Fast Approximation of EEG Forward Problem and Application to Tissue Conductivity Estimation

Bioelectric source analysis in the human brain from scalp electroencephalography (EEG) signals is sensitive to the conductivity of the different head tissues. Conductivity values are subject dependent, so non-invasive methods for conductivity estimation are necessary to fine tune the EEG models. To do so, the EEG forward problem solution (so-called lead field matrix) must be computed for a large number of conductivity configurations. Computing one lead field requires a matrix inversion which is computationally intensive for realistic head models. Thus, the required time for computing a large number of lead fields can become impractical. In this work, we propose to approximate the lead field matrix for a set of conductivity configurations, using the exact solution only for a small set of basis points in the conductivity space. Our approach accelerates the computing time, while controlling the approximation error. Our method is tested for brain and skull conductivity estimation , with simulated and measured EEG data, corresponding to evoked somato-sensory potentials. This test demonstrates that the used approximation does not introduce any bias and runs significantly faster than if exact lead field were to be computed.Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to [email protected]

L’anémie de Fanconi : gènes et fonction(s) revisités

Des mutations dans les gènes FANC sont responsables de l’anémie de Fanconi (AF), une maladie génétique de phénotype complexe incluant une pancytopénie, des malformations congénitales et une prédisposition élevée au cancer. L’augmentation par les agents pontant l’ADN de la fréquence des aberrations chromosomiques, une caractéristique de l’AF, est utilisée pour le diagnostic. Parmi les onze gènes FANC, neuf sont identifiés. Huit de ces gènes sont localisés sur des autosomes, tandis que FANCB est situé sur le chromosome X. L'un des gènes FANC est BRCA2, impliqué dans la prédisposition génétique au cancer du sein et/ou de l’ovaire. Sept des protéines FANC s’associent pour former un complexe à géométrie variable dépendant de sa localisation subcellulaire, tandis que FANCD1 (BRCA2), FANCD2, FANCI et FANCJ ne sont pas associées au complexe. La mono-ubiquitinylation de FANCD2, dépendante du complexe, jouerait un rôle important dans la gestion des pontages de l’ADN. Les protéines FANC et BRCA1, étroitement associées, participent, entre autres, avec les protéines ATM, NBS1 et ATR, à un réseau multiprotéique impliqué dans la détection, la signalisation et la réparation des lésions bloquant la réplication de l’ADN.Fanconi anemia (FA), a rare inherited disorder, exhibits a complex phenotype including progressive bone marrow failure, congenital malformations and increased risk of cancers, mainly acute myeloid leukaemia. At the cellular level, FA is characterized by hypersensitivity to DNA cross-linking agents and by high frequencies of induced chromosomal aberrations, a property used for diagnosis. FA results from mutations in one of the eleven FANC(FANCA to FANCJ) genes. Nine of them have been identified. In addition, FANCD1 gene has been shown to be identical to BRCA2, one of the two breast cancer susceptibility genes. Seven of the FANC proteins form a complex, which exists in four different forms depending of its subcellular localisation. Four FANC proteins (D1(BRCA2), D2, I and J) are not associated to the complex. The presence of the nuclear form of the FA core complex is necessary for the mono-ubiquitinylation of FANCD2 protein, a modification required for its re-localization to nuclear foci, likely to be sites of DNA repair. A clue towards understanding the molecular function of the FANC genes comes from the recently identified connection of FANC to the BRCA1, ATM, NBS1 and ATR genes. Two of the FANC proteins (A and D2) directly interact with BRCA1, which in turn interacts with the MRE11/RAD50/NBS1 complex, which is one of the key components in the mechanisms involved in the cellular response to DNA double strand breaks (DSB). Moreover, ATM, a protein kinase that plays a central role in the network of DSB signalling, phosphorylates in vitro and in vivo FANCD2 in response to ionising radiations. Moreover, the NBS1 protein and the monoubiquitinated form of FANCD2 seem to act together in response to DNA crosslinking agents. Taken together with the previously reported impaired DSB and DNA interstrand crosslinks repair in FA cells, the connection of FANC genes to the ATM, ATR, NBS1 and BRCA1 links the FANC genes function to the finely orchestrated network involved in the sensing, signalling and repair of DNA replication-blocking lesions

De Gaulle's charisma as a public relations factor in implementing his political philosophy

Thesis (M.S.)--Boston UniversityThis study has been undertaken to study what role charismatic characteristics in a man play in Public Relations. It is my contention that when such supernatural gifts are found in a person, then this person need not follow -- indeed as in the case of de Gaulle does not follow -- the traditional channels of P.R. to achieve his ends. His charisma takes the place of a planned Public Relations program in molding Public Opinion. It acts as a channel of communications between the leader and the masses. It is an accepted fact that politcal leaders -- more so than other types (religious, educational, cultural) -- come to be what they are with the tide of historical circumstances. In times of crisis these leaders are selected from others -- maybe with similar personalities -- but with certain specific gifts of the body and spirit -- gifts that have been believed to be supernatural, and not accessible to everybody [TRUNCATED

Jitter-Adaptive Dictionary Learning - Application to Multi-Trial Neuroelectric Signals

Dictionary Learning has proven to be a powerful tool for many image processing tasks, where atoms are typically defined on small image patches. As a drawback, the dictionary only encodes basic structures. In addition, this approach treats patches of different locations in one single set, which means a loss of information when features are well-aligned across signals. This is the case, for instance, in multi-trial magneto- or electroencephalography (M/EEG). Learning the dictionary on the entire signals could make use of the alignement and reveal higher-level features. In this case, however, small missalignements or phase variations of features would not be compensated for. In this paper, we propose an extension to the common dictionary learning framework to overcome these limitations by allowing atoms to adapt their position across signals. The method is validated on simulated and real neuroelectric data.Comment: 9 pages, 5 figures, minor correction

Le cerveau dans tous ses états. Des sciences cognitives au diagnostic : entretien avec Stéphane Lehéricy propos recueillis par Dominique Chouchan

Article suivi par un entretien "des sciences cognitives au diagnostic" avec Stéphane Lehéricy directeur du Centre de neuro-imagerie de recherche (CENIR) du CHU Pitié Salpêtrière et professeur dans le service de neuroradiologie de ce CHU. Propos recueillis par Dominique ChouchanNational audienceChacun de nos quelque 100 milliards de neurones peut communiquer avec des milliers d'autres : autant dire qu'à ce jour, le cerveau est pour l'essentiel terra incognita. On sait qu'il comporte des aires spécialisées (dans la vision, la marche, les émotions...) dites corticales, qui constituent la matière grise. Celles-ci s'échangent des messages, électriques notamment, au travers de fibres nerveuses, la substance blanche. La compréhension de l'anatomie du cerveau (structure spatiale) et de sa réponse à des stimuli (approche temporelle) vont donc de pair. Aujourd'hui, nous disposons de techniques de mesure et d'imagerie performantes. Mais encore faut-il interpréter les données obtenues. Un défi qui nécessite d'étroites collaborations entre mathématiciens, informaticiens, spécialistes des neurosciences et médecins

An embedding for EEG signals learned using a triplet loss

Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing brain state of a patient or healthy user. In a brain-computer interface (BCI), this decoded brain state information can be used with minimal time delay to either control an application, e.g., for communication or for rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e.g., in a demanding work environment. A specific challenge in such decoding tasks is posed by the small dataset sizes in BCI compared to other domains of machine learning like computer vision or natural language processing. A possibility to tackle classification or regression problems in BCI despite small training data sets is through transfer learning, which utilizes data from other sessions, subjects or even datasets to train a model. In this exploratory study, we propose novel domain-specific embeddings for neurophysiological data. Our approach is based on metric learning and builds upon the recently proposed ladder loss. Using embeddings allowed us to benefit, both from the good generalisation abilities and robustness of deep learning and from the fast training of classical machine learning models for subject-specific calibration. In offline analyses using EEG data of 14 subjects, we tested the embeddings' feasibility and compared their efficiency with state-of-the-art deep learning models and conventional machine learning pipelines. In summary, we propose the use of metric learning to obtain pre-trained embeddings of EEG-BCI data as a means to incorporate domain knowledge and to reach competitive performance on novel subjects with minimal calibration requirements.Comment: 23 pages, 11 figures, 5 appendix pages, 6 appendix figures, work conducted in 2020-2021 during an ARPE (https://ens-paris-saclay.fr/en/masters/ens-paris-saclay-degree/year-pre-doctoral-research-abroad-arpe

Topography-Time-Frequency Atomic Decomposition for Event-Related M/EEG Signals.

International audienceWe present a method for decomposing MEG or EEG data (channel x time x trials) into a set of atoms with fixed spatial and time-frequency signatures. The spatial part (i.e., topography) is obtained by independent component analysis (ICA). We propose a frequency prewhitening procedure as a pre-processing step before ICA, which gives access to high frequency activity. The time-frequency part is obtained with a novel iterative procedure, which is an extension of the matching pursuit procedure. The method is evaluated on a simulated dataset presenting both low-frequency evoked potentials and high-frequency oscillatory activity. We show that the method is able to recover well both low-frequency and high-frequency simulated activities. There was however cross-talk across some recovered components due to the correlation introduced in the simulation

Modelling thin tissue compartiments using the immersed FEM (continuous Galerkin)

International audienceThis presentation describes a trilinear immersed finite element method for solving the electroencephalography forward problem, which is a three-dimensional elliptic interface problem in the head geometry. The method uses hexahedral Cartesian meshes (i.e. 3D images which can be explored using standard visualization tools for MR images) independent of the interfaces between head tissues, thus avoiding the sometimes difficult task of generating geometry fitting meshes (which is exacerbated for child brains which contains close interfaces requiring a very high number of elements to obtain numerically good mesh representations). Brain interfaces are provided as level sets representations, which are also 3D images. Such levelset representations can directly be used in head segmentation tools but can be also easily obtained from meshes. The finite element space is locally modified to better approximating the continuity properties of the solution (continuous potential and normal currents despite a discontinuity of the conductivity). Numerical results show that this method achieves the same accuracy as the standard linear finite element method with geometry fitting meshes without the hassle of creating meshes for the complex domain that is the head

Quantitative comparisons of forward problems in MEEG.

This document gives comparisons between several methods that solve the forward problem in MEEG by comparing their precision on a 3-layer spherical model. These methods are based on finite elements which either use surfacic meshes with triangles, volumic meshes with tetrahedra, or implicit elements deduced from levelsets