary of synthesized elements—HFOs and epileptic spikes—from different patients and brain areas by extracting these elements from the original data using discrete wavelet transform coefficients. These elements were then added to their corresponding simulated background activity (preserving patient- and region- specific spectra). We tested five existing detectors against this benchmark. Compared to other studies confronting detectors, we did not only ranked them according their performance but we investigated the reasons leading to these results. Our simulations, thanks to their realism and their variability, enabled us to highlight unreported issues of current detectors: (1) the lack of robust estimation of the background activity, (2) the underestimated impact of the 1/f spectrum, and (3) the inadequate criteria defining an HFO. We believe that our benchmark framework could be a valuable tool to translate HFOs into a clinical environment.