shamo: A tool for electromagnetic modelling, simulation and sensitivity analysis of the head

Accurate electromagnetic modelling of the head of a subject is of main interest in the fields of source reconstruction and brain stimulation. Those processes rely heavily on the quality of the model and, even though the geometry of the tissues can be extracted from magnetic resonance images (MRI) or computed tomography (CT), their physical properties such as the electrical conductivity are hard to measure with non intrusive techniques. In this paper, we propose a tool to assess the uncertainty in the model parameters as well as compute a parametric electroencephalography (EEG) forward solution and current distribution for transcranial direct current stimulation (tDCS).Comment: 8 pages, 5 figure

Studying experimental variability in EEG and tDCS through uncertainty and sensitivity analyses

In neuroscience, simulating electric current in the head of a subject is of main interest for both electroencephalography (EEG) and transcranial direct current stimulation (tDCS). EEG is used to reconstruct the electric activity of the brain based on the measured electric potential on the scalp. On the other hand, tDCS consists in injecting a small electric current through the head of a subject to modulate the activity of a specific brain region. Such simulations rely heavily on the electric conductivity of the biological tissues composing the head. Unfortunately, there is currently no effective and non-invasive method to measure it accurately for each individual. Consequently, researchers and practitioners have to set arbitrary values chosen from the literature, despite the fact that this property has been shown to vary widely both inter- and intra-subject. The simulations also depend on the geometry of the tissues and on how they are modelled. In this thesis, we studied the influence of different skull models and of the electrical conductivity of the tissues on the EEG forward problem. We also analysed the effect of the uncertainty in the conductivity on the electric field induced in different regions of the brain by several stimulating electrode montages in tDCS. To support these experiments, we developed a python package named Shamo which provides the user with tools to perform mesh generation, current simulation, surrogate modelling and sensitivity and uncertainty analyses with a user-friendly API. It interfaces with industrial grade software to perform the computationally intensive tasks and is easy to use on distributed architectures. The present work describes both Shamo and the results that it helped to obtain for the different experiments.Dans le domaine des neurosciences, la simulation du courant électrique dans la tête d’un sujet est d’un intérêt majeur, tant pour l’électroencéphalographie (EEG) que pour la stimulation transcrânienne à courant continu (tDCS). L’EEG est utilisée pour reconstruire l’activité électrique du cerveau à partir du potentiel électrique mesuré sur le cuir chevelu. D’autre part, la tDCS consiste à injecter un petit courant électrique dans la tête d’un sujet pour moduler l’activité d’une région spécifique du cerveau. De telles simulations dépendent de la conductivité électrique des tissus biologiques composant la tête. Malheureusement, il n’existe actuellement aucune méthode efficace et non invasive pour la mesurer avec précision pour chaque individu. Par conséquent, les chercheurs et les praticiens doivent fixer des valeurs arbitraires choisies dans la littérature, malgré le fait qu’il a été démontré que cette propriété varie considérablement entre les sujets et à l’intérieur d’un même sujet. Les simulations dépendent également de la géométrie des tissus et de la façon dont ils sont modélisés. Dans cette thèse, nous avons étudié l’influence de différents modèles de crâne et de la conductivité électrique des tissus sur le problème direct de l’EEG. Nous avons également analysé l’effet de la conductivité sur le champ électrique induit dans différentes régions du cerveau par plusieurs montages d’électrodes en tDCS. Pour soutenir ces expériences, nous avons développé un package python nommé Shamo qui fournit à l’utilisateur des outils pour effectuer la génération de maillage, la simulation de courant, la génération de modèles de substitution et les analyses de sensibilité et d’incertitude avec une API simple. Il s’interface avec des logiciels de qualité industrielle pour effectuer les tâches de calcul intensif et est facile à utiliser sur des architectures distribuées. Ce travail décrit à la fois Shamo et les résultats que cet outil a permis d’obtenir pour les différentes expériences

Shamo v1.0 - Stochastic electromagnetic head modelling made easy

We introduce a Python 3 package: “shamo”. It can perform mesh generation, electromagnetic simulations and sensitivity analysis

Heterogeneity in the links between sleep arousals, amyloid-beta and cognition

peer reviewedBACKGROUND. Tight relationships between sleep quality, cognition and amyloid-beta (Aβ) accumulation, a hallmark of Alzheimer’s disease (AD) neuropathology, emerge in the literature. Sleep arousals become more prevalent with ageing and are considered to reflect poorer sleep quality. Yet, heterogeneity in arousals has been suggested while their associations with Aβ and cognition are not established. METHODS. We recorded undisturbed night-time sleep with EEG in 101 healthy individuals in late midlife (50-70y), devoid of cognitive and sleep disorders. We classified spontaneous arousals according to their association with muscular tone increase (M+/M-) and sleep stage transition (T+/T-). We assessed cortical Aβ burden over earliest affected regions via PET imaging, and cognition via extensive neuropsychological testing. RESULTS. Arousal types differed in their oscillatory composition in theta and beta EEG bands. Furthermore, T+M- arousals, which interrupt sleep continuity, were positively linked to Aβ burden (p=.0053, R²β*=0.08). By contrast, more prevalent T-M+ arousals, upholding sleep continuity, were associated with lower Aβ burden (p=.0003, R²β*=0.13), and better cognition, particularly over the attentional domain (p<.05, R²β*≥0.04). CONCLUSION. Contrasting with what is commonly accepted, we provide empirical evidence that arousals are diverse and differently associated with early AD-related neuropathology and cognition. This suggests that sleep arousals, and their coalescence with other brain oscillations during sleep, may actively contribute to the beneficial functions of sleep. This warrants re-evaluation of age-related sleep changes and suggests that spontaneous arousals could constitute a marker of favourable brain and cognitive health trajectories

Analyse de la sensibilité globale du problème direct en EGG

When carrying out an EEG experiment for source reconstruction, one has to provide both accurate geometry and electrical properties of the head tissues. Authors usually set the electrical conductivities based on values reported in the literature which have been shown to vary widely. Here we propose a method to assess the sensitivity of the EEG forward problem to those parameters using a realistic finite element (FEM) head model including white matter anisotropic tensor. The chosen sensitivity descriptor are the first and total order Sobol indices.EOS Memody

Stochastic HeAd MOdelling

Constructing accurate subject specific head model is of main interest in the fields of source imaging (EEG/MEG) and brain stimulation (tDCS/tMS). shamo is an open source python package to calculate EEG leadfields, current flows, and electric potential distribution in the head. From a labelled 3D image of the head, the whole process is fully automatized, relying only on a few parameter files, e.g. conductivities (including white matter anisotropy) plus source and electrode locations. Since there is no non-invasive method to measure the electromagnetic (EM) properties of the head tissues, shamo can also be used to assess the sensitivity of the EM head model to these parameters.MemoDynFirst official release1.0.