A coherent neurobiological framework for functional neuroimaging provided by a model integrating compartmentalized energy metabolism.

Functional neuroimaging has undergone spectacular developments in recent years. Paradoxically, its neurobiological bases have remained elusive, resulting in an intense debate around the cellular mechanisms taking place upon activation that could contribute to the signals measured. Taking advantage of a modeling approach, we propose here a coherent neurobiological framework that not only explains several in vitro and in vivo observations but also provides a physiological basis to interpret imaging signals. First, based on a model of compartmentalized energy metabolism, we show that complex kinetics of NADH changes observed in vitro can be accounted for by distinct metabolic responses in two cell populations reminiscent of neurons and astrocytes. Second, extended application of the model to an in vivo situation allowed us to reproduce the evolution of intraparenchymal oxygen levels upon activation as measured experimentally without substantially altering the initial parameter values. Finally, applying the same model to functional neuroimaging in humans, we were able to determine that the early negative component of the blood oxygenation level-dependent response recorded with functional MRI, known as the initial dip, critically depends on the oxidative response of neurons, whereas the late aspects of the signal correspond to a combination of responses from cell types with two distinct metabolic profiles that could be neurons and astrocytes. In summary, our results, obtained with such a modeling approach, support the concept that both neuronal and glial metabolic responses form essential components of neuroimaging signals.

c-Jun N-terminal kinase binding domain-dependent phosphorylation of mitogen-activated protein kinase kinase 4 and mitogen-activated protein kinase kinase 7 and balancing cross-talk between c-Jun N-terminal kinase and extracellular signal-regulated kinase pathways in cortical neurons

The c-Jun N-terminal kinase (JNK) is a mitogen-activated protein kinase (MAPK) activated by stress-signals and involved in many different diseases. Previous results proved the powerful effect of the cell permeable peptide inhibitor d-JNKI1 (d-retro-inverso form of c-Jun N-terminal kinase-inhibitor) against neuronal death in CNS diseases, but the precise features of this neuroprotection remain unclear. We here performed cell-free and in vitro experiments for a deeper characterization of d-JNKI1 features in physiological conditions. This peptide works by preventing JNK interaction with its c-Jun N-terminal kinase-binding domain (JBD) dependent targets. We here focused on the two JNK upstream MAPKKs, mitogen-activated protein kinase kinase 4 (MKK4) and mitogen-activated protein kinase kinase 7 (MKK7), because they contain a JBD homology domain. We proved that d-JNKI1 prevents MKK4 and MKK7 activity in cell-free and in vitro experiments: these MAPKK could be considered not only activators but also substrates of JNK. This means that d-JNKI1 can interrupt downstream but also upstream events along the JNK cascade, highlighting a new remarkable feature of this peptide. We also showed the lack of any direct effect of the peptide on p38, MEK1, and extracellular signal-regulated kinase (ERK) in cell free, while in rat primary cortical neurons JNK inhibition activates the MEK1-ERK-Ets1/c-Fos cascade. JNK inhibition induces a compensatory effect and leads to ERK activation via MEK1, resulting in an activation of the survival pathway-(MEK1/ERK) as a consequence of the death pathway-(JNK) inhibition. This study should hold as an important step to clarify the strong neuroprotective effect of d-JNKI1.

Insulin and IGF-1 enhance the expression of the neuronal monocarboxylate transporter MCT2 by translational activation via stimulation of the phosphoinositide 3-kinase-Akt-mammalian target of rapamycin pathway.

MCT2 is the main neuronal monocarboxylate transporter essential for facilitating lactate and ketone body utilization as energy substrates. Our study reveals that treatment of cultured cortical neurons with insulin and IGF-1 led to a striking enhancement of MCT2 immunoreactivity in a time- and concentration-dependent manner. Surprisingly, neither insulin nor IGF-1 affected MCT2 mRNA expression, suggesting that regulation of MCT2 protein expression occurs at the translational rather than the transcriptional level. Investigation of the putative signalling pathways leading to translation activation revealed that insulin and IGF-1 induced p44- and p42 MAPK, Akt and mTOR phosphorylation. S6 ribosomal protein, a component of the translational machinery, was also strongly activated by insulin and IGF-1. Phosphorylation of p44- and p42 MAPK was blocked by the MEK inhibitor PD98058, while Akt phosphorylation was abolished by the PI3K inhibitor LY294002. Phosphorylation of mTOR and S6 was blocked by the mTOR inhibitor rapamycin. In parallel, it was observed that LY294002 and rapamycin almost completely blocked the effects of insulin and IGF-1 on MCT2 protein expression, whereas PD98059 and SB202190 (a p38K inhibitor) had no effect on insulin-induced MCT2 expression and only a slight effect on IGF-1-induced MCT2 expression. At the subcellular level, a significant increase in MCT2 protein expression within an intracellular pool was observed while no change at the cell surface was apparent. As insulin and IGF-1 are involved in synaptic plasticity, their effect on MCT2 protein expression via an activation of the PI3K-Akt-mTOR-S6K pathway might contribute to the preparation of neurons for enhanced use of nonglucose energy substrates following altered synaptic efficacy.

Mobile robot experimental modeling and control strategies using sensor fusion

This research work deals with the problem of modeling and design of low level speed controller for the mobile robot PRIM. The main objective is to develop an effective educational, and research tool. On one hand, the interests in using the open mobile platform PRIM consist in integrating several highly related subjects to the automatic control theory in an educational context, by embracing the subjects of communications, signal processing, sensor fusion and hardware design, amongst others. On the other hand, the idea is to implement useful navigation strategies such that the robot can be served as a mobile multimedia information point. It is in this context, when navigation strategies are oriented to goal achievement, that a local model predictive control is attained. Hence, such studies are presented as a very interesting control strategy in order to develop the future capabilities of the system. In this context the research developed includes the visual information as a meaningful source that allows detecting the obstacle position coordinates as well as planning the free obstacle trajectory that should be reached by the robot

Self-potentials in partially saturated media: the importance of explicit modeling of electrode effects

Self-potential (SP) data are of interest to vadose zone hydrology because of their direct sensitivity to water flow and ionic transport. There is unfortunately little consensus in the literature about how to best model SP data under partially saturated conditions, and different approaches (often supported by one laboratory data set alone) have been proposed. We argue that this lack of agreement can largely be traced to electrode effects that have not been properly taken into account. A series of drainage and imbibition experiments were considered in which we found that previously proposed approaches to remove electrode effects were unlikely to provide adequate corrections. Instead, we explicitly modeled the electrode effects together with classical SP contributions using a flow and transport model. The simulated data agreed overall with the observed SP signals and allowed decomposing the different signal contributions to analyze them separately. After reviewing other published experimental data, we suggest that most of them include electrode effects that have not been properly taken into account. Our results suggest that previously presented SP theory works well when considering the modeling uncertainties presently associated with electrode effects. Additional work is warranted to not only develop suitable electrodes for laboratory experiments but also to assure that associated electrode effects that appear inevitable in longer term experiments are predictable, so that they can be incorporated into the modeling framework.

Statistical modeling of bowing control applied to violin sound synthesis

Excitation-continuous music instrument control patterns are often not explicitly represented in current sound synthesis techniques when applied to automatic performance. Both physical model-based and sample-based synthesis paradigmswould benefit from a flexible and accurate instrument control model, enabling the improvement of naturalness and realism. Wepresent a framework for modeling bowing control parameters inviolin performance. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing control parameter signals.We model the temporal contour of bow velocity, bow pressing force, and bow-bridge distance as sequences of short Bézier cubic curve segments. Considering different articulations, dynamics, and performance contexts, a number of note classes are defined. Contours of bowing parameters in a performance database are analyzed at note-level by following a predefined grammar that dictates characteristics of curve segment sequences for each of the classes in consideration. As a result, contour analysis of bowing parameters of each note yields an optimal representation vector that is sufficient for reconstructing original contours with significant fidelity. From the resulting representation vectors, we construct a statistical model based on Gaussian mixtures suitable for both the analysis and synthesis of bowing parameter contours. By using the estimated models, synthetic contours can be generated through a bow planning algorithm able to reproduce possible constraints caused by the finite length of the bow. Rendered contours are successfully used in two preliminary synthesis frameworks: digital waveguide-based bowed stringphysical modeling and sample-based spectral-domain synthesis.

Notch1 is required for neuronal and glial differentiation in the cerebellum.

The mechanisms that guide progenitor cell fate and differentiation in the vertebrate central nervous system (CNS) are poorly understood. Gain-of-function experiments suggest that Notch signaling is involved in the early stages of mammalian neurogenesis. On the basis of the expression of Notch1 by putative progenitor cells of the vertebrate CNS, we have addressed directly the role of Notch1 in the development of the mammalian brain. Using conditional gene ablation, we show that loss of Notch1 results in premature onset of neurogenesis by neuroepithelial cells of the midbrain-hindbrain region of the neural tube. Notch1-deficient cells do not complete differentiation but are eliminated by apoptosis, resulting in a reduced number of neurons in the adult cerebellum. We have also analyzed the effects of Notch1 ablation on gliogenesis in vivo. Our results show that Notch1 is required for both neuron and glia formation and modulates the onset of neurogenesis within the cerebellar neuroepithelium.

Characterization of the neuronal microtubule regulator SCG10 in transfected cells and " in vivo "

SUMMARY The ability of neuronal processes to find their way along complex paths and to establish appropriate connections depends on continual rearrangements of the cytoskeletal components. The regulation of microtubules plays an important role for morphological changes underlying nevrite outgrowth, axonal elongation, and growth cone steering. SCG10 (superior cervical ganglion clone 10) is a neuronal growthassociated protein developmentally regulated and highly enriched in the neuronal growth cones. SCG10 presents a microtubule destabilizing activity that could participate to the regulation of microtubule dynamics and thus explain microtubule behaviors in the growth cone during axonal elongation and turning. It is here suggested that a tight control of the opposite effects on microtubules of SCG10 and the stabilizing microtubule-associated protein MAP1B allows a fine tuning of cytoskeletal rearrangement and may provide the required microtubule dynamic instability to promote axonal growth. Moreover, antibodyblockade of SCG10 function, that leads to growth cone pauses similar as those triggered by the guidance molecule EphB, and the modulation of SCG10 activity by the Rho GTPase Rnd1 suggest a potential role for SCG10 in the signal transduction pathways of extracellular guidance cues. The identification of the active zone protein Bassoon as a potential interaction partner for the SCG10-related protein NPC2, using atomic force microscopy as well as COS-7 and neuronal cell cultures, also gives new insights for a role of this protein family into the processes of synapse genesis or plasticity. Finally, SCG10 mutant mice generated by gene targeting and expressing a soluble form of the protein have been characterized during early postnatal development and in the adulthood. Due to the deletion of its membrane binding domain, SCG10 specific subcellular targeting to growth cones is compromised and results in impairments of motor and coordination development. Further histological analysis in the sciatic nerve reveal that these symptoms are associated with neurodegenerative signs. RESUME Une navigation correcte des prolongements cellulaires neuronaux leur permettant de former des connections appropriées repose sur de continuels réarrangements des constituants de leur cytosquelette. La régulation des microtubules joue notamment un rôle important dans les changements morphologiques qui accompagnent la croissance axonale et les réorientations du cône de croissance. SCG10 (superior cervical ganglion clone 10) est une protéine étroitement associée à la croissance neuronale, hautement régulée durant le développement et abondante au niveau du cône de croissance. SCG10 présente une activité déstabilisatrice sur les microtubules qui pourrait permettre une régulation des paramètres dynamiques propres aux microtubules et ainsi expliquer leur comportement durant la navigation du cône de croissance. Il est ici proposé qu'un contrôle précis des effets opposés de SCG10 et d'une autre protéine stabilisante associée aux microtubules (MAP1 B) permette un réglage fin des réarrangements du cytosquelette et puisse ainsi produire l'instabilité dynamique nécessaire à la croissance anale. Par ailleurs, le blocage de la fonction de SCG10 par un anticorps spécifique, conduisant à des pauses du cônes de croissance similaires à celles provoquées par la molécule de guidage EphB, ainsi que la modulation de l'activité de SCG10 par la Rho GTPase Rnd1 suggèrent une potentielle implication de SCG10 dans les voies de transduction des signaux provenant de molécules de guidage extracellulaires. L'identification d'une interaction de la protéine synaptique Bassoon avec la protéine NPC2 apparentée à SCG10, au moyen de la microscopie à force atomique et dans des cultures de cellules neuronales et COS-7, ouvre des perspectives concernant ces protéines dans la formation et la plasticité synaptiques. Finalement, des souris mutantes pour SCG10 produites par ciblage de gène et exprimant une forme soluble de la protéine ont été caractérisées durant la phase précoce du développement et à l'âge adulte. La délétion du domaine permettant l'ancrage de SCG10 aux membranes compromet sa sub-localisation au niveau du cône de croissance et résulte en l'apparition de troubles moteurs et de la coordination. Des analyses histologiques complémentaires au niveau du nerf sciatique montrent que ces symptômes sont associés avec des signes neurodégénératifs.

Implicit methods for qualitative modeling of gene regulatory networks.

Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

Neuronal death in the development of the vertebrate central nervous system

Neuronal death occurs naturally in the development of the vertebrate central nervous system, deleting large numbers of neurons at the time when afferent and efferent connections are being formed. It is these that regulate it, by means of anterograde and retrograde survival signals that depend on trophic molecules and electrical activity. Possible roles include the regulation of neuronal numbers (numerical matching) and the elimination of axonal targeting errors.

Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders.

Quantitative phase microscopy (QPM) has recently emerged as a new powerful quantitative imaging technique well suited to noninvasively explore a transparent specimen with a nanometric axial sensitivity. In this review, we expose the recent developments of quantitative phase-digital holographic microscopy (QP-DHM). Quantitative phase-digital holographic microscopy (QP-DHM) represents an important and efficient quantitative phase method to explore cell structure and dynamics. In a second part, the most relevant QPM applications in the field of cell biology are summarized. A particular emphasis is placed on the original biological information, which can be derived from the quantitative phase signal. In a third part, recent applications obtained, with QP-DHM in the field of cellular neuroscience, namely the possibility to optically resolve neuronal network activity and spine dynamics, are presented. Furthermore, potential applications of QPM related to psychiatry through the identification of new and original cell biomarkers that, when combined with a range of other biomarkers, could significantly contribute to the determination of high risk developmental trajectories for psychiatric disorders, are discussed.

A cellular system for spatial signal decoding in chemical gradients.

Directional cell growth requires that cells read and interpret shallow chemical gradients, but how the gradient directional information is identified remains elusive. We use single-cell analysis and mathematical modeling to define the cellular gradient decoding network in yeast. Our results demonstrate that the spatial information of the gradient signal is read locally within the polarity site complex using double-positive feedback between the GTPase Cdc42 and trafficking of the receptor Ste2. Spatial decoding critically depends on low Cdc42 activity, which is maintained by the MAPK Fus3 through sequestration of the Cdc42 activator Cdc24. Deregulated Cdc42 or Ste2 trafficking prevents gradient decoding and leads to mis-oriented growth. Our work discovers how a conserved set of components assembles a network integrating signal intensity and directionality to decode the spatial information contained in chemical gradients.

Modeling and Analysis of Noise and Interconnects for On-Chip Communication Link Design

This thesis considers modeling and analysis of noise and interconnects in onchip communication. Besides transistor count and speed, the capabilities of a modern design are often limited by on-chip communication links. These links typically consist of multiple interconnects that run parallel to each other for long distances between functional or memory blocks. Due to the scaling of technology, the interconnects have considerable electrical parasitics that affect their performance, power dissipation and signal integrity. Furthermore, because of electromagnetic coupling, the interconnects in the link need to be considered as an interacting group instead of as isolated signal paths. There is a need for accurate and computationally effective models in the early stages of the chip design process to assess or optimize issues affecting these interconnects. For this purpose, a set of analytical models is developed for on-chip data links in this thesis. First, a model is proposed for modeling crosstalk and intersymbol interference. The model takes into account the effects of inductance, initial states and bit sequences. Intersymbol interference is shown to affect crosstalk voltage and propagation delay depending on bus throughput and the amount of inductance. Next, a model is proposed for the switching current of a coupled bus. The model is combined with an existing model to evaluate power supply noise. The model is then applied to reduce both functional crosstalk and power supply noise caused by a bus as a trade-off with time. The proposed reduction method is shown to be effective in reducing long-range crosstalk noise. The effects of process variation on encoded signaling are then modeled. In encoded signaling, the input signals to a bus are encoded using additional signaling circuitry. The proposed model includes variation in both the signaling circuitry and in the wires to calculate the total delay variation of a bus. The model is applied to study level-encoded dual-rail and 1-of-4 signaling. In addition to regular voltage-mode and encoded voltage-mode signaling, current-mode signaling is a promising technique for global communication. A model for energy dissipation in RLC current-mode signaling is proposed in the thesis. The energy is derived separately for the driver, wire and receiver termination.

Mathematical modeling of level of anaesthesia from EEG measurements

Problem of modeling of anaesthesia depth level is studied in this Master Thesis. It applies analysis of EEG signals with nonlinear dynamics theory and further classification of obtained values. The main stages of this study are the following: data preprocessing; calculation of optimal embedding parameters for phase space reconstruction; obtaining reconstructed phase portraits of each EEG signal; formation of the feature set to characterise obtained phase portraits; classification of four different anaesthesia levels basing on previously estimated features. Classification was performed with: Linear and quadratic Discriminant Analysis, k Nearest Neighbours method and online clustering. In addition, this work provides overview of existing approaches to anaesthesia depth monitoring, description of basic concepts of nonlinear dynamics theory used in this Master Thesis and comparative analysis of several different classification methods.

NAA and NAAG variation in neuronal activation during visual stimulation

N-acetyl-aspartyl-glutamate (NAAG) and its hydrolysis product N-acetyl-L-aspartate (NAA) are among the most important brain metabolites. NAA is a marker of neuron integrity and viability, while NAAG modulates glutamate release and may have a role in neuroprotection and synaptic plasticity. Investigating on a quantitative basis the role of these metabolites in brain metabolism in vivo by magnetic resonance spectroscopy (MRS) is a major challenge since the main signals of NAA and NAAG largely overlap. This is a preliminary study in which we evaluated NAA and NAAG changes during a visual stimulation experiment using functional MRS. The paradigm used consisted of a rest period (5 min and 20 s), followed by a stimulation period (10 min and 40 s) and another rest period (10 min and 40 s). MRS from 17 healthy subjects were acquired at 3T with TR/TE = 2000/288 ms. Spectra were averaged over subjects and quantified with LCModel. The main outcomes were that NAA concentration decreased by about 20% with the stimulus, while the concentration of NAAG concomitantly increased by about 200%. Such variations fall into models for the energy metabolism underlying neuronal activation that point to NAAG as being responsible for the hyperemic vascular response that causes the BOLD signal. They also agree with the fact that NAAG and NAA are present in the brain at a ratio of about 1:10, and with the fact that the only known metabolic pathway for NAAG synthesis is from NAA and glutamate.