Classification des pointes épileptiques en électro-magnéto-encéphalographie

L'électroencéphalographie (EEG) et la magétoencéphalographie (MEG) sont des outils essentiels de diagnostic et de traitement de l'épilepsie. Elles permettent d'observer des événements fortement associés à l'épilepsie, les pointes épileptiques. Ces pointes permettent entre autres de localiser la région du cerveau qui cause les symptômes de l'épilepsie. Toutefois, pour obtenir une localisation précise, les signaux EEG et MEG qui contiennent les pointes doivent avoir un rapport signal sur bruit (SNR) élevé. Une technique qui permet d'augmenter le SNR est de faire la moyenne de plusieurs signaux similaires. Cependant, comment peut-on s'assurer que les signaux sont suffisamment similaires pour en faire la moyenne? La solution consiste à effectuer la classification des pointes épileptiques. Ce mémoire présente la méthodologie et son évaluation, de la conception d'une nouvelle technique de classification de pointes épileptiques mesurées en EEG et en MEG. Parce que cette nouvelle technique utilise la représentation des pointes dans l'espace des sources, elle permet de classifier des pointes morphologiquement similaires, mais provenant de sources distinctes. La performance de cet algorithme a été évaluée sur des signaux EEG et MEG simulés. Les résultats indiquent que la technique proposée permet de grouper les pointes qui possèdent une représentation dans l'espace des sources similaires. L'utilisation de l'algorithme sur des signaux épileptiques réels a permis de trouver des régions actives du cerveau qui n'apparaissaient pas lors de l'analyse traditionnelle

Estimation of Axonal Conduction Speed and the Inter Hemispheric Transfer Time using Connectivity Informed Maximum Entropy on the Mean

International audienceThe different lengths and conduction velocities of axons connecting cortical regions of the brain yield information transmission delays which are believed to be fundamental to brain dynamics. A critical step in the estimation of axon conduction speed in vivo is the estimation of the inter hemispheric transfer time (IHTT). The IHTT is estimated using electroencephalography (EEG) by measuring the latency between the peaks of specific electrodes or by computing the lag to maximum correlation on contra lateral electrodes. These approaches do not take the subject's anatomy into account and, due to the limited number of electrodes used, only partially leverage the information provided by EEG. Using the previous published Connectivity Informed Maximum Entropy on the Mean (CIMEM) method, we propose a new approach to estimate the IHTT. In CIMEM, a Bayesian network is built using the structural connectivity information between cortical regions. EEG signals are then used as evidence into this network to compute the posterior probability of a connection being active at a particular time. Here, we propose a new quantity which measures how much of the EEG signals are supported by connections, which is maximized when the correct conduction delays are used. Using simulations, we show that CIMEM provides a more accurate estimation of the IHTT compared to the peak latency and lag to maximum correlation methods

Estimation of Axon Conduction Delay, Conduction Speed, and Diameter from Information Flow using Diffusion MRI and MEG

Data were provided by the Human Connectome Project (HCP), WU-MinnConsortium (Principal Investigators: David Van Essen and Kamil Ugurbil;1U54MH091657) funded by the 16 NIH Institutes and Centers that supportthe NIH Blueprint for Neuroscience Research; and by the McDonnell Center forSystems Neuroscience at Washington UniversityInternational audienc

A myoelectric digital twin for fast and realistic modelling in deep learning

Muscle electrophysiology has emerged as a powerful tool to drive human machine interfaces, with many new recent applications outside the traditional clinical domains, such as robotics and virtual reality. However, more sophisticated, functional, and robust decoding algorithms are required to meet the fine control requirements of these applications. Deep learning has shown high potential in meeting these demands, but requires a large amount of high-quality annotated data, which is expensive and time-consuming to acquire. Data augmentation using simulations, a strategy applied in other deep learning applications, has never been attempted in electromyography due to the absence of computationally efficient models. We introduce a concept of Myoelectric Digital Twin - highly realistic and fast computational model tailored for the training of deep learning algorithms. It enables simulation of arbitrary large and perfectly annotated datasets of realistic electromyography signals, allowing new approaches to muscular signal decoding, accelerating the development of human-machine interfaces

A Paradigm Free Regularization Approach to Recover Brain Activations: Validation on Task fMRI

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Towards validation of diffusion MRI tractography: bridging the resolution gap with 3D Polarized Light Imaging

International audienceThree-dimensional Polarized Light Imaging (3D-PLI) is an optical approach presented as a good candidate for validation of diffusion Magnetic Resonance Imaging (dMRI) results such as orientation estimates (fiber Orientation Distribution Functions) and tractography. We developed an anlytical approach to reconstruct fiber ODFs from 3D-PLI datasets. From these fODFs, here we compute brain fiber tracts via dMRI-based probabilistic tractography algorithm. Reconstructed fODFs at different scales proves the ability to bridge the resolution gap between 3D-PLI and dMRI, demonstrating, therefore, a great promise to validate diffusion MRI tractography thanks to multi-scale fiber tracking based on 3D-PLI

Utiliser les fibres de matière blanche comme le modèle de sources pour le problème inverse MEG

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Investigating the effect of DMRI signal representation on fully-connected neural networks brain tissue microstructure estimation

International audienceIn this work, we evaluate the performance of three different diffusion MRI (dMRI) signal representations in the estimation of brain microstructural indices in combination with fully connected neural networks (FC-NN). The considered signal representations are the raw samples on the sphere, the spherical harmonics coefficients, and a novel set of recently presented rotation invariant features (RIF). To train FC-NN and validate our results, we create a synthetic dMRI dataset that mimics the signal properties of brain tissues and provides us a real ground truth for our experiments. We test 8 different network configurations changing both the depth of the networks and the number of perceptrons. Results show that our new RIF are able to estimate the brain microstructural indices more precisely than the diffusion signal samples or its spherical harmonics coefficients in all the tested network configurations. Finally, we apply the best-performing FC-NN in-vivo on a healthy human brain

Novel 4-D Algorithm for Functional MRI Image Regularization using Partial Differential Equations

International audienceState-of-the-art techniques for denoising functional MRI (fMRI) images consider the problems of spatial and temporal regularization as decoupled tasks. In this work we propose a partial differential equations (PDEs)-based algorithm that acts directly on the 4-D fMRI image. Our approach is based on the idea that large image variations should be preserved as they occur during brain activation, but small variations should be smoothed to remove noise. Starting from this principle, by means of PDEs we were able to smooth the fMRI image with an anisotropic regularization, thus recovering the location of the brain activations in space and their timing and duration

Forward modelling of M/EEG: Towards a new automatic head and brain tissue segmentation system

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