Christophe C. Jouny

Cardiac activation during arousal in humans : further evidence for hierarchy in the arousal response

OBJECTIVES One major subject of discussion in sleep studies is whether bursts of K-complexes (K-bursts) and delta waves (D-bursts), expressions of a subcortical arousal, truly reflect an arousal response during sleep. To address this question we studied the changes in heart rate (HR) during spontaneous arousals in healthy subjects. METHODS Twenty-seven healthy adults were examined. Arousals were graded in 4 levels, including the standard definition of a microarousal (MA), phases of transitory activation (PAT), D-bursts and K-bursts. HR was analyzed for 10 beats before and 20 beats during arousal. EEG spectral analysis was performed for all types of arousals, including in the analysis the 20 s period preceding the actual event. RESULTS Each type of arousal was associated with HR changes consisting of a tachycardia followed by a bradycardia. Changes were more pronounced during MA and PAT. Detailed analysis of the HR response showed that HR always increased before MA and PAT onset, associated with a rise in delta, theta and fast EEG activities, and suggesting a cerebral activation. CONCLUSIONS Our data suggest that such subcortical arousals represent a real arousal response inducing cardiac activation similar to that found during MA and PAT. During MA and PAT, a rise in HR appears before the onset of the actual arousal associated with an increase in EEG slow and fast activity. The link between EEG and HR variation during MA and PAT and the fluctuations in HR during subcortical arousal suggest a continuous spectrum in the arousal mechanisms, starting at the brainstem level and progressing to cortical areas.

Characterization of epileptic seizure dynamics using Gabor atom density.

OBJECTIVE The study of epileptic electroencephalograph (EEG) dynamics can potentially provide insights into seizure onset, evolution and termination. We propose a new synthetic measure based on time-frequency decomposition to provide detailed characterization of these dynamic changes. METHODS The matching pursuit (MP) method allows for continuous time-frequency decomposition. We have developed a derivative of the MP method, the Gabor atom density method (GAD) that facilitates interpretation during the dynamic ictal period. The GAD analysis was applied to intracranial recordings of complex partial seizures (n = 43) of mesial temporal origin in 7 patients. RESULTS Complex partial seizure occurrence is systematically associated with a GAD increase of 400 +/- 150%. The GAD increase coincides with the electrographical evidence of seizure onset. The similarity between seizures in a given patient is very high with uniform onset slope, maximum level and termination pattern. Global GAD responses over all channels can reveal detailed seizure propagation patterns including secondary independent foci and secondary generalization. CONCLUSIONS The GAD measure based on the MP decomposition is a reliable tool to detect seizure occurrence in long-term recordings, to differentiate seizures from artifacts on a multi-channel basis and to examine patterns of seizure propagation. The reproducible GAD pattern suggests consistent changes in signal inner structure and may provide new clues about seizure dynamics and evolution. SIGNIFICANCE The GAD method can provide information about seizure dynamics that can contribute to methods of seizure detection. These analyses may lead to better understanding of seizure termination and help facilitate application of responsive seizure control devices in humans.

Signal complexity and synchrony of epileptic seizures: is there an identifiable preictal period?

OBJECTIVE Epileptic seizures are characterized by increases in synchronized activity and increased signal complexity. Prediction of seizures depends upon detectable preictal changes before the actual ictal event. The studies reported here test whether two methods designed to detect changes in synchrony and complexity can identify any changes in a preictal period before visual EEG changes or clinical manifestations. METHODS Two methods are used to characterize different, but linked, properties of the signal-complexity and synchrony. The Gabor atom density (GAD) method allows for quantification of the time-frequency components of the EEG and characterizes the complexity of the EEG signal. The measure S, based on the goodness of fit of a multivariable autoregressive model, allows for characterization of the degree of synchrony of the EEG signal. RESULTS Complex partial seizures produce very specific patterns of increased signal complexity and subsequent postictal low complexity states. The measure S shows increased synchronization later including a prolonged period of increased synchrony in the postictal period. No significant preictal changes were seen unless contaminated by residual postictal changes in closely clustered seizures. CONCLUSIONS Both GAD and S measures reveal ictal and prolonged postictal changes; however, there were no significant preictal changes in either complexity or synchrony. Any application of methods to detect preictal changes must be tested on seizures sufficiently separated to avoid residual postictal changes in the potential preictal period.

Time course of arousal response during periodic leg movements in patients with periodic leg movements and restless legs syndrome

OBJECTIVE The temporal evolution of periodic leg movements (PLM) and the relationship of their arousing effect on sleep episode has not been extensively investigated. We studied the nocturnal evolution of PLM associated or not with microarousal (MA) and associated with slow wave activity (PLM with slow wave activity) in 23 patients with PLM and/or restless legs syndrome (RLS). METHODS All subjects had PLM associated with MA or with slow wave activity as well as without MA and all slept for 4 sleep cycles. Spectral electroencephalogrpahic (EEG) analysis was done for the 4 sleep cycles to assess the nocturnal variation in slow wave activity (SWA). RESULTS Sixty percent of PLM were associated with MA, 4% were associated with slow wave activity whereas 36% showed no EEG changes. There was a clear prevalence of PLM with MA in stages 1 and 2 while PLM without MA were prevalent in slow wave sleep. The night-time PLM index progressively declined from the first to the last sleep cycle (P<0.005), without differences between PLM types, or between PLM and RLS patients. The decline of PLM duplicated the temporal trend in SWA over consecutive sleep cycles. CONCLUSIONS PLM showed a typical pattern of progressive decline throughout the night following the exponential decline in SWA. These over-time variations occurred independently of changes in the rate of PLM associated or not with MA or associated with slow wave activity, suggesting that variations in arousal threshold and sleep propensity did not affect the PLM arousing effect. The PLM-related arousal response might be affected by interaction of circadian and sleep stage influences with the addition of sleep oscillatory processes.

Duration of complex partial seizures: An intracranial EEG study

Purpose: The dynamics of partial seizures originating from neocortical and mesial temporal regions are thought to differ, yet there are no quantitative comparative studies. The studies reported here investigate the duration of complex partial seizures in these populations using analyses of seizures recorded from intracranial arrays.

Characterization of early partial seizure onset: Frequency, complexity and entropy

OBJECTIVE A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed. METHODS Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures). RESULTS GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset. CONCLUSIONS Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset. SIGNIFICANCE Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations

OBJECTIVE We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans. METHODS We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ). We analyzed changes of HFO activity from interictal to preictal and to ictal periods. RESULTS The average HFO rate is higher for SOZ channels compared to non-SOZ channels during all periods. Amplitudes and durations of HFO are higher for events within the SOZ in all periods compared to non-SOZ events, while their frequency is lower. All analyzed HFO features increase for the ictal period. CONCLUSIONS HFO may occur in all channels but their rate is significantly higher within SOZ and HFO characteristics differ from HFO outside the SOZ, but the effect size of difference is small. SIGNIFICANCE The present results show that based on accumulated dataset it is possible to distinguish HFO features different for SOZ and non-SOZ channels, and to show changes in HFO characteristics during the transition from interictal to preictal and to ictal periods.

Intrinsic Ictal Dynamics at the Seizure Focus: Effects of Secondary Generalization Revealed by Complexity Measures

Summary:  Purpose: Partial seizures (PSs) may be self‐limited regional events or propagate further and secondarily generalize. The mechanisms and dynamics of secondarily generalized tonic–clonic seizures (GTCSs) are not well understood. Methods with which to assess the dynamic of those events are also limited.

Ictal propagation of high frequency activity is recapitulated in interictal recordings: Effective connectivity of epileptogenic networks recorded with intracranial EEG

Seizures are increasingly understood to arise from epileptogenic networks across which ictal activity is propagated and sustained. In patients undergoing invasive monitoring for epilepsy surgery, high frequency oscillations have been observed within the seizure onset zone during both ictal and interictal intervals. We hypothesized that the patterns by which high frequency activity is propagated would help elucidate epileptogenic networks and thereby identify network nodes relevant for surgical planning. Intracranial EEG recordings were analyzed with a multivariate autoregressive modeling technique (short-time direct directed transfer function--SdDTF), based on the concept of Granger causality, to estimate the directionality and intensity of propagation of high frequency activity (70-175 Hz) during ictal and interictal recordings. These analyses revealed prominent divergence and convergence of high frequency activity propagation at sites identified by epileptologists as part of the ictal onset zone. In contrast, relatively little propagation of this activity was observed among the other analyzed sites. This pattern was observed in both subdural and depth electrode recordings of patients with focal ictal onset, but not in patients with a widely distributed ictal onset. In patients with focal ictal onsets, the patterns of propagation recorded during pre-ictal (up to 5 min immediately preceding ictal onset) and interictal (more than 24h before and after seizures) intervals were very similar to those recorded during seizures. The ability to characterize epileptogenic networks from interictal recordings could have important clinical implications for epilepsy surgery planning by reducing the need for prolonged invasive monitoring to record spontaneous seizures.

Partial Seizures Are Associated with Early Increases in Signal Complexity

OBJECTIVES Partial seizures are often believed to be associated with EEG signals of low complexity because seizures are associated with increased neural network synchrony. The investigations reported here provide an assessment of the signal complexity of epileptic seizure onsets using newly developed quantitative measures. METHODS Using the Gabor atom density (GAD) measure of signal complexity, 339 partial seizures in 45 patients with intracranial electrode arrays were analyzed. Segmentation procedures were applied to determine the timing and amplitude of GAD changes relative to the electrographic onset of the seizure. RESULTS Three hundred and thirty out of 339 seizures have significant complexity level changes, with 319 (97%) having an increase in complexity. GAD increases occur within seconds of the onset of the partial seizure but are not observed in channels remote from the focus. The complexity increase is similar for seizures from mesial temporal origin, neocortical temporal and extra-temporal origin. CONCLUSIONS Partial onset seizures are associated with early increases in signal complexity as measured by GAD. This increase is independent of the location of the seizure focus. SIGNIFICANCE Despite the often predominant rhythmic activity that characterizes onset and early evolution of epileptic seizures, partial seizure onset is associated with an early increase in complexity. These changes are common to partial seizures originating from different brain regions, indicating a similar seizure dynamic.