Stability of ICA decomposition across within-subject EEG datasets.

International audienceIndependent Component Analysis (ICA) has been successfully used to identify brain related signals and artifacts from multi-channel electroencephalographic (EEG) data. However the stability of ICA decompositions across sessions from a single subject has not been investigated. The goal of this study was to isolate EEG independent components (ICs) across sessions for each subject so as to assess whether ICs are reproducible across sessions. We used 64-channel EEG data recorded from two subjects during a simple mind-wandering experiment. Each subject participated in 11 twenty-minute sessions over a period of five weeks. Extended Infomax ICA decomposition was performed on the continuous data of each session. We used a simple IC clustering technique based on correlation of scalp topographies. Several clusters of homogenous components were identified for each subject. Typical component clusters accounting for eye movement and eye blink artifacts were identified. Both clusters included one component from each recording session. In addition, several clusters corresponding to brain electrical sources, among them clusters exhibiting prominent alpha, beta and Mu band activities, included components from most sessions. These results present evidence that ICA can provide relatively stable solutions across sessions, with important implications for Brain Computer Interface research

Dietary Phenylalanine Requirement of Fingerling Oreochromis Niloticus (Linnaeus)

This study was conducted to determine the dietary phenylalanine for fingerling Oreochromis niloticus by conducting an 8 weeks experiment in a flow-through system (1-1.5L/min) at 28°C water temperature. Phenylalanine requirement was determined by feeding six casein-gelatin based amino acid test diets (350 g kg– 1 CP; 16.72 kJ g–1 GE) with graded levels of phenylalanine (4, 6.5, 9, 11.5, 14 and 16.5 g kg–1 dry diet) at a constant level (10 g kg–1) of dietary tyrosine to triplicate groups of fish (1.65±0.09 g) near to satiation. Absolute weight gain (AWG g fish-1), feed conversion ratio (FCR), protein deposition (PD%), phenylalanine retention efficiency (PRE%) and RNA/DNA ratio was found to improve with the increasing concentrations of phenylalanine and peaked at 11.5 g kg–1 of dry diet. Quadratic regression analysis of AWG, PD and PRE against varying levels of dietary phenylalanine indicated the requirement at 12.1, 11.6, and 12.7 g kg–1 dry diet, respectively and the inclusion of phenylalanine at 12.1 g kg–1 of dry diet, corresponding to 34.6 g kg–1 dietary protein is optimum for this fish. Based on above data, total aromatic amino acid requirement of fingerling O. niloticus was found to be 20.6 g kg–1 (12.1 g kg–1 phenylalanine+8.5 g kg–1 tyrosine) of dry diet, corresponding to 58.8 g kg–1 of dietary protein

SLEEPING WHILE AWAKE: A NEUROPHYSIOLOGICAL INVESTIGATION ON SLEEP DURING WAKEFULNESS.

Il sonno e la veglia vengono comunemente considerati come due stati distinti. L\u2019alternanza tra essi, la cui presenza \ue8 stata dimostrata in ogni specie animale studiata fino ad oggi, sembra essere una delle caratteristiche che definisce la nostra vita. Allo stesso tempo, per\uf2, le scoperte portate alla luce negli ultimi decenni hanno offuscato i confini tra questi due stati. I meccanismi del sonno hanno sempre affascinato i neurofisiologi, che infatti, nell\u2019ultimo secolo, li hanno caratterizzati in dettaglio: ora sappiamo che all\u2019attivit\ue0 del sonno sottost\ue0 una specifica attivit\ue0 neuronale chiamata slow oscillation. La slow oscillation, che \ue8 costituita da (ancora una volta) un\u2019alternanza tra periodi di attivit\ue0 e periodi di iperpolarizzazione e silenzio neuronale (OFF-periods), \ue8 la modalit\ue0 base di attivazione del cervello dormiente. Questa alternanza \ue8 dovuta alla tendenza dei neuroni surante lo stato di sonno, di passare ad un periodo silente dopo un\u2019attivazione iniziale, una tendenza a cui viene dato il nome di bistabilit\ue0 neuronale. Molti studi hanno dimostrato come la bistabilit\ue0 neuronale tipica del sonno ed i relativi OFF-periods, possano accadere anche durante la veglia in particolari condizioni patologiche, nelle transizioni del sonno e durante le deprivazioni di sonno. Per questo motivo, se accettassimo che la bistabilit\ue0 neuronale e gli OFF-periods rappresentino una caratteristica fondamentale del sonno, allora dovremmo ammettere che stiamo assistendo ad un cambio di paradigma: da una prospettiva neurofisiologica il sonno pu\uf2 intrudere nella veglia. In questa tesi ho analizzato i nuovi -fluidi- confini tra sonno e veglia e le possibili implicazioni di questi nel problema della persistenza personale attraverso il tempo. Inoltre, ho studiato le implicazioni cliniche dell\u2019intrusione di sonno nella veglia in pazienti con lesioni cerebrali focali di natura ischemica. In particolare, i miei obiettivi sono stati: 1) Dimostrare come la bistabilit\ue0 neuronale possa essere responsabile della perdita di funzione nei pazienti affetti da ischemia cerebrale e come questo potrebbe avere implicazioni nello studio della patofisiologia dell\u2019ischemia cerebrale e nella sua terapia; 2) Stabilire le basi per un modello di sonno locale presente nella vita di tutti i giorni: la sensazione di sonnolenza. Infatti, essa potrebbe riflettere la presenza di porzioni di corteccia in stato di sonno, ma durante lo stato di veglia; 3) Difendere il criterio biologico di identit\ue0, che troverebbe nell\u2019attivit\ue0 cerebrale la continuit\ue0 necessaria al mantenimento della nostra identit\ue0 nel tempo.Sleep and wakefulness are considered two mutually exclusive states. The alternation between those two states seems to be a defining characteristic of our life, a ubiquitous phenomenon demonstrated in every animal species investigated so far. However, during the last decade, advances in neurophysiology have blurred the boundaries between those states. The mechanisms of sleep have always intrigued neurophysiologists and great advances have been made over the last century in understanding them: we now know that the defining characteristic underlying sleep activity is a specific pattern of neuronal activity, namely the slow oscillation. The slow oscillation, which is characterized by the periodic alternation between periods of activity (ON-periods) and periods of hyperpolarization and neuronal silence (OFF-periods) is the default mode of activity of the sleeping cortex. This alternation is due to the tendency of neurons to fall into a silent period after an initial activation; such tendency is known as \u201cbistability\u201d. There is accumulating evidence that sleep-like bistability, and the ensuing OFF-periods, may occur locally in the awake human brain in some pathological conditions, in sleep transition, as well as after sleep deprivation. Therefore, to the extent that bistability and OFF periods represents the basic neuronal features of sleep, a paradigm shift is in place: from a neurophysiological perspective sleep can intrude into wakefulness. In this thesis, I explore the fluid boundaries between sleep and wakefulness and investigate their possible implications on the problem of personal persistence over time. Moreover, I study the clinical implications of the intrusion of sleep into wakefulness in patients with focal brain injury due to stroke. Specifically, I aim to: 1) show how the sleep-like bistability can be responsible for the loss of function in stroke patients. This may have implications for understanding the pathophysiology of stroke and helping to foster recovery; 2) establish the basis for a model of local sleep that might be present in the everyday life, id est the sensation of sleepiness. Indeed, sleepiness could reflect islands of sleep during wakefulness; 3) advocate the biological criterion of identity, in which the continuity necessary for maintaining ourselves over time could be represented by never resting activity in the brain

SLEEP-LIKE CORTICAL BISTABILITY IN VEGETATIVE STATE PATIENTS

The human brain is able to generate a wide repertoire of behavioral and psychological phenomena spanning from simple motor acts to cognition, from unimodal sensory perceptions to conscious experience. All these abilities are based on two key parameters of cortico-thalamic circuits functioning: the reactivity to a direct, local stimulation (cortical excitability) and the ability to causally interact (cortical effective connectivity). Indeed, alterations of these parameters have been suggested to underlie neurologic and psychiatric conditions. Over the last ten years, high-density electroencephalography combined with transcranial magnetic stimulation (TMS/hd-EEG) has been used to non-invasively probe cortical excitability and connectivity and to track over time pathological alterations, plastic changes and therapy-induced modifications in cortical circuits. A recently proposed theory suggests that consciousness depends on the brain\u2019s ability to engage in complex activity patterns that are, at once, distributed among interacting cortical areas (integrated) and differentiated in space and time (information-rich). In a recent series of experiments the electroencephalographic TMS-evoked brain response was recorded in healthy subjects during wakefulness, non-rapid eyes movement sleep (NREM), under pharmacological conditions (anesthesia), and pathological conditions (severely brain-injured, vegetative state patients). Indeed, TMS/hd-EEG measurements showed that during wakefulness the brain is able to sustain long-range specific patterns of activation, while when consciousness fades in NREM sleep, anesthesia and vegetative state, the thalamo-cortical system produces either a local or a global slow wave which underlies respectively a loss of differentiation or integration. We hypothesize that, like spontaneous sleep slow waves, the slow waves triggered by TMS are due to bistability between periods of neuronal activity (up-state) and silence (down-state) in cortical networks. Thalamo-cortical bistability could impair the ability of thalamo-cortical circuits to sustain long-range, differentiated patterns of activation, a key theoretical requisite for consciousness. Animal studies show that the extracellular signature of the down-state is a transient suppression of high frequency (>20Hz) power in the local field potential (LFP). More recently, intracranial recordings during NREM sleep in humans have shown that a intracranial stimulations induce a widespread suppression of high frequencies (i.e. cortical down-states) that impair the ability of thalamo-cortical circuits to engage in causal interactions. In the present thesis we use a TMS/hd-EEG approach in patients affected by disorders of consciousness such as vegetative state (VS) and minimally conscious state (MCS) to investigate whether bistability could underlie also pathological loss of consciousness. To verify this hypothesis, we recorded TMS-evoked potentials (TEPs) in awake VS and MCS patients as well as in healthy controls (HC) during wakefulness and NREM sleep. TEPs were analyzed by means of time-frequency analyses (power and phase-locking factor - PLF). We observed that TEPs recorded in VS patients were characterized by a large positive-negative deflection, closely resembling the one recorded in HC during NREM sleep. This sleep-like slow-wave was associated with a significant suppression of power in the high frequency band (>20 Hz) together with an early drop of PLF. Interestingly, in VS patients the power suppression slowly recovered to the baseline whereas in the NREM sleep of HC it was replaced by a late increase of power. Finally, the recovery of consciousness assessed in two patients evaluated longitudinally was paralleled by the resurgence of TEPs high frequency oscillations and by an increase of PLF duration. These results suggest that the slow waves evoked by TMS in VS patients possibly reflect a condition of cortical bistability that prevents the entrainment of thalamocortical modules in effective interactions and, hence, the emergence of consciousness. Intriguingly, the resumption of TEPs high frequency oscillations and a longer duration of phase-locked components (PLF) seem to be associated with the recovery of consciousness. Since bistability is, in principle, reversible and its mechanisms are well understood at the cellular and network level, it may represent a suitable target for novel therapeutic approaches in patients in whom consciousness is impaired, in spite of preserved cortical activity

Electroencéphalographie et interfaces cerveau-machine : nouvelles méthodes pour étudier les états mentaux

Avec les avancées technologiques dans le domaine de l'imagerie cérébrale fonctionnelle et les progrès théoriques dans la connaissance des différents éléments neurophysiologiques liés à la cognition, les deux dernières décennies ont vu l'apparition d'interfaces cerveau-machine (ICM) permettant à une personne d'observer en temps réel, ou avec un décalage qui se limite à quelques secondes, sa propre activité cérébrale. Le domaine clinique en général, et plus particulièrement celui de la neuropsychologie et des pathologies conduisant à un handicap moteur lourd, pour lesquels les applications potentielles sont nombreuses qu'elles soient thérapeutiques ou en vue d'une réhabilitation fonctionnelle, a constitué un moteur important de la recherche sur ce nouveau domaine des neurosciences temps réel. Parmi ces applications, le neurofeedback, ou neurothérapie, qui vise l'acquisition par le sujet du contrôle volontaire de certains aspects de son activité cérébrale en vue de les amplifier ou au contraire les diminuer dans un but thérapeutique, voire d'optimisation cognitive, représente une technique prometteuse, alternative aux thérapies et traitements médicamenteux. Cependant, la validation de ce type d'intervention et la compréhension des mécanismes mis en jeux en sont encore à leurs balbutiements. L'entraînement par neurofeedback est souvent long, pouvant s'étaler sur plusieurs semaines. Il est donc très probable que ce type de rééducation cérébrale sollicite des phénomènes de plasticité qui s'inscrivent dans une dynamique lente, et de ce fait, requiert une durée relativement longue d'entraînement pour atteindre les effets à long terme recherchés. Cependant, à cela peuvent s'ajouter de nombreux éléments perturbateurs qui pourraient être à l'origine de la difficulté de l'apprentissage et des longs entraînements nécessaires pour obtenir les résultats attendus. Parmi eux, les perturbations qui viennent déformer le signal enregistré, ou les éléments artefactuels qui ne font pas partie du signal d'intérêt, sont une première cause potentielle. Le manque de spécificité fonctionnelle du signal retourné au sujet pourrait en constituer une deuxième. Nous avons d'une part développé des outils méthodologiques de traitement du signal en vue d'améliorer la robustesse des analyses des signaux EEG, principalement utilisés jusqu'à maintenant dans le domaine du neurofeedback et des ICM, face aux artefacts et au bruit électromagnétique. D'autre part, si l'on s'intéresse au problème de la spécificité fonctionnelle du signal présenté au sujet, des études utilisant l'IRM fonctionnelle ou des techniques de reconstruction de sources à partir du signal EEG, qui fournissent des signaux ayant une meilleure spécificité spatiale, laissent entrevoir de possibles améliorations de la vitesse d'apprentissage. Afin d'augmenter la spécificité spatiale et la contingence fonctionnelle du feedback présenté au sujet, nous avons étudié la stabilité de la décomposition de l'EEG en différentes sources d'activité électrique cérébrale par Analyse en Composantes Indépendantes à travers différentes séances d'enregistrement effectuées sur un même sujet. Nous montrons que ces décompositions sont stables et pourraient permettre d'augmenter la spécificité fonctionnelle de l'entraînement au contrôle de l'activité cérébrale pour l'utilisation d'une ICM. Nous avons également travaillé à l'implémentation d'un outil logiciel permettant l'optimisation des protocoles expérimentaux basés sur le neurofeedback afin d'utiliser ces composantes indépendantes pour rejeter les artefacts en temps réel ou extraire l'activité cérébrale à entraîner. Ces outils sont utiles dans le cadre de l'analyse et de la caractérisation des signaux EEG enregistrés, ainsi que dans l'exploitation de leurs résultats dans le cadre d'un entraînement de neurofeedback. La deuxième partie de ce travail s'intéresse à la mise en place de protocoles de neurofeedback et à l'impact de l'apprentissage. Nous décrivons tout d'abord des résultats obtenus sur une étude pilote qui cherche à évaluer chez des sujets sains l'impact d'un protocole de neurofeedback basé sur le contrôle du rythme Mu. Les changements comportementaux ont été étudiés à l'aide d'un paradigme de signal stop qui permet d'indexer les capacités attentionnelles et d'inhibition de réponse motrice sur lesquelles on s'attend à ce que l'entraînement ICM ait une influence. Pour clore cette partie, nous présentons un nouvel outil interactif immersif pour l'entraînement cérébral, l'enseignement, l'art et le divertissement pouvant servir à évaluer l'impact de l'immersion sur l'apprentissage au cours d'un protocole de neurofeedback. Enfin, les perspectives de l'apport des méthodes et résultats présentés sont discutées dans le contexte du développement des ICMs de nouvelle génération qui prennent en compte la complexité de l'activité cérébrale. Nous présentons les dernières avancées dans l'étude de certains aspects des corrélats neuronaux liés à deux états mentaux ou classes d'états mentaux que l'on pourrait qualifier d'antagonistes par rapport au contrôle de l'attention : la méditation et la dérive attentionnelle, en vue de leur intégration à plus long terme dans un entraînement ICM par neurofeedback.With new technological advances in functional brain imaging and theoretical progress in the knowledge of the different neurophysiologic processes linked to cognition, the last two decades have seen the emergence of Brain-Machine Interfaces (BCIs) allowing a person to observe in real-time, or with a few seconds delay, his own cerebral activity. Clinical domain in general, and more particularly neuropsychology and pathologies leading to heavy motor handicaps, for which potential applications are numerous, whether therapeutic or for functional rehabilitation, has been a major driver of research on this new field of real-time neurosciences. Among these applications, neurofeedback, or neurotherapy, which aims the subject to voluntary control some aspects of his own cerebral activity in order to amplify or reduce them in a therapeutic goal, or for cognitive optimization, represents a promising technique, and an alternative to drug treatments. However, validation of this type of intervention and understanding of involved mechanisms are still in their infancy. Neurofeedback training is often long, up to several weeks. It is therefore very likely that this type of rehabilitation is seeking brain plasticity phenomena that are part of slow dynamics, and thus require a relatively long drive to achieve the desired long-term effects. However, other disturbing elements that could add up to the cause of the difficulty of learning and long training sessions required to achieve the expected results. Among them, the disturbances that come from recorded signal distortions, or artifactual elements that are not part of the signal of interest, are a first potential cause. The lack of functional specificity of the signal returned to the subject could be a second one. We have developed signal processing methodological tools to improve the robustness to artifacts and electromagnetic noise of EEG signals analysis, the main brain imaging technique used so far in the field of neurofeedback and BCIs. On the other hand, if one looks at the issue of functional specificity of the signal presented to the subject, studies using functional MRI or source reconstruction methods from the EEG signal, which both provide signals having a better spatial specificity, suggest improvements to the speed of learning. Seeing Independent Component Analysis as a potential tool to increase the spatial specificity and functional contingency of the feedback signal presented to the subject, we studied the stability of Independent Component Analysis decomposition of the EEG across different recording sessions conducted on the same subjects. We show that these decompositions are stable and could help to increase the functional specificity of BCI training. We also worked on the implementation of a software tool that allows the optimization of experimental protocols based on neurofeedback to use these independent components to reject artifacts or to extract brain activity in real-time. These tools are useful in the analysis and characterization of EEG signals recorded, and in the exploitation of their results as part of a neurofeedback training. The second part focuses on the development of neurofeedback protocols and the impact of learning. We first describe the results of a pilot study which seeks to evaluate the impact of a neurofeedback protocol based on the Mu rhythm control on healthy subjects. The behavioral changes were studied using a stop signal paradigm that indexes the attentional abilities and inhibition of motor responses on which the BCI training can possibly have influence. To conclude this section, we present a new tool for immersive interactive brain training, education, art and entertainment that can be used to assess the impact of immersion on learning during a neurofeedback protocol. Finally, prospects for methods and results presented are discussed in the context of next-generation BCI development which could take brain activity complexity into account. We present the latest advances in the study of certain aspects of the neural correlates associated with two mental states or classes of mental states that could be described as antagonistic with respect to the control of attention: meditation and mind wandering, for their integration in the longer term in an BCI training using neurofeedback

Using independent components analysis to identify visually driven regions and networks in the human brain, using data collected during movie watching

Traditionally, regions involved in visual processing are mapped in the brain using simple localisers and/or anatomical techniques. As a more efficient (and interesting) alternative, Bartels & Zeki (2004) suggested that independent components analysis (ICA) could be used to segment the brain into functional regions, using data collected during movie watching. The first aim of this thesis was to explore the potential of this technique for reliable identification of visually driven regions and networks. In Chapter 2 I thoroughly and systematically explore the sensitivity of tensor ICA (TICA) to common pre-processing parameters and identify an optimal analysis pipeline. Despite some sensitivity of TICA to the parameters tested, robust components in visually responsive regions could be identified across outputs. Using an optimized pipeline, in Chapter 3 I demonstrate that visually driven components (in particular, peak voxels) are consistent across different samples and movie clips, supporting the use of this technique. In Chapter 4 I show that established resting state networks can be identified in an ICA analysis using movies, and that by increasing dimensionality sub-regions of these networks can be identified. Chapter 5 shows how these reliable components represented visual regions in the motion processing pathway. Based on the success of the technique at the group level, in Chapter 6 I apply the technique to individual observer data. Results show that functional networks and visual regions of interest can be reliably identified, supporting its use in future neuroscientific research. To address the short-comings of BOLD, the second aim of this thesis was to investigate whether MEG frequency data and fMRI bold data could be combined for analysis in a novel technique using TICA. First in Chapter 7 I address some prerequisites for a combined MEG frequency analysis using the technique. On the back of these results, I use the technique to generate interesting cross-frequency components (Chapter 8) and cross modality components using combined MEG and fMRI data (Chapter 9). These results show exciting promise for potential use in future neuroscientific work. In the final chapter, I investigate the potential use of ICA and changing dimensionality for mapping the functional hierarchy of the visual system. With development this could be a useful tool for understanding connectivity between sub-regions of functional networks. These results have important implications for the identification of visually responsive regions and for understanding neural activity during natural viewing