RESUMEN
BACKGROUND: Understanding the pathophysiological dynamics that underline Interictal Epileptiform Events (IEEs) such as epileptic spikes, spike-and-waves or High-Frequency Oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in Local Field Potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. METHOD: a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. RESULTS: Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. CONCLUSION: The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.
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Epilepsia , Modelos Neurológicos , Neocórtex , Neocórtex/fisiopatología , Humanos , Epilepsia/fisiopatología , Animales , Simulación por Computador , ElectroencefalografíaRESUMEN
OBJECTIVE: Microelectrodes allow the recording of neural activities with a high spatial resolution. However, their small sizes result in high impedance causing high thermal noise and poor signal-to-noise ratio. In drug-resistant epilepsy, the accurate detection of Fast Ripples (FRs) can help in the identification of epileptogenic networks. Consequently, good-quality recordings are instrumental in improving surgical outcomes. In this work, we propose a novel model-based approach for the design of microelectrodes optimized for FRs recording. METHODS: A 3D microscale computational model was developed to simulate FRs generated in the hippocampus. It was coupled with a model of the Electrode-Tissue Interface that accounts for the biophysical properties of intracortical microelectrode. This hybrid model was used to analyze the microelectrode geometrical and physical characteristics and their impact on recorded FRs. For model validation, experimental signals (local field potentials, LFPs) were recorded from CA1 using different electrode materials: stainless steel, gold, and gold coated with poly(3,4-ethylene dioxythiophene)/Poly(styrene sulfonate) (Au:PEDOT/PSS). RESULTS: results indicated that a radius between 65 and 120 µm for a wire microelectrode is the most optimal for recording FRs. In addition, in silico and in vivo quantified results showed a possible improvement in FRs observability using PEDOT/PSS coated microelectrodes. CONCLUSION: the optimization of the design of microelectrodes for FRs recording can improve the observability and detectability of FRs which are a recognized marker of epileptogenicity. SIGNIFICANCE: This model-based approach can assist in the design of hybrid electrodes that can be used in the presurgical evaluation of epileptic patients with drug-resistant epilepsy.
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Epilepsia , Polímeros , Humanos , Microelectrodos , Electrodos Implantados , Epilepsia/diagnóstico , Epilepsia/cirugía , OroRESUMEN
Cortical folding pattern is a main characteristic of the geometry of the human brain which is formed by gyri (ridges) and sulci (grooves). Several biological hypotheses have suggested different mechanisms that attempt to explain the development of cortical folding and its abnormal evolutions. Based on these hypotheses, biomechanical models of cortical folding have been proposed. In this work, we compare biomechanical simulations for several initial conditions by using an adaptive spherical parameterization approach. Our approach allows us to study and explore one of the most potential sources of reproducible cortical folding pattern: the specification of initial geometry of the brain.
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Encéfalo , Humanos , Imagen por Resonancia Magnética , MorfogénesisRESUMEN
High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level.
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Algoritmos , Electromiografía , Procesamiento de Señales Asistido por Computador , Simulación por Computador , Humanos , Contracción Isométrica/fisiología , Masculino , Relación Señal-Ruido , Adulto JovenRESUMEN
The Brachialis (BR) is placed under the Biceps Brachii (BB) deep in the upper arm. Therefore, the detection of the corresponding surface Electromyogram (sEMG) is a complex task. The BR is an important elbow flexor, but it is usually not considered in the sEMG based force estimation process. The aim of this study was to attempt to separate the two sEMG activities of the BR and the BB by using a High Density sEMG (HD-sEMG) grid placed at the upper arm and Canonical Component Analysis (CCA) technique. For this purpose, we recorded sEMG signals from seven subjects with two 8 × 4 electrode grids placed over BB and BR. Four isometric voluntary contraction levels were recorded (5, 10, 30 and 50 %MVC) for 90° elbow angle. Then using CCA and image processing tools the sources of each muscle activity were separated. Finally, the corresponding sEMG signals were reconstructed using the remaining canonical components in order to retrieve the activity of the BB and the BR muscles.
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Electromiografía/métodos , Músculo Esquelético , Brazo , Codo , Humanos , Contracción Isométrica , Contracción MuscularRESUMEN
The aim of the present study is to propose a subject-specific screening approach of High Density surface EMG (HD-sEMG) Probability Density Function (PDF) shape evolution in experimental conditions following a ramp exercise from 0% to 50% of the Maximum Voluntary Contraction (MVC) during 25 seconds of isometric contractions of the Biceps Brachii from six healthy subjects. This method uses High Order Statistics (HOS), namely the kurtosis and the skewness for PDF shape screening examined on selectively positioned Laplacian sEMG channels obtained on an 8×8 HD-sEMG grid. For each subject, the position of the Laplacian channels was chosen based on the level of muscle activation obtained from the Signal to Noise Ratio (SNR) matrix computed for the 64 sEMG signals of the grid in order to obtain independent Laplacian configurations localized in areas with high SNRs indicating high muscle activation. Afterwards, we used the Principal Component Analysis (PCA) to obtain the principal trend of the kurtosis and the skewness computed from the selected Laplacian signals according to force level variation. The obtained results show a globally common increasing HOS trend according to force increase from 0% to 50% MVC for all the subjects regardless of the anatomical, instrumental and physiological variability that usually strongly influences these trends.
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Electromiografía/métodos , Músculo Esquelético/fisiología , Femenino , Humanos , Contracción Isométrica/fisiología , Masculino , Fuerza Muscular , Procesamiento de Señales Asistido por Computador , Relación Señal-Ruido , Adulto JovenRESUMEN
Recent studies pointed out possible shape modifications of the Probability Density Function (PDF) of surface electromyographical (sEMG) data according to several contexts like fatigue and muscle force increase. Following this idea, criteria have been proposed to monitor these shape modifications mainly using High Order Statistics (HOS) parameters like skewness and kurtosis. In experimental conditions, these parameters are confronted with small sample size in the estimation process. This small sample size induces errors in the estimated HOS parameters restraining real-time and precise sEMG PDF shape monitoring. Recently, a functional formalism, the Core Shape Model (CSM), has been used to analyse shape modifications of PDF curves. In this work, taking inspiration from CSM method, robust functional statistics are proposed to emulate both skewness and kurtosis behaviors. These functional statistics combine both kernel density estimation and PDF shape distances to evaluate shape modifications even in presence of small sample size. Then, the proposed statistics are tested, using Monte Carlo simulations, on both normal and Log-normal PDFs that mimic observed sEMG PDF shape behavior during muscle contraction. According to the obtained results, the functional statistics seem to be more robust than HOS parameters to small sample size effect and more accurate in sEMG PDF shape screening applications.