Search

Objective: The stereo electroencephalogram (SEEG) recordings are the state-of-the art tool used in pre-surgical evaluation of drug-unresponsive epileptic patients. Coupled with SEEG, electrical cortical stimulation (CS) offers a complementary tool to investigate the lesioned/healthy brain regions and to identify the epileptic zones with precision. However, the propagation of this stimulation inside the brain masks the cerebral activity recorded by nearby multi-contact SEEG electrodes. The objective of this paper is to propose a novel filtering approach for suppressing the CS artifact in SEEG signals using time, frequency as well as spatial information. Methods: The method combines spatial filtering with tunable-Q wavelet transform (TQWT). SEEG signals are spatially filtered to isolate the CS artifacts within a few number of sources/components. The artifacted components are then decomposed into oscillatory background and sharp varying transient signals using TQWT. The CS artifact is assumed to lie in the transient part of the signal. Using prior known time-frequency information of the CS artifacts, we selectively mask the wavelet coefficients of the transient signal and extract out any remaining significant electro-physiological activity. Results: We have applied our proposed method of CS artifact suppression on simulated and real SEEG signals with convincing performance. The experimental results indicate the effectiveness of the proposed approach. Conclusion: The proposed method suppresses CS artifacts without affecting the background SEEG signal. Significance: The proposed method can be applied for suppressing both low and high frequency CS artifacts and outperforms current methods from the literature. [ABSTRACT FROM AUTHOR]

Most of the literature on the brain impedance proposes a frequency-independent resistive model. Recently, this conclusion was tackled by a series of papers (Bédard et al., 2006; Bédard and Destexhe, 2009; Gomes et al., 2016), based on microscopic sale modeling and measurements. Our paper aims to investigate the impedance issue using simultaneous in vivo depth and surface signals recorded during intracerebral electrical stimulation of epileptic patients, involving a priori different tissues with different impedances. Our results confirm the conclusions from Logothethis et al. (2007): there is no evidence of frequency dependence of the brain tissue impedance (more precisely, there is no difference, in terms of frequency filtering, between the brain and the skull bone), at least at a macroscopic scale. In order to conciliate findings from both microscopic and macroscopic scales, we recall different neural/synaptic current generators’ models from the literature and we propose an original computational model, based on fractional dynamics. [ABSTRACT FROM AUTHOR]

In difficult epileptic patients, the brain structures are explored by means of depth multicontact electrodes [stereoelectroencephalography (SEEG)]. Recently, a novel diagnostic technique allows an accurate definition of the epileptogenic zone using deep brain stimulation (DBS). The stimulation signal propagates in the brain and thus it appears on most of the other SEEG electrodes, masking the local brain electrophysiological activity. The objective of this paper is the DBS-SEEG signals detrending and denoising in order to recover the masked physiological sources. We review the main filtering methods and put forward an approach based on the combination of filtering with generalized eigenvalue decomposition (GEVD). An experimental study on simulated and real SEEG shows that our approach is able to separate DBS sources from brain activity. The best results are obtained by an original singular spectrum analysis-GEVD approach. [ABSTRACT FROM PUBLISHER]

The goal of this letter is to study convergence conditions for a previously presented iterative wavelet denoising method and to shed light on its relationship with outlier rejection. This method involves a user-defined parameter, which must fulfill certain conditions in order to ensure denoising. Using generalized Gaussian modeling for the wavelet coefficients distribution, we obtain a lower bound for this parameter, and the resulting threshold, both adapted to the shape of the distribution. The properties of this threshold are examined, and the proposed method is compared with other classical rejection methods. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a major public health problem. The retina is a relevant site to indirectly study brain functioning. Alterations in retinal processing were demonstrated in MDD with the pattern electroretinogram (PERG). Here, the relevance of signal processing and machine learning tools applied on PERG was studied. PERG – whose stimulation is reversible checkerboards – was performed according to the International Society for Clinical Electrophysiology of Vision (ISCEV) standards in 24 MDD patients and 29 controls at the inclusion. PERG was recorded every 4 weeks for 3 months in patients. Amplitude and implicit time of P50 and N95 were evaluated. Then, time/frequency features were extracted from the PERG time series based on wavelet analysis. A statistical model has been learned in this feature space and a metric aiming at quantifying the state of the MDD patient has been derived, based on minimum covariance determinant (MCD) mahalanobis distance. MDD patients showed significant increase in P50 and N95 implicit time (p = 0,006 and p = 0,0004, respectively, Mann–Whitney U test) at the inclusion. The proposed metric extracted from the raw PERG provided discrimination between patients and controls at the inclusion (p = 0,0001). At the end of the follow-up at week 12, the difference between the metrics extracted on controls and patients was not significant (p = 0,07), reflecting the efficacy of the treatment. Signal processing and machine learning tools applied on PERG could help clinical decision in the diagnosis and the follow-up of MDD in measuring treatment response. • Retinal function is relevant to study brain function in major depressive disorder. • Retinal dysfunctions were observed with electroretinogram in major depressive disorder. • Signal processing and machine learning tools were applied on electroretinogram data. • Signal processing and machine learning tools can help clinical decision. [ABSTRACT FROM AUTHOR]

Le problème direct repose sur des modèles de milieux de propagation (tissus constitutifs de la tête) s’appuyant sur des équations biophysiques qui relient les sources aux potentiels générés (mesures EEG/SEEG). Ces modèles sont construits à partir, d’une part, des structures cérébrales qui sont en réalité anatomiquement complexes, inhomogènes et anisotrope et, d’autre part, à partir des paramètres de sources également complexes à modéliser en raison de leur dynamique spatio-temporelle. Grâce à l’utilisation en routine clinique des stimulations électriques intracérébrales, qui sont parfaitement déterministes (lieu, amplitude, fréquence,…), on peut déterminer et maîtriser les paramètres de « sources » intracérébrales. Le problème direct se ramène alors à la modélisation des structures cérébrales et de leur conductibilité. Sous certaines hypothèses, nous effectuons une analyse de sensibilité des paramètres du modèle de propagation basée sur des constructions FEM réalistes à 5 compartiments extraits des images CT et IRM des patients explorés en intracérébrale. Nous discuterons les performances de différents modèles de milieux de propagation (FEM, BEM et sphérique) et nous justifierons de leurs limites en fonction de la localisation de la stimulation : profonde, médiane ou latérale. Nous comparons également les résultats obtenus lorsque nous prenons en compte les mesures simultanées d’EEG surface et de profondeur (SEEG). Pour conclure, nous aborderons le problème inverse de localisation de sources à partir de données intracérébrales SEEG en fonction du modèle biophysique et du nombre de sources. Cette étude de localisation de source basée sur la pondération des mesures SEEG au voisinage des sources a été appliquée sur plusieurs activités simultanées sur des données en présence de simulations électrique intracérébrale et plus largement sur des données cliniques. Ces travaux de l’équipe ont fait l’objet de différentes publications [1–3] . [ABSTRACT FROM AUTHOR]