Quantifying connectivity in a physiology based model using adaptive dynamic causal modelling.

This paper proposes an Adaptive Dynamic Causal Modelling based approach to detect and quantify effective connectivity in human brain structures injured by epileptic activities. The identification of the parameters in the physiology based model subtended the Electroencephalographic observations is performed by improving the optimization step in the Expectation Maximization algorithm. Considering unidirectional flow propagation, we show the efficiency of our proposed approach compared to the conventional technique.

Seizures of temporal lobe epilepsy: identification of subtypes by coherence analysis using stereo-electro-encephalography.

OBJECTIVES: Two subtypes of temporal lobe epilepsy (TLE) according to the structures initially involved during seizures are currently recognized: medial TLE (MTLE) and lateral (or neocortical) TLE (LTLE). A few reports have suggested that the classification of TLE subtypes might be larger according to variations in the interactions between medial structures and the neocortex. In this study, we analyzed these interactions using coherence analysis of stereo-encephalographic (SEEG) signals during spontaneous seizures. METHODS: Twenty-seven patients with drug-resistant TLE, diagnosed from ictal SEEG recordings obtained during pre-surgical evaluation, were studied. Orthogonally implanted depth electrodes with multiple leads according to Talairach's method were used to sample medial and neocortical structures. Coherence analysis of ictal discharges was performed between two SEEG bipolar signals from adjacent leads located either in medial structures (amygdala and hippocampus) or in neocortical regions of the temporal lobe. A new algorithm, which was designed to reduce the bias inherent in coherence estimation, was used to compute the coherence. RESULTS: We were able to classify TLE seizures (TLES) into 4 distinct categories: (1) 'medial' TLES, characterized by medial onset with later involvement of the neocortex in the form of a 'phasic' discharge. High ictal coherence values were observed between medial structures; (2) 'medial-lateral' TLES which started in medial structures with a fast low-voltage discharge (FLVD) which rapidly affects the neocortex (< or = 3 s). High coherence values were observed between medial and lateral structures; (3) 'lateral-medial' TLES, which are different from medial-lateral TLES in that the FLVD starts in the lateral neocortex and involves the amygdala and/or hippocampus almost immediately after; (4) 'lateral' TLES: characterized by a neocortical onset, a delayed involvement of medial structures (when present), and high coherence values between neocortical structures. CONCLUSIONS: These results demonstrate the existence of numerous interactions between medial limbic structures and the neocortex during TLE seizures. Such findings could have implications for surgical strategies and the prognosis of epilepsy surgery, particularly when limited resection is indicated.

K-means clustering method for auditory evoked potentials selection.

Surface auditory evoked potentials are generally recorded using a headset of 32, 64 or 128 electrodes, but the quality of the responses is quite heterogeneous on the scalp surface. In some contexts, such as the analysis of auditory evoked potentials recorded in radio-frequency fields, the signal quality is essential, and it appears pertinent to consider only a limited number of electrodes. Therefore, before analysing signals influenced by radio-frequency fields, it is necessary to consider the preliminary step of selecting the channels where auditory activity is strong. This step is often realised by human visual selection and can take a considerable time. In this paper, a simple k-means clustering method is proposed, to select automatically the important channels, and the results are compared with traditional methods of selection. The method detected channels that showed a concordance rate of 86.5% with the visual selection (performed by five individuals) and gave the same final selection (only two extra electrodes in the automatic case). Moreover, the time needed for this automatic selection was 100 times less than that for the visual selection, and also human variability was avoided.

Analysis of auditory evoked potential parameters in the presence of radiofrequency fields using a support vector machines method.

The paper presents a study of global system for mobile (GSM) phone radiofrequency effects on human cerebral activity. The work was based on the study of auditory evoked potentials (AEPs) recorded from healthy humans and epileptic patients. The protocol allowed the comparison of AEPs recorded with or without exposure to electrical fields. Ten variables measured from AEPs were employed in the design of a supervised support vector machines classifier. The classification performance measured the classifier's ability to discriminate features performed with or without radiofrequency exposure. Most significant features were chosen by a backward sequential selection that ranked the variables according to their pertinence for the discrimination. Finally, the most discriminating features were analysed statistically by a Wilcoxon signed rank test. For both populations, the N100 amplitudes were reduced under the influence of GSM radiofrequency (mean attenuation of -0.36 microV for healthy subjects and -0.60 microV for epileptic patients). Healthy subjects showed a N100 latency decrease (-5.23 ms in mean), which could be consistent with mild, localised heating. The auditory cortical activity in humans was modified by GSM phone radiofrequencies, but an effect on brain functionality has not been proven.

Exploring neural directed interactions with transfer entropy based on an adaptive kernel density estimator.

This paper aims at estimating causal relationships between signals to detect flow propagation in autoregressive and physiological models. The main challenge of the ongoing work is to discover whether neural activity in a given structure of the brain influences activity in another area during epileptic seizures. This question refers to the concept of effective connectivity in neuroscience, i.e. to the identification of information flows and oriented propagation graphs. Past efforts to determine effective connectivity rooted to Wiener causality definition adapted in a practical form by Granger with autoregressive models. A number of studies argue against such a linear approach when nonlinear dynamics are suspected in the relationship between signals. Consequently, nonlinear nonparametric approaches, such as transfer entropy (TE), have been introduced to overcome linear methods limitations and promoted in many studies dealing with electrophysiological signals. Until now, even though many TE estimators have been developed, further improvement can be expected. In this paper, we investigate a new strategy by introducing an adaptive kernel density estimator to improve TE estimation.

Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.

Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.

Effects of GSM signals on auditory evoked responses.

The article presents a study of the influence of radio frequency (RF) fields emitted by mobile phones on human cerebral activity. Our work was based on the study of Auditory Evoked Potentials (AEPs) recorded on the scalp of healthy humans and epileptic patients. The protocol allowed us to compare AEPs recorded with or without exposure to RFs. To get a reference, a control session was also introduced. In this study, the correlation coefficients computed between AEPs, as well as the correlation coefficients between spectra of AEPs were investigated to detect a possible difference due to RFs. A difference in the correlation coefficients computed in control and experimental sessions was observed, but it was difficult to deduce the effect of RFs on human health.