Topographic electrophysiological signatures of FMRI Resting State Networks

Background: fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG. Methodology/Principal Findings: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band. Conclusions/Significance: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.

An Evaluation of Compression Schemes for Wireless Networks

Data gathering, either for event recognition or for monitoring applications is the primary intention for sensor network deployments. In many cases, data is acquired periodically and autonomously, and simply logged onto secondary storage (e.g. flash memory) either for delayed offline analysis or for on demand burst transfer. Moreover, operational data such as connectivity information, node and network state is typically kept as well. Naturally, measurement and/or connectivity logging comes at a cost. Space for doing so is limited. Finding a good representative model for the data and providing clever coding of information, thus data compression, may be a means to use the available space to its best. In this paper, we explore the design space for data compression for wireless sensor and mesh networks by profiling common, publicly available algorithms. Several goals such as a low overhead in terms of utilized memory and compression time as well as a decent compression ratio have to be well balanced in order to find a simple, yet effective compression scheme.

Gravity-based Local Clock Synchronization in Wireless Sensor Networks

Contention-based MAC protocols follow periodic listen/sleep cycles. These protocols face the problem of virtual clustering if different unsynchronized listen/sleep schedules occur in the network, which has been shown to happen in wireless sensor networks. To interconnect these virtual clusters, border nodes maintaining all respective listen/sleep schedules are required. However, this is a waste of energy, if locally a common schedule can be determined. We propose to achieve local synchronization with a mechanism that is similar to gravitation. Clusters represent the mass, whereas synchronization messages sent by each cluster represent the gravitation force of the according cluster. Due to the mutual attraction caused by the clusters, all clusters merge finally. The exchange of synchronization messages itself is not altered by LACAS. Accordingly, LACAS introduces no overhead. Only a not yet used property of synchronization mechanisms is exploited.

Energy-Efficient Management Of Heterogeneous Wireless Sensor Networks

Various applications for the purposes of event detection, localization, and monitoring can benefit from the use of wireless sensor networks (WSNs). Wireless sensor networks are generally easy to deploy, with flexible topology and can support diversity of tasks thanks to the large variety of sensors that can be attached to the wireless sensor nodes. To guarantee the efficient operation of such a heterogeneous wireless sensor networks during its lifetime an appropriate management is necessary. Typically, there are three management tasks, namely monitoring, (re) configuration, and code updating. On the one hand, status information, such as battery state and node connectivity, of both the wireless sensor network and the sensor nodes has to be monitored. And on the other hand, sensor nodes have to be (re)configured, e.g., setting the sensing interval. Most importantly, new applications have to be deployed as well as bug fixes have to be applied during the network lifetime. All management tasks have to be performed in a reliable, time- and energy-efficient manner. The ability to disseminate data from one sender to multiple receivers in a reliable, time- and energy-efficient manner is critical for the execution of the management tasks, especially for code updating. Using multicast communication in wireless sensor networks is an efficient way to handle such traffic pattern. Due to the nature of code updates a multicast protocol has to support bulky traffic and endto-end reliability. Further, the limited resources of wireless sensor nodes demand an energy-efficient operation of the multicast protocol. Current data dissemination schemes do not fulfil all of the above requirements. In order to close the gap, we designed the Sensor Node Overlay Multicast (SNOMC) protocol such that to support a reliable, time-efficient and energy-efficient dissemination of data from one sender node to multiple receivers. In contrast to other multicast transport protocols, which do not support reliability mechanisms, SNOMC supports end-to-end reliability using a NACK-based reliability mechanism. The mechanism is simple and easy to implement and can significantly reduce the number of transmissions. It is complemented by a data acknowledgement after successful reception of all data fragments by the receiver nodes. In SNOMC three different caching strategies are integrated for an efficient handling of necessary retransmissions, namely, caching on each intermediate node, caching on branching nodes, or caching only on the sender node. Moreover, an option was included to pro-actively request missing fragments. SNOMC was evaluated both in the OMNeT++ simulator and in our in-house real-world testbed and compared to a number of common data dissemination protocols, such as Flooding, MPR, TinyCubus, PSFQ, and both UDP and TCP. The results showed that SNOMC outperforms the selected protocols in terms of transmission time, number of transmitted packets, and energy-consumption. Moreover, we showed that SNOMC performs well with different underlying MAC protocols, which support different levels of reliability and energy-efficiency. Thus, SNOMC can offer a robust, high-performing solution for the efficient distribution of code updates and management information in a wireless sensor network. To address the three management tasks, in this thesis we developed the Management Architecture for Wireless Sensor Networks (MARWIS). MARWIS is specifically designed for the management of heterogeneous wireless sensor networks. A distinguished feature of its design is the use of wireless mesh nodes as backbone, which enables diverse communication platforms and offloading functionality from the sensor nodes to the mesh nodes. This hierarchical architecture allows for efficient operation of the management tasks, due to the organisation of the sensor nodes into small sub-networks each managed by a mesh node. Furthermore, we developed a intuitive -based graphical user interface, which allows non-expert users to easily perform management tasks in the network. In contrast to other management frameworks, such as Mate, MANNA, TinyCubus, or code dissemination protocols, such as Impala, Trickle, and Deluge, MARWIS offers an integrated solution monitoring, configuration and code updating of sensor nodes. Integration of SNOMC into MARWIS further increases performance efficiency of the management tasks. To our knowledge, our approach is the first one, which offers a combination of a management architecture with an efficient overlay multicast transport protocol. This combination of SNOMC and MARWIS supports reliably, time- and energy-efficient operation of a heterogeneous wireless sensor network.

Repeatability analysis of global and local metrics of brain structural networks

Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric and neurodegenerative disorders. In order to ensure the validity of results in clinical settings the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, nineteen healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in networks comparisons.

Intussusceptive microvascular growth: a common alternative to capillary sprouting.

Intussusceptive capillary growth represents a new principle for microvascular growth as described in the lungs of growing rats. According to this concept, the capillary network expands by the formation of slender transcapillary tissue pillars, which give rise to new vascular meshes. The process was first observed in Mercox casts of the lung microvasculature, which revealed the existence of multiple tiny holes with diameters around 1.5 microns. Consecutive transmission electron microscopic investigation of serial sections demonstrated that the holes corresponded to slender tissue pillars (Burri and Tarek, 1990). The corrosion cast technique thus appears to be an adequate screening method for intussusceptive growth. In the present investigation, Mercox casts of various vascular systems, namely, those of the eye, submandibular gland, heart, liver, stomach, small and large intestine, trachea, kidney, uterus and ovary were prepared from rats aged between 4 and 9 weeks in order to screen them for the existence of the typical tiny holes representing tissue pillars. In all organs investigated, these structures were observed in various locations to a variable degree. They were mainly encountered within dilated vascular segments or at triple or quadruple branching points of the circulation. Even in capillary networks with a three-dimensional arrangement could these pillars be detected. Intussusception thus appears to be a principle of growth appertaining to many vascular systems.

The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides

The application of pesticides and fertilizers in agricultural areas is of crucial importance for crop yields. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of their speed and effectiveness in the spraying operation. However, some factors may reduce the yield, or even cause damage (e.g., crop areas not covered in the spraying process, overlapping spraying of crop areas, applying pesticides on the outer edge of the crop). Weather conditions, such as the intensity and direction of the wind while spraying, add further complexity to the problem of maintaining control. In this paper, we describe an architecture to address the problem of self-adjustment of the UAV routes when spraying chemicals in a crop field. We propose and evaluate an algorithm to adjust the UAV route to changes in wind intensity and direction. The algorithm to adapt the path runs in the UAV and its input is the feedback obtained from the wireless sensor network (WSN) deployed in the crop field. Moreover, we evaluate the impact of the number of communication messages between the UAV and the WSN. The results show that the use of the feedback information from the sensors to make adjustments to the routes could significantly reduce the waste of pesticides and fertilizers.

Virtual Infrastructures as a Service enabling Converged Optical Networks and Data Centres

Abstract Cloud computing service emerged as an essential component of the Enterprise {IT} infrastructure. Migration towards a full range and large-scale convergence of Cloud and network services has become the current trend for addressing requirements of the Cloud environment. Our approach takes the infrastructure as a service paradigm to build converged virtual infrastructures, which allow offering tailored performance and enable multi-tenancy over a common physical infrastructure. Thanks to virtualization, new exploitation activities of the physical infrastructures may arise for both transport network and Data Centres services. This approach makes network and Data Centres’ resources dedicated to Cloud Computing to converge on the same flexible and scalable level. The work presented here is based on the automation of the virtual infrastructure provisioning service. On top of the virtual infrastructures, a coordinated operation and control of the different resources is performed with the objective of automatically tailoring connectivity services to the Cloud service dynamics. Furthermore, in order to support elasticity of the Cloud services through the optical network, dynamic re-planning features have been provided to the virtual infrastructure service, which allows scaling up or down existing virtual infrastructures to optimize resource utilisation and dynamically adapt to users’ demands. Thus, the dynamic re-planning of the service becomes key component for the coordination of Cloud and optical network resource in an optimal way in terms of resource utilisation. The presented work is complemented with a use case of the virtual infrastructure service being adopted in a distributed Enterprise Information System, that scales up and down as a function of the application requests.

When the systems fail to communicate. Cerebral resting state networks in schizophrenia spectrum disorders.

There have been numerous attempts to reveal the neurobiological basis of schizophrenia spectrum disorders. Results however, remain as heterogeneous as the schizophrenia spectrum disorders itself. Therefore, one aim of this thesis was to divide patients affected by this disorder into subgroups in order to homogenize the results of future studies. In a first study it is suggested that psychopathological rating scales should focus on symptoms-clusters that may have a common neurophysiological background. The here presented Bern Psychopathology Scale (BPS) proposes that alterations in three wellknown brain systems (motor, language, and affective) are largely leading to the communication failures observable on a behavioral level, but also - as repeatedly hypothesized - to dysconnectivity within and between brain systems in schizophrenia spectrum disorders. The external validity of the motor domain in the BPS was tested against the objective measure of 24 hours wrist actigraphy, in a second study. The subjective, the quantitative, as well as the global rating of the degree of motor disorders in this patient group showed significant correlations to the acquired motor activity. This result confirmed in a first step the practicability of the motor domain of the BPS, but needs further validation regarding pathological brain alterations. Finally, in a third study (independent from the two other studies), two cerebral Resting State Networks frequently altered in schizophrenia were investigated for the first time using simultaneous EEG/fMRI: The well-known default mode network and the left working memory network. Besides the changes in these fMRI-based networks, there are well-documented findings that patients exhibit alterations in EEG spectra compared to healthy controls. However, only through the multimodal approach it was possible to discover that patients with schizophrenia spectrum disorders have a slower driving frequency of the Resting State Networks compared to the matched healthy controls. Such a dysfunctional coupling between neuronal frequency and functional brain organization could explain in a uni- or multifactorial way (dysfunctional cross-frequency coupling, maturational effects, vigilance fluctuations, task-related suppression), how the typical psychotic symptoms might occur. To conclude, the major contributions presented in this thesis were on one hand the development of a psychopathology rating scale that is based on the assumption of dysfunctional brain networks, as well as the new evidence of a dysfunctional triggering frequency of Resting State Networks from the simultaneous EEG/fMRI study in patients affected by a schizophrenia spectrum disorder.

Functional connectivity in BOLD and CBF data: similarity and reliability of resting brain networks

Resting-state functional connectivity (FC) fMRI (rs-fcMRI) offers an appealing approach to mapping the brain's intrinsic functional organization. Blood oxygen level dependent (BOLD) and arterial spin labeling (ASL) are the two main rs-fcMRI approaches to assess alterations in brain networks associated with individual differences, behavior and psychopathology. While the BOLD signal is stronger with a higher temporal resolution, ASL provides quantitative, direct measures of the physiology and metabolism of specific networks. This study systematically investigated the similarity and reliability of resting brain networks (RBNs) in BOLD and ASL. A 2×2×2 factorial design was employed where each subject underwent repeated BOLD and ASL rs-fcMRI scans on two occasions on two MRI scanners respectively. Both independent and joint FC analyses revealed common RBNs in ASL and BOLD rs-fcMRI with a moderate to high level of spatial overlap, verified by Dice Similarity Coefficients. Test-retest analyses indicated more reliable spatial network patterns in BOLD (average modal Intraclass Correlation Coefficients: 0.905±0.033 between-sessions; 0.885±0.052 between-scanners) than ASL (0.545±0.048; 0.575±0.059). Nevertheless, ASL provided highly reproducible (0.955±0.021; 0.970±0.011) network-specific CBF measurements. Moreover, we observed positive correlations between regional CBF and FC in core areas of all RBNs indicating a relationship between network connectivity and its baseline metabolism. Taken together, the combination of ASL and BOLD rs-fcMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of RBNs. These findings pave the way for future BOLD and ASL rs-fcMRI studies in clinical populations that are carried out across time and scanners.

The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.