Frédéric Alexis Rudolf Zubler

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest

OBJECTIVE Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures. METHODS 94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients. RESULTS The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81. CONCLUSION Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power. SIGNIFICANCE Quantitative methods might increase the prognostic yield of currently used multi-modal approaches.

Prognostic and diagnostic value of EEG signal coupling measures in coma

OBJECTIVE Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20-30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow-Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Chapter Eight - Seizure Termination

A better understanding of the mechanisms by which most focal epileptic seizures stop spontaneously within a few minutes would be of highest importance, because they could potentially help to improve existing and develop novel therapeutic measures for seizure control. Studies devoted to unraveling mechanisms of seizure termination often take one of the two following approaches. The first approach focuses on metabolic mechanisms such as ionic concentrations, acidity, or neuromodulator release, studying how they are dependent on, and in turn affect changes of neuronal activity. The second approach uses quantitative tools to derive functional networks from electrophysiological recordings and analyzes these networks with mathematical methods, without focusing on actual details of cell biology. In this chapter, we summarize key results obtained by both of these approaches and attempt to show that they are complementary and equally necessary in our aim to gain a better understanding of seizure termination.

Suppression of interictal spikes during phasic rapid eye movement sleep: a quantitative stereo-electroencephalography study.

Tonic and phasic rapid eye movement (REM) sleep seem to represent two different brain states exerting different effects on epileptic activity. In particular, interictal spikes are suppressed strongly during phasic REM sleep. The reason for this effect is not understood completely. A different level of synchronization in phasic and tonic REM sleep has been postulated, yet never measured directly. Here we assessed the interictal spike rate across non‐REM (NREM) sleep, phasic and tonic REM sleep in nine patients affected by drug resistant focal epilepsy: five with type II focal cortical dysplasia and four with hippocampal sclerosis. Moreover, we applied different quantitative measures to evaluate the level of synchronization at the local and global scale during phasic and tonic REM sleep. We found a lower spike rate in phasic REM sleep, both within and outside the seizure onset zone. This effect seems to be independent from the histopathological substrate and from the brain region, where epileptic activity is produced (temporal versus extra‐temporal). A higher level of synchronization was observed during tonic REM sleep both on a large (global) and small (local) spatial scale. Phasic REM sleep appears to be an interesting model for understanding the mechanisms of suppression of epileptic activity.

Auditory discrimination improvement predicts awakening of postanoxic comatose patients treated with targeted temperature management at 36°C.

BACKGROUND Outcome prognostication in postanoxic comatose patients is more accurate in predicting poor than good recovery. Using electroencephalography recordings in patients treated with targeted temperature management at 33°C (TTM 33), we have previously shown that improvement in auditory discrimination over the first days of coma predicted awakening. Given the increased application of a 36°C temperature target (TTM 36), here we aimed at validating the predictive value of auditory discrimination in the TTM 36 setting. METHODS In this prospective multicenter study, we analyzed the EEG responses to auditory stimuli from 60 consecutive patients from the first and second coma day. A semiautomatic decoding analysis was applied to single patient data to quantify discrimination performance between frequently repeated and deviant sounds. The decoding change from the first to second day was used for predicting patient outcome. RESULTS We observed an increase in auditory discrimination in 25 out of 60 patients. Among them, 17 awoke from coma (68% positive predictive value; 95% confidence interval: 0.46-0.85). By excluding patients with electroencephalographic epileptiform features, 15 of 18 exhibited improvement in auditory discrimination (83% positive predictive value; 95% confidence interval: 0.59-0.96). Specificity of good outcome prediction increased after adding auditory discrimination to EEG reactivity. CONCLUSION These results suggest that tracking of auditory discrimination over time is informative of good recovery independent of the temperature target. This quantitative test provides complementary information to existing clinical tools by identifying patients with high chances of recovery and encouraging the maintenance of life support.

Correlating Interictal Spikes with Sigma and Delta Dynamics during Non-Rapid-Eye-Movement-Sleep

Interictal spikes (IS) are one of the major hallmarks of epilepsy. Understanding the factors promoting or suppressing IS would increase our comprehension of epilepsy and possibly open new avenues for therapy. Sleep strongly influences epileptic activity, and the modulatory effects of the different sleep stages on IS have been studied for decades. However, several aspects are still disputed, in particular the role of sleep spindles and slow waves in the activation of IS during Non-REM sleep. Here, we correlate the rate of IS with quantitative measures derived from stereo-EEG during one Non-REM cycle in 10 patients suffering from drug-resistant epilepsy due to type 2 focal cortical dysplasia. We show that the IS rate (ISR) is positively correlated with sigma power (a surrogate for sleep-spindle density) but negatively correlated with delta power (surrogate for slow wave activity). In addition, we present two new indices for quantifying the spatial and temporal instability of sleep. We found that both instability indices are correlated with a high ISR. The main contribution of this study is to confirm the suppressive effect of stable deep sleep on IS. This result might influence future guidelines for therapy of patients suffering from epilepsy and sleep disorders.

Stroke causes a transient imbalance of interhemispheric information flow in EEG during non-REM sleep

OBJECTIVE Large-scale connectivity, especially interhemispheric connections, plays a crucial role for recovery after stroke. Here we used methods from information theory to characterize interhemispheric information flow in wake- and sleep-EEG after cerebral ischemia. METHODS 34 patients with unilateral ischemic stroke were included. Symbolic Transfer Entropy (STE) was applied between bipolar EEG signals on the left and the right cerebral hemisphere during polysomnographic recordings in the acute phase and 3 months after stroke. RESULTS In the acute phase, we found a sleep stage-dependent preferred interhemispheric asymmetry: during non-REM sleep the information flow was predominantly directed from the contralesional toward the ipsilesional hemisphere. This effect was greatly reduced in a follow-up recording 3 months after stroke onset. CONCLUSION Our findings are consistent with functional imaging studies showing a transient hyperactivity of contralesional areas after stroke. We conclude that STE is a robust method for detecting post-stroke connectivity reorganizations, and that sleep stages have to be taken into account when assessing functional connectivity. SIGNIFICANCE EEG is more widely available than functional MRI. Future studies will have to confirm whether EEG derived STE can be useful in a clinical setting during rehabilitation after stroke.

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma

Prominent research in patients with disorders of consciousness investigated the electrophysiological correlates of auditory deviance detection as a marker of consciousness recovery. Here, we extend previous studies by investigating whether somatosensory deviance detection provides an added value for outcome prediction in postanoxic comatose patients.