Development, degeneration and neural network of the bodily self

The question addressed by this dissertation is how the human brain builds a coherent representation of the body, and how this representation is used to recognize its own body. Recent approaches by neuroimaging and TMS revealed hints for a distinct brain representation of human body, as compared with other stimulus categories. Neuropsychological studies demonstrated that body-parts and self body-parts recognition are separate processes sub-served by two different, even if possibly overlapping, networks within the brain. Bodily self-recognition is one aspect of our ability to distinguish between self and others and the self/other distinction is a crucial aspect of social behaviour. This is the reason why I have conducted a series of experiment on subjects with everyday difficulties in social and emotional behaviour, such as patients with autism spectrum disorders (ASD) and patients with Parkinson’s disease (PD). More specifically, I studied the implicit self body/face recognition (Chapter 6) and the influence of emotional body postures on bodily self-processing in TD children as well as in ASD children (Chapter 7). I found that the bodily self-recognition is present in TD and in ASD children and that emotional body postures modulate self and others’ body processing. Subsequently, I compared implicit and explicit bodily self-recognition in a neuro-degenerative pathology, such as in PD patients, and I found a selective deficit in implicit but not in explicit self-recognition (Chapter 8). This finding suggests that implicit and explicit bodily self-recognition are separate processes subtended by different mechanisms that can be selectively impaired. If the bodily self is crucial for self/other distinction, the space around the body (personal space) represents the space of interaction and communication with others. When, I studied this space in autism, I found that personal space regulation is impaired in ASD children (Chapter 9).

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

Regulatory networks of Neisseria meningitidis and their implications for pathogenesis

Neisseria meningitidis, the leading cause of bacterial meningitis, can adapt to different host niches during human infection. Both transcriptional and post-transcriptional regulatory networks have been identified as playing a crucial role for bacterial stress responses and virulence. We investigated the N. meningitidis transcriptional landscape both by microarray and by RNA sequencing (RNAseq). Microarray analysis of N. meningitidis grown in the presence or absence of glucose allowed us to identify genes regulated by carbon source availability. In particular, we identified a glucose-responsive hexR-like transcriptional regulator in N. meningitidis. Deletion analysis showed that the hexR gene is accountable for a subset of the glucose-responsive regulation, and in vitro assays with the purified protein showed that HexR binds to the promoters of the central metabolic operons of meningococcus, by targeting a DNA region overlapping putative regulatory sequences. Our results indicate that HexR coordinates the central metabolism of meningococcus in response to the availability of glucose, and N. meningitidis strains lacking the hexR gene are also deficient in establishing successful bacteremia in a mouse model of infection. In parallel, RNAseq analysis of N. meningitidis cultured under standard or iron-limiting in vitro growth conditions allowed us to identify novel small non-coding RNAs (sRNAs) potentially involved in N. meningitidis regulatory networks. Manual curation of the RNAseq data generated a list of 51 sRNAs, 8 of which were validated by Northern blotting. Deletion of selected sRNAs caused attenuation of N. meningitidis infection in a murine model, leading to the identification of the first sRNAs influencing meningococcal bacteraemia. Furthermore, we describe the identification and initial characterization of a novel sRNA unique to meningococcus, closely associated to genes relevant for the intracellular survival of pathogenic Neisseriae. Taken together, our findings could help unravel the regulation of N. meningitidis adaptation to the host environment and its implications for pathogenesis.

Cooperative Cognitive Wireless Networks over TV White and Gray Spaces

Wireless networks rapidly became a fundamental pillar of everyday activities. Whether at work or elsewhere, people often benefits from always-on connections. This trend is likely to increase, and hence actual technologies struggle to cope with the increase in traffic demand. To this end, Cognitive Wireless Networks have been studied. These networks aim at a better utilization of the spectrum, by understanding the environment in which they operate, and adapt accordingly. In particular recently national regulators opened up consultations on the opportunistic use of the TV bands, which became partially free due to the digital TV switch over. In this work, we focus on the indoor use of of TVWS. Interesting use cases like smart metering and WiFI like connectivity arise, and are studied and compared against state of the art technology. New measurements for TVWS networks will be presented and evaluated, and fundamental characteristics of the signal derived. Then, building on that, a new model of spectrum sharing, which takes into account also the height from the terrain, is presented and evaluated in a real scenario. The principal limits and performance of TVWS operated networks will be studied for two main use cases, namely Machine to Machine communication and for wireless sensor networks, particularly for the smart grid scenario. The outcome is that TVWS are certainly interesting to be studied and deployed, in particular when used as an additional offload for other wireless technologies. Seeing TVWS as the only wireless technology on a device is harder to be seen: the uncertainity in channel availability is the major drawback of opportunistic networks, since depending on the primary network channel allocation might lead in having no channels available for communication. TVWS can be effectively exploited as offloading solutions, and most of the contributions presented in this work proceed in this direction.

Long-term potentiation and neural network activity in the neonatal rat cerebral cortex in vivo

Long-term potentiation in the neonatal rat rnbarrel cortex in vivo rnLong-term potentiation (LTP) is important for the activity-dependent formation of early cortical circuits. In the neonatal rodent barrel cortex LTP has been so far only studied in vitro. I combined voltage-sensitive dye imaging with extracellular multi-electrode recordings to study whisker stimulation-induced LTP for both the slope of field potential and the number of multi-unit activity in the whisker-to-barrel cortex pathway of the neonatal rat barrel cortex in vivo. Single whisker stimulation at 2 Hz for 10 min induced an age-dependent expression of LTP in postnatal day (P) 0 to P14 rats with the strongest expression of LTP at P3-P5. The magnitude of LTP was largest in the stimulated barrel-related column, smaller in the surrounding septal region and no LTP could be observed in the neighboring barrel. Current source density analyses revealed an LTP-associated increase of synaptic current sinks in layer IV / lower layer II/III at P3-P5 and in the cortical plate / upper layer V at P0-P1. This study demonstrates for the first time an age-dependent and spatially confined LTP in the barrel cortex of the newborn rat in vivo. These activity-dependent modifications during the critical period may play an important role in the development and refinement of the topographic map in the barrel cortex. (An et al., 2012)rnEarly motor activity triggered by gamma and spindle bursts in neonatal rat motor cortexrnSelf-generated neuronal activity generated in subcortical regions drives early spontaneous motor activity, which is a hallmark of the developing sensorimotor system. However, the neuronal activity patterns and functions of neonatal primary motor cortex (M1) in the early movements are still unknown. I combined voltage-sensitive dye imaging with simultaneous extracellular multi-electrode recordings in the neonatal rat S1 and M1 in vivo. At P3-P5, gamma and spindle bursts observed in M1 could trigger early paw movements. Furthermore, the paw movements could be also elicited by the focal electrical stimulation of M1 at layer V. Local inactivation of M1 could significantly attenuate paw movements, suggesting that the neonatal M1 operates in motor mode. In contrast, the neonatal M1 can also operate in sensory mode. Early spontaneous movements and sensory stimulations of paw trigger gamma and spindle bursts in M1. Blockade of peripheral sensory input from the paw completely abolished sensory evoked gamma and spindle bursts. Moreover, both sensory evoked and spontaneously occurring gamma and spindle bursts mediated interactions between S1 and M1. Accordingly, local inactivation of the S1 profoundly reduced paw stimulation-induced and spontaneously occurring gamma and spindle bursts in M1, indicating that S1 plays a critical role in generation of the activity patterns in M1. This study proposes that both self-generated and sensory evoked gamma and spindle bursts in M1 may contribute to the refinement and maturation of corticospinal and sensorimotor networks required for sensorimotor coordination.rn

Simulating event related potentials in tasks involving conflict resolution: a neural network

In this thesis, the main Executive Control theories are exposed. Methods typical of Cognitive and Computational Neuroscience are introduced and the role of behavioural tasks involving conflict resolution in the response elaboration, after the presentation of a stimulus to the subject, are highlighted. In particular, the Eriksen Flanker Task and its variants are discussed. Behavioural data, from scientific literature, are illustrated in terms of response times and error rates. During experimental behavioural tasks, EEG is registered simultaneously. Thanks to this, event related potential, related with the current task, can be studied. Different theories regarding relevant event related potential in this field - such as N2, fERN (feedback Error Related Negativity) and ERN (Error Related Negativity) – are introduced. The aim of this thesis is to understand and simulate processes regarding Executive Control, including performance improvement, error detection mechanisms, post error adjustments and the role of selective attention, with the help of an original neural network model. The network described here has been built with the purpose to simulate behavioural results of a four choice Eriksen Flanker Task. Model results show that the neural network can simulate response times, error rates and event related potentials quite well. Finally, results are compared with behavioural data and discussed in light of the mentioned Executive Control theories. Future perspective for this new model are outlined.

Extracting FSA descriptions of robot behaviours from the dynamics of automatically designed controller networks

Automatic design has become a common approach to evolve complex networks, such as artificial neural networks (ANNs) and random boolean networks (RBNs), and many evolutionary setups have been discussed to increase the efficiency of this process. However networks evolved in this way have few limitations that should not be overlooked. One of these limitations is the black-box problem that refers to the impossibility to analyze internal behaviour of complex networks in an efficient and meaningful way. The aim of this study is to develop a methodology that make it possible to extract finite-state automata (FSAs) descriptions of robot behaviours from the dynamics of automatically designed complex controller networks. These FSAs unlike complex networks from which they're extracted are both readable and editable thus making the resulting designs much more valuable.

Fluctuation properties in random walks on networks and simple integrate and fire models

In questa tesi si è studiato l’insorgere di eventi critici in un semplice modello neurale del tipo Integrate and Fire, basato su processi dinamici stocastici markoviani definiti su una rete. Il segnale neurale elettrico è stato modellato da un flusso di particelle. Si è concentrata l’attenzione sulla fase transiente del sistema, cercando di identificare fenomeni simili alla sincronizzazione neurale, la quale può essere considerata un evento critico. Sono state studiate reti particolarmente semplici, trovando che il modello proposto ha la capacità di produrre effetti "a cascata" nell’attività neurale, dovuti a Self Organized Criticality (auto organizzazione del sistema in stati instabili); questi effetti non vengono invece osservati in Random Walks sulle stesse reti. Si è visto che un piccolo stimolo random è capace di generare nell’attività della rete delle fluttuazioni notevoli, in particolar modo se il sistema si trova in una fase al limite dell’equilibrio. I picchi di attività così rilevati sono stati interpretati come valanghe di segnale neurale, fenomeno riconducibile alla sincronizzazione.

Intermittent spike-wave dynamics in a heterogeneous, spatially extended neural mass model

Generalised epileptic seizures are frequently accompanied by sudden, reversible transitions from low amplitude, irregular background activity to high amplitude, regular spike-wave discharges (SWD) in the EEG. The underlying mechanisms responsible for SWD generation and for the apparently spontaneous transitions to SWD and back again are still not fully understood. Specifically, the role of spatial cortico-cortical interactions in ictogenesis is not well studied. We present a macroscopic, neural mass model of a cortical column which includes two distinct time scales of inhibition. This model can produce both an oscillatory background and a pathological SWD rhythm. We demonstrate that coupling two of these cortical columns can lead to a bistability between out-of-phase, low amplitude background dynamics and in-phase, high amplitude SWD activity. Stimuli can cause state-dependent transitions from background into SWD. In an extended local area of cortex, spatial heterogeneities in a model parameter can lead to spontaneous reversible transitions from a desynchronised background to synchronous SWD due to intermittency. The deterministic model is therefore capable of producing absence seizure-like events without any time dependent adjustment of model parameters. The emergence of such mechanisms due to spatial coupling demonstrates the importance of spatial interactions in modelling ictal dynamics, and in the study of ictogenesis.

Dynamics of Spatial Game Theory Networks with Novel Modifications

Introduction: Advances in biotechnology have shed light on many biological processes. In biological networks, nodes are used to represent the function of individual entities within a system and have historically been studied in isolation. Network structure adds edges that enable communication between nodes. An emerging fieldis to combine node function and network structure to yield network function. One of the most complex networks known in biology is the neural network within the brain. Modeling neural function will require an understanding of networks, dynamics, andneurophysiology. It is with this work that modeling techniques will be developed to work at this complex intersection. Methods: Spatial game theory was developed by Nowak in the context of modeling evolutionary dynamics, or the way in which species evolve over time. Spatial game theory offers a two dimensional view of analyzingthe state of neighbors and updating based on the surroundings. Our work builds upon this foundation by studying evolutionary game theory networks with respect to neural networks. This novel concept is that neurons may adopt a particular strategy that will allow propagation of information. The strategy may therefore act as the mechanism for gating. Furthermore, the strategy of a neuron, as in a real brain, isimpacted by the strategy of its neighbors. The techniques of spatial game theory already established by Nowak are repeated to explain two basic cases and validate the implementation of code. Two novel modifications are introduced in Chapters 3 and 4 that build on this network and may reflect neural networks. Results: The introduction of two novel modifications, mutation and rewiring, in large parametricstudies resulted in dynamics that had an intermediate amount of nodes firing at any given time. Further, even small mutation rates result in different dynamics more representative of the ideal state hypothesized. Conclusions: In both modificationsto Nowak's model, the results demonstrate the network does not become locked into a particular global state of passing all information or blocking all information. It is hypothesized that normal brain function occurs within this intermediate range and that a number of diseases are the result of moving outside of this range.

Examining The Genetic, Social-Cognitive, And Neural Correlates Of Autism Spectrum Disorder Behaviors In Typically Developing Chi

Autism spectrum disorders (ASD) are pervasive developmental disorders that affect approximately 1 in 50 children (Blumberg et al., 2013). Due to the social nature of the deficits that characterize the disorders, many have classified them as disorders of social cognition, which is the process that individuals use in order to successfully interact with members of their own species (Frith & Frith, 2007). Previous research has typically neglected the spectrum nature of ASD in favor of a more categorical approach of ¿autistic¿ versus ¿non-autistic,¿ but the spectrum requires a more continuous approach. Thus, the present study sought to examine the genetic, social-cognitive, and neural correlates of ASD-like traits as well as the relationship between these dimensions in typically developing children. Parents and children completed several quantitative measures examining several areas of social-cognitive functioning, including theory of mind and social functioning, restricted/repetitive behaviors and interests, and adaptive/maladaptive functioning. Children were also asked to undergo an EEG and both parents and children contributed a saliva sample that was used to sequence four single nucleotide polymorphisms (SNPs) of the OXTR gene, rs1042778, rs53576, rs2254298, and rs237897. We successfully demonstrated a significant relationship between behavioral measures of social-cognition and differences in face perception via the N170. However, the directionality of these relationships varied based on the behavioral measure and particular N170 difference scores. We also found support for the associations between the G_G allelic combination of rs1042778 and the A_A and A_G allelic combinations of rs2254298 and increased ASD-like behavior with decreased social-cognitive functioning. In contrast, our results contradict previous findings with rs237897 and imply that individuals with the A_A and A_G genotypes are less similar to those with ASD and have higher social cognitive functioning than those with the G_G genotype. In conclusion, we have demonstrated the existence of ASD-like traits in typically developing children and have shown a link between behavioral, genetic, and neural correlates of social-cognition. These findings demonstrate the importance of considering autism as a spectrum disorder and provide support for the move to a more continuous approach to neurodevelopmental disorders.

Self-assembly of sensory neurons into ganglia-like microtissues

Unraveling intra- and inter-cellular signaling networks managing cell-fate control, coordinating complex differentiation regulatory circuits and shaping tissues and organs in living systems remain major challenges in the post-genomic era. Resting on the laurels of past-century monolayer culture technologies, the cell culture community has only recently begun to appreciate the potential of three-dimensional mammalian cell culture systems to reveal the full scope of mechanisms orchestrating the tissue-like cell quorum in space and time. Capitalizing on gravity-enforced self-assembly of monodispersed primary embryonic mouse cells in hanging drops, we designed and characterized a three-dimensional cell culture model for ganglion-like structures. Within 24h, a mixture of mouse embryonic fibroblasts (MEF) and cells, derived from the dorsal root ganglion (DRG) (sensory neurons and Schwann cells) grown in hanging drops, assembled to coherent spherical microtissues characterized by a MEF feeder core and a peripheral layer of DRG-derived cells. In a time-dependent manner, sensory neurons formed a polar ganglion-like cap structure, which coordinated guided axonal outgrowth and innervation of the distal pole of the MEF feeder spheroid. Schwann cells, present in embryonic DRG isolates, tended to align along axonal structures and myelinate them in an in vivo-like manner. Whenever cultivation exceeded 10 days, DRG:MEF-based microtissues disintegrated due to an as yet unknown mechanism. Using a transgenic MEF feeder spheroid, engineered for gaseous acetaldehyde-inducible interferon-beta (ifn-beta) production by cotransduction of retro-/ lenti-viral particles, a short 6-h ifn-beta induction was sufficient to rescue the integrity of DRG:MEF spheroids and enable long-term cultivation of these microtissues. In hanging drops, such microtissues fused to higher-order macrotissue-like structures, which may pave the way for sophisticated bottom-up tissue engineering strategies. DRG:MEF-based artificial micro- and macrotissue design demonstrated accurate key morphological aspects of ganglions and exemplified the potential of self-assembled scaffold-free multicellular micro-/macrotissues to provide new insight into organogenesis.

The cytoplasmic domain of the ligand ephrinB2 is required for vascular morphogenesis but not cranial neural crest migration

The transmembrane ligand ephrinB2 and its cognate Eph receptor tyrosine kinases are important regulators of vascular morphogenesis. EphrinB2 may have an active signaling role, resulting in bi-directional signal transduction downstream of both ephrinB2 and Eph receptors. To separate the ligand and receptor-like functions of ephrinB2 in mice, we replaced the endogenous gene by cDNAs encoding either carboxyterminally truncated (ephrinB2(DeltaC)) or, as a control, full-length ligand (ephrinB2(WT)). While homozygous ephrinB2(WT/WT) animals were viable and fertile, loss of the ephrinB2 cytoplasmic domain resulted in midgestation lethality similar to ephrinB2 null mutants (ephrinB2(KO)). The truncated ligand was sufficient to restore guidance of migrating cranial neural crest cells, but ephrinB2(DeltaC/DeltaC) embryos showed defects in vasculogenesis and angiogenesis very similar to those observed in ephrinB2(KO/KO) animals. Our results indicate distinct requirements of functions mediated by the ephrinB carboxyterminus for developmental processes in the vertebrate embryo.

Learning real-world stimuli in a neural network with spike-driven synaptic dynamics

We present a model of spike-driven synaptic plasticity inspired by experimental observations and motivated by the desire to build an electronic hardware device that can learn to classify complex stimuli in a semisupervised fashion. During training, patterns of activity are sequentially imposed on the input neurons, and an additional instructor signal drives the output neurons toward the desired activity. The network is made of integrate-and-fire neurons with constant leak and a floor. The synapses are bistable, and they are modified by the arrival of presynaptic spikes. The sign of the change is determined by both the depolarization and the state of a variable that integrates the postsynaptic action potentials. Following the training phase, the instructor signal is removed, and the output neurons are driven purely by the activity of the input neurons weighted by the plastic synapses. In the absence of stimulation, the synapses preserve their internal state indefinitely. Memories are also very robust to the disruptive action of spontaneous activity. A network of 2000 input neurons is shown to be able to classify correctly a large number (thousands) of highly overlapping patterns (300 classes of preprocessed Latex characters, 30 patterns per class, and a subset of the NIST characters data set) and to generalize with performances that are better than or comparable to those of artificial neural networks. Finally we show that the synaptic dynamics is compatible with many of the experimental observations on the induction of long-term modifications (spike-timing-dependent plasticity and its dependence on both the postsynaptic depolarization and the frequency of pre- and postsynaptic neurons).