Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity

Lattice Element Method and its application to Multiphysics

In this thesis, a Lattice element modelling method is developed and is applied to model the loose and cemented, natural and artificial, granular matters subject to thermo-hydro-mechanical coupled loading conditions. In lattice element method, the lattice nodes which can be considered as the centres of the unit cells, are connected by cohesive links, such as spring beams that can carry normal and shear forces, bending and torsion moment. For the heat transfer due to conduction, the cohesive links are also used to carry heat as 1D pipes, and the physical properties of these rods are computed based on the Hertz contact model. The hydro part is included with the pore network modelling scheme. The voids are inscribed with the pore nodes and connected with throats, and then the meso level flow equation is solved. The Euler-Bernoulli and Timoshenko beams are chosen as the cohesive links or the lattice elements, while the latter should be used when beam elements are short and deep. This property becomes interesting in modelling auxetic materials. The model is applied to study benchmarks in geotechnical engineering. For heat transfer in the dry and full range of saturation, and fractures in the cemented granular media.How through porous media failure behaviours of rocks at high temperature and pressure and granular composites subjected to coupled Thermo hydro Mechanical loads. The model is further extended to capture the wave motion in the heterogeneous granular matter, and a few case studies for the wavefield modification with existing cracks are presented. The developed method is capable of capturing the complex interaction of crack wave interaction with relative ease and at a substantially less computational cost

La nanoélectronique pour l'interfaçage neuronal : des nanofils de silicium à des dispositifs de carbone

In line with the technological progress of last decades a variety of adapted bioelectrical interfaces was developed to record electrical activity from the nervous system reaching from whole brain activity to single neuron signaling. Although neural interfaces have reached clinical utility and are commonly used in fundamental neuroscience, their performance is still limited. In this work we investigated alternative materials and techniques, which could improve the monitoring of neuronal activity of cultured networks, and the long-term performance of prospective neuroprosthetics. While silicon nanowire transistor arrays and diamond based microelectrodes are proposed for improving the spatial resolution and the electrode stability in biological environment respectively, the main focus of this thesis is set on the evaluation of graphene based field effect transistor arrays for bioelectronics. Due to its outstanding electrical, mechanical and chemical properties graphene appears as a promising candidate for the realization of chemically stable flexible electronics required for long-term neural interfacing. Here we demonstrate the outstanding neural affinity of pristine graphene and the realization of highly sensitive fast graphene transistors for neural interfaces.Dans la lignée des progrès technologiques récents en électronique, ces dernières décennies ont vu l’émergence d’une variété de systèmes permettant l’interface bioélectronique, allant de la mesure de l’activité électrique émise par l’ensemble du cerveau jusqu’à la mesure du signal émis par un neurone unique. Bien que des interfaces électroniques avec les neurones ont montré leur utilité pour des applications cliniques et sont communément utilisés par les neurosciences fondamentales, leurs performances sont encore très limitées, notamment en raison de l’incompatibilité relative entre les systèmes à l’état solide et le vivant. Dans ce travail de thèse, nous avons étudié des techniques et des matériaux nouveaux permettant une approche alternative et qui pourraient améliorer le suivi de l’activité de réseaux de neurones cultivés in situ et à terme la performance des neuroprothèses in vivo. Dans ce travail, des réseaux de nanofils de silicium et des microélectrodes en diamant sont élaborés pour respectivement améliorer la résolution spatiale et la stabilité des électrodes dans un environnement biologique. Un point important de cette thèse est également l’évaluation des performances de transistors à effet de champ en graphène pour la bio électronique. En raison des performances remarquables et combinées sur les aspects électrique, mécanique et chimique du graphène, ce matériau apparaît comme un candidat très prometteur pour la réalisation d’une électronique permettant une interface stable et sensible avec un réseau de neurones. Nous montrons dans ce travail l’affinité exceptionnelle des neurones avec une surface de graphène brut et la réalisation d’une électronique de détection rapide et sensible à base de transistor en graphène

Machine learning based computational models with permeability for white matter microstructure imaging

Characterising tissue microstructure is of paramount importance for understanding neurological conditions such as Multiple Sclerosis. Therefore, there is a growing interest in imaging tissue microstructure non-invasively. One way to achieve this is by developing tissue models and fitting them to the diffusion-MRI signal. Nevertheless, some microstructure parameters, such as permeability, remain elusive because analytical models that incorporate them are intractable. Machine learning based computational models offer a promising alternative as they bypass the need for analytical expressions. The aim of this thesis is to develop the first machine learning based computational model for white matter microstructure imaging using two promising approaches: random forests and neural networks. To test the feasibility of this new approach, we provide for the first time a direct comparison of machine learning parameter estimates with histology. In this thesis, we demonstrate the idea by estimating permeability via the intra-axonal exchange time τ_i, a potential imaging biomarker for demyelinating pathologies. We use simulations of the diffusion-MRI signal to construct a mapping between signals and microstructure parameters including τ_i. We show for the first time that clinically viable diffusion-weighted sequences can probe exchange times up to approximately 1000 ms. Using healthy in-vivo human and mouse data, we show that our model's estimates are within the plausible range for white matter tissue and display well known trends such as the high-low-high intra-axonal volume fraction f across the corpus callosum. Using human and mouse data from demyelinated tissue, we show that our model detects trends in line with the expected MS pathology: a significant decrease in f and τ_i. Moreover, we show that our random forest estimates of f and τ_i correlate very strongly with histological measurements of f and myelin thickness. This thesis demonstrates that machine learning based computational models are a feasible approach for white matter microstructure imaging. The continually improving SNR in the clinical scanners and the availability of more realistic simulations open up possibilities of using such models as imaging biomarkers for demyelinating diseases such as Multiple Sclerosis

Real-Time Artifacts Reduction during TMS-EEG Co-Registration: A Comprehensive Review on Technologies and Procedures

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field

Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

Renewable Energy Resource Assessment and Forecasting

In recent years, several projects and studies have been launched towards the development and use of new methodologies, in order to assess, monitor, and support clean forms of energy. Accurate estimation of the available energy potential is of primary importance, but is not always easy to achieve. The present Special Issue on ‘Renewable Energy Resource Assessment and Forecasting’ aims to provide a holistic approach to the above issues, by presenting multidisciplinary methodologies and tools that are able to support research projects and meet today’s technical, socio-economic, and decision-making needs. In particular, research papers, reviews, and case studies on the following subjects are presented: wind, wave and solar energy; biofuels; resource assessment of combined renewable energy forms; numerical models for renewable energy forecasting; integrated forecasted systems; energy for buildings; sustainable development; resource analysis tools and statistical models; extreme value analysis and forecasting for renewable energy resources

Volume Electron Microscopic Analyses in the Larval Zebrafish

The goal of this work was two-fold: 1) To apply serial block-face electron microscopy (SBEM) to the spinal cord of a larval zebrafish, in order to gain a mechanistic understanding of motoneuron (MN) recruitment, based on a reconstruction of the wiring between spinal interneurons and MNs and 2) to implement technological improvements to SBEM that would allow datasets to be acquired at much higher speed, in order to acquire a dataset of a complete larval zebrafish brain. The spinal cord of vertebrates contains a neural circuit known as a central pattern generator (CPG), which can generate the rhythmic muscle contractions underlying locomotion independently of the brain. In fish, the rhythm consists of muscle contractions that alternate between the left and right side of the tail and that travel down the length of the fish, from head to tail. When swimming fast, such as during escapes, the rhythm has a high frequency and muscles contract vigorously. During slow, routine swimming, the rhythm has a low frequency and muscles contract with less strength. The MNs in the spinal cord, which elicit the contractions of the tail musculature, are recruited to different degrees during these different behaviors. With increasing contraction strength, more and larger MNs are activated. This phenomenon is called orderly recruitment. The rhythmic excitation that recruits MNs is provided by Circumferential Descending (CiD) interneurons located in the spinal cord. These interneurons also follow a specific recruitment pattern: During weak swimming, ventral cells are active exclusively and dorsal cells are silent. As swims increase in vigor, the activity in these cells shifts towards more dorsal cells, with more ventral cells becoming inactive. The aim of the first part of this thesis was to reconstruct the MNs along with the CiDs that excite them, using a high resolution SBEM dataset of the spinal cord, to identify the pattern of connectivity between these types of neurons and distinguish between competing hypotheses of orderly MN recruitment. Conceptually, orderly recruitment could either be implemented with unspecific connectivity, in which case it would be a consequence of the interplay of size-dependent biophysical properties (in particular the input resistance) with the strengths of the synapses driving them. Alternatively, the wiring pattern could be specific and the CiDs could select the subset of MNs to activate by making synapses with just those cells. MNs in the larval zebrafish spinal cord clustered into distinct subtypes, depending on their size: Small, intermediate and large. The small MNs received almost no synaptic inputs and appeared to be immature. CiDs differentially innervated the intermediate and large MNs: Ventrally located CiDs did not differentiate between the two subtypes, but the dorsal CiDs made synapses onto large MNs with high specificity. Since dorsal CiDs are active only during the fastest swims, this finding can be interpreted as a labeled line specifically recruiting the strongest MNs during the most vigorous behaviors. During weaker behaviors, when the dorsal CiDs are inactive and the more ventral ones are active exclusively, differences in MN excitability due to size would encode the recruitment order. The second objective was to improve SBEM technology to acquire a whole larval zebrafish brain in a relatively short period of time. Due to the very high resolution required to trace small neurites and to identify synapses, even very small brains, such as the brain of a larval zebrafish, would take many months to acquire using a typical SBEM setup. Two main techniques were used to increase net speed. First, line-scanning of individual image tiles was implemented, where the electron beam scans the image in one axis only and the other axis is scanned by moving the stage. This allows larger individual images to be taken, greatly reducing the number of motor moves between images. Second, dynamic adaptation of the image tile mosaic to the shape of the sample was used to avoid scanning the blank plastic regions surrounding an irregularly shaped sample. These improvements allowed the complete brain of a 5 day old larval zebrafish to be imaged in less than 30% of the time than would have been required previously. In a collaborative project with Dr. Fumi Kubo, two-photon calcium imaging was performed prior to EM imaging, revealing pretectal cells active during optokinetic stimulation. The two-photon dataset was successfully registered to the EM data and a functionally identified pretectal cell could be traced. This dataset will be used to reconstruct the complete neural networks that compute the optokinetic response