Looming signals reveal synergistic principles of multisensory integration.

Multisensory interactions are a fundamental feature of brain organization. Principles governing multisensory processing have been established by varying stimulus location, timing and efficacy independently. Determining whether and how such principles operate when stimuli vary dynamically in their perceived distance (as when looming/receding) provides an assay for synergy among the above principles and also means for linking multisensory interactions between rudimentary stimuli with higher-order signals used for communication and motor planning. Human participants indicated movement of looming or receding versus static stimuli that were visual, auditory, or multisensory combinations while 160-channel EEG was recorded. Multivariate EEG analyses and distributed source estimations were performed. Nonlinear interactions between looming signals were observed at early poststimulus latencies (∼75 ms) in analyses of voltage waveforms, global field power, and source estimations. These looming-specific interactions positively correlated with reaction time facilitation, providing direct links between neural and performance metrics of multisensory integration. Statistical analyses of source estimations identified looming-specific interactions within the right claustrum/insula extending inferiorly into the amygdala and also within the bilateral cuneus extending into the inferior and lateral occipital cortices. Multisensory effects common to all conditions, regardless of perceived distance and congruity, followed (∼115 ms) and manifested as faster transition between temporally stable brain networks (vs summed responses to unisensory conditions). We demonstrate the early-latency, synergistic interplay between existing principles of multisensory interactions. Such findings change the manner in which to model multisensory interactions at neural and behavioral/perceptual levels. We also provide neurophysiologic backing for the notion that looming signals receive preferential treatment during perception.

Self-paced Movement Intention Detection from Human Brain Signals: Invasive and Non-invasive EEG

Neural signatures of humans' movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive and noninvasive methods. In the first study with scalp electroencephalograph (EEG) signals from healthy controls, we report single trial detection of movement intention using movement related potentials (MRPs) in a frequency range between 0.1 to 1 Hz. Movement intention can be detected above chance level (p<0.05) on average 460 ms before the movement onset with low detection rate during the on-movement intention period. Using intracranial EEG (iEEG) from one epileptic subject, we detect movement intention as early as 1500 ms before movement onset with accuracy above 90% using electrodes implanted in the bilateral supplementary motor area (SMA). The coherent results obtained with non-invasive and invasive method and its generalization capabilities across different days of recording, strengthened the theory that self-paced movement intention can be detected before movement initiation for the advancement in robot-assisted neurorehabilitation.

Coagulation factor Xa activates thrombin in ischemic neural tissue.

Thrombin is involved in mediating neuronal death in cerebral ischemia. We investigated its so far unknown mode of activation in ischemic neural tissue. We used an in vitro approach to distinguish the role of circulating coagulation factors from endogenous cerebral mechanisms. We modeled ischemic stroke by subjecting rat organotypic hippocampal slice cultures to 30-min oxygen (5%) and glucose (1 mmol/L) deprivation (OGD). Perinuclear activated factor X (FXa) immunoreactivity was observed in CA1 neurons after OGD. Selective FXa inhibition by fondaparinux during and after OGD significantly reduced neuronal death in the CA1 after 48 h. Thrombin enzyme activity was increased in the medium 24 h after OGD and this increase was prevented by fondaparinux suggesting that FXa catalyzes the conversion of prothrombin to thrombin in neural tissue after ischemia in vitro. Treatment with SCH79797, a selective antagonist of the thrombin receptor protease-activated receptor-1 (PAR-1), significantly decreased neuronal cell death indicating that thrombin signals ischemic damage via PAR-1. The c-Jun N-terminal kinase (JNK) pathway plays an important role in excitotoxicity and cerebral ischemia and we observed activation of the JNK substrate, c-Jun in our model. Both the FXa inhibitor, fondaparinux and the PAR-1 antagonist SCH79797, decreased the level of phospho-c-Jun Ser73. These results indicate that FXa activates thrombin in cerebral ischemia, which leads via PAR-1 to the activation of the JNK pathway resulting in neuronal death.

Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.

The long-term survival of in vitro engineered nervous tissue derived from the specific neural differentiation of mouse embryonic stem cells.

Embryonic stem cells (ESCs) offer attractive prospective as potential source of neurons for cell replacement therapy in human neurodegenerative diseases. Besides, ESCs neural differentiation enables in vitro tissue engineering for fundamental research and drug discovery aimed at the nervous system. We have established stable and long-term three-dimensional (3D) culture conditions which can be used to model long latency and complex neurodegenerative diseases. Mouse ESCs-derived neural progenitor cells generated by MS5 stromal cells induction, result in strictly neural 3D cultures of about 120-mum thick, whose cells expressed mature neuronal, astrocytes and myelin markers. Neurons were from the glutamatergic and gabaergic lineages. This nervous tissue was spatially organized in specific layers resembling brain sub-ependymal (SE) nervous tissue, and was maintained in vitro for at least 3.5 months with great stability. Electron microscopy showed the presence of mature synapses and myelinated axons, suggesting functional maturation. Electrophysiological activity revealed biological signals involving action potential propagation along neuronal fibres and synaptic-like release of neurotransmitters. The rapid development and stabilization of this 3D cultures model result in an abundant and long-lasting production that is compatible with multiple and productive investigations for neurodegenerative diseases modeling, drug and toxicology screening, stress and aging research.

Bump time-frequency toolbox: a toolbox for time-frequency oscillatory bursts extraction in electrophysiological signals

Background: oscillatory activity, which can be separated in background and oscillatory burst pattern activities, is supposed to be representative of local synchronies of neural assemblies. Oscillatory burst events should consequently play a specific functional role, distinct from background EEG activity – especially for cognitive tasks (e.g. working memory tasks), binding mechanisms and perceptual dynamics (e.g. visual binding), or in clinical contexts (e.g. effects of brain disorders). However extracting oscillatory events in single trials, with a reliable and consistent method, is not a simple task. Results: in this work we propose a user-friendly stand-alone toolbox, which models in a reasonable time a bump time-frequency model from the wavelet representations of a set of signals. The software is provided with a Matlab toolbox which can compute wavelet representations before calling automatically the stand-alone application. Conclusion: The tool is publicly available as a freeware at the address: http:// www.bsp.brain.riken.jp/bumptoolbox/toolbox_home.html

The behavioral relevance of multisensory neural response interactions.

Sensory information can interact to impact perception and behavior. Foods are appreciated according to their appearance, smell, taste and texture. Athletes and dancers combine visual, auditory, and somatosensory information to coordinate their movements. Under laboratory settings, detection and discrimination are likewise facilitated by multisensory signals. Research over the past several decades has shown that the requisite anatomy exists to support interactions between sensory systems in regions canonically designated as exclusively unisensory in their function and, more recently, that neural response interactions occur within these same regions, including even primary cortices and thalamic nuclei, at early post-stimulus latencies. Here, we review evidence concerning direct links between early, low-level neural response interactions and behavioral measures of multisensory integration.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures

Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Water deprivation and the double- depletion hypothesis: common neural mechanisms underlie thirst and salt appetite

Water deprivation-induced thirst is explained by the double-depletion hypothesis, which predicts that dehydration of the two major body fluid compartments, the extracellular and intracellular compartments, activates signals that combine centrally to induce water intake. However, sodium appetite is also elicited by water deprivation. In this brief review, we stress the importance of the water-depletion and partial extracellular fluid-repletion protocol which permits the distinction between sodium appetite and thirst. Consistent enhancement or a de novo production of sodium intake induced by deactivation of inhibitory nuclei (e.g., lateral parabrachial nucleus) or hormones (oxytocin, atrial natriuretic peptide), in water-deprived, extracellular-dehydrated or, contrary to tradition, intracellular-dehydrated rats, suggests that sodium appetite and thirst share more mechanisms than previously thought. Water deprivation has physiological and health effects in humans that might be related to the salt craving shown by our species.

Engagement of multifocal neural circuits during recall of autobiographical happy events

Happy emotional states have not been extensively explored in functional magnetic resonance imaging studies using autobiographic recall paradigms. We investigated the brain circuitry engaged during induction of happiness by standardized script-driven autobiographical recall in 11 healthy subjects (6 males), aged 32.4 ± 7.2 years, without physical or psychiatric disorders, selected according to their ability to vividly recall personal experiences. Blood oxygen level-dependent (BOLD) changes were recorded during auditory presentation of personal scripts of happiness, neutral content and negative emotional content (irritability). The same uniform structure was used for the cueing narratives of both emotionally salient and neutral conditions, in order to decrease the variability of findings. In the happiness relative to the neutral condition, there was an increased BOLD signal in the left dorsal prefrontal cortex and anterior insula, thalamus bilaterally, left hypothalamus, left anterior cingulate gyrus, and midportions of the left middle temporal gyrus (P < 0.05, corrected for multiple comparisons). Relative to the irritability condition, the happiness condition showed increased activity in the left insula, thalamus and hypothalamus, and in anterior and midportions of the inferior and middle temporal gyri bilaterally (P < 0.05, corrected), varying in size between 13 and 64 voxels. Findings of happiness-related increased activity in prefrontal and subcortical regions extend the results of previous functional imaging studies of autobiographical recall. The BOLD signal changes identified reflect general aspects of emotional processing, emotional control, and the processing of sensory and bodily signals associated with internally generated feelings of happiness. These results reinforce the notion that happiness induction engages a wide network of brain regions.

Mapping and signaling of neural pathways involved in the regulation of hydromineral homeostasis

Several forebrain and brainstem neurochemical circuitries interact with peripheral neural and humoral signals to collaboratively maintain both the volume and osmolality of extracellular fluids. Although much progress has been made over the past decades in the understanding of complex mechanisms underlying neuroendocrine control of hydromineral homeostasis, several issues still remain to be clarified. The use of techniques such as molecular biology, neuronal tracing, electrophysiology, immunohistochemistry, and microinfusions has significantly improved our ability to identify neuronal phenotypes and their signals, including those related to neuron-glia interactions. Accordingly, neurons have been shown to produce and release a large number of chemical mediators (neurotransmitters, neurohormones and neuromodulators) into the interstitial space, which include not only classic neurotransmitters, such as acetylcholine, amines (noradrenaline, serotonin) and amino acids (glutamate, GABA), but also gaseous (nitric oxide, carbon monoxide and hydrogen sulfide) and lipid-derived (endocannabinoids) mediators. This efferent response, initiated within the neuronal environment, recruits several peripheral effectors, such as hormones (glucocorticoids, angiotensin II, estrogen), which in turn modulate central nervous system responsiveness to systemic challenges. Therefore, in this review, we shall evaluate in an integrated manner the physiological control of body fluid homeostasis from the molecular aspects to the systemic and integrated responses.

Modeling High-Dimensional Audio Sequences with Recurrent Neural Networks

Cette thèse étudie des modèles de séquences de haute dimension basés sur des réseaux de neurones récurrents (RNN) et leur application à la musique et à la parole. Bien qu'en principe les RNN puissent représenter les dépendances à long terme et la dynamique temporelle complexe propres aux séquences d'intérêt comme la vidéo, l'audio et la langue naturelle, ceux-ci n'ont pas été utilisés à leur plein potentiel depuis leur introduction par Rumelhart et al. (1986a) en raison de la difficulté de les entraîner efficacement par descente de gradient. Récemment, l'application fructueuse de l'optimisation Hessian-free et d'autres techniques d'entraînement avancées ont entraîné la recrudescence de leur utilisation dans plusieurs systèmes de l'état de l'art. Le travail de cette thèse prend part à ce développement. L'idée centrale consiste à exploiter la flexibilité des RNN pour apprendre une description probabiliste de séquences de symboles, c'est-à-dire une information de haut niveau associée aux signaux observés, qui en retour pourra servir d'à priori pour améliorer la précision de la recherche d'information. Par exemple, en modélisant l'évolution de groupes de notes dans la musique polyphonique, d'accords dans une progression harmonique, de phonèmes dans un énoncé oral ou encore de sources individuelles dans un mélange audio, nous pouvons améliorer significativement les méthodes de transcription polyphonique, de reconnaissance d'accords, de reconnaissance de la parole et de séparation de sources audio respectivement. L'application pratique de nos modèles à ces tâches est détaillée dans les quatre derniers articles présentés dans cette thèse. Dans le premier article, nous remplaçons la couche de sortie d'un RNN par des machines de Boltzmann restreintes conditionnelles pour décrire des distributions de sortie multimodales beaucoup plus riches. Dans le deuxième article, nous évaluons et proposons des méthodes avancées pour entraîner les RNN. Dans les quatre derniers articles, nous examinons différentes façons de combiner nos modèles symboliques à des réseaux profonds et à la factorisation matricielle non-négative, notamment par des produits d'experts, des architectures entrée/sortie et des cadres génératifs généralisant les modèles de Markov cachés. Nous proposons et analysons également des méthodes d'inférence efficaces pour ces modèles, telles la recherche vorace chronologique, la recherche en faisceau à haute dimension, la recherche en faisceau élagué et la descente de gradient. Finalement, nous abordons les questions de l'étiquette biaisée, du maître imposant, du lissage temporel, de la régularisation et du pré-entraînement.