Core networks for visual-concrete and abstract thought content: a brain electric microstate analysis

Commonality of activation of spontaneously forming and stimulus-induced mental representations is an often made but rarely tested assumption in neuroscience. In a conjunction analysis of two earlier studies, brain electric activity during visual-concrete and abstract thoughts was studied. The conditions were: in study 1, spontaneous stimulus-independent thinking (post-hoc, visual imagery or abstract thought were identified); in study 2, reading of single nouns ranking high or low on a visual imagery scale. In both studies, subjects' tasks were similar: when prompted, they had to recall the last thought (study 1) or the last word (study 2). In both studies, subjects had no instruction to classify or to visually imagine their thoughts, and accordingly were not aware of the studies' aim. Brain electric data were analyzed into functional topographic brain images (using LORETA) of the last microstate before the prompt (study 1) and of the word-type discriminating event-related microstate after word onset (study 2). Conjunction analysis across the two studies yielded commonality of activation of core networks for abstract thought content in left anterior superior regions, and for visual-concrete thought content in right temporal-posterior inferior regions. The results suggest that two different core networks are automatedly activated when abstract or visual-concrete information, respectively, enters working memory, without a subject task or instruction about the two classes of information, and regardless of internal or external origin, and of input modality. These core machineries of working memory thus are invariant to source or modality of input when treating the two types of information.

SNA--a toolbox for the stoichiometric analysis of metabolic networks

BACKGROUND: Despite recent algorithmic and conceptual progress, the stoichiometric network analysis of large metabolic models remains a computationally challenging problem. RESULTS: SNA is a interactive, high performance toolbox for analysing the possible steady state behaviour of metabolic networks by computing the generating and elementary vectors of their flux and conversions cones. It also supports analysing the steady states by linear programming. The toolbox is implemented mainly in Mathematica and returns numerically exact results. It is available under an open source license from: http://bioinformatics.org/project/?group_id=546. CONCLUSION: Thanks to its performance and modular design, SNA is demonstrably useful in analysing genome scale metabolic networks. Further, the integration into Mathematica provides a very flexible environment for the subsequent analysis and interpretation of the results.

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.

Investigating the value of privacy on online social networks: conjoint analysis

Popularity of Online Social Networks has been recently overshadowed by the privacy problems they pose. Users are getting increasingly vigilant concerning information they disclose and are strongly opposing the use of their information for commercial purposes. Nevertheless, as long as the network is offered to users for free, providers have little choice but to generate revenue through personalized advertising to remain financially viable. Our study empirically investigates the ways out of this deadlock. Using conjoint analysis we find that privacy is indeed important for users. We identify three groups of users with different utility patterns: Unconcerned Socializers, Control-conscious Socializers and Privacy-concerned. Our results provide relevant insights into how network providers can capitalize on different user preferences by specifically addressing the needs of distinct groups in the form of various premium accounts. Overall, our study is the first attempt to assess the value of privacy in monetary terms in this context.

The scholarly networks of Albrecht von Haller and the Economic Society of Bern – a multi-level network analysis

Here, by the example of the transfer of cultivated plants in the context of the correspondence networks of Albrecht von Haller and the Economic Society, a multi-level network analysis is suggested. By a multi-level procedure, the chronological dynamics, the social structure, the spatial distribution and the functional networking are analyzed one after the other. These four levels of network analysis do not compete with each other but are mutually supporting. This aims at a deeper understanding of how these networks contributed to an international transfer of knowledge in the 18th century.

A Comparative Analysis of Beaconless Opportunistic Routing Protocols for Video Dissemination over Flying Ad-Hoc Networks

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes, and also to recover the quality level of the delivered multiple video flows under dynamic network topologies. The user experience on watching live videos must also be satisfactory even in scenarios with network congestion, buffer overflow, and packet loss ratio, as experienced in many FANET multimedia applications. In this paper, we perform a comparative simulation study to assess the robustness, reliability, and quality level of videos transmitted via well-known beaconless opportunistic routing protocols. Simulation results shows that our developed protocol XLinGO achieves multimedia dissemination with Quality of Experience (QoE) support and robustness in a multi-hop, multi-flow, and mobile networks, as required in many multimedia FANET scenarios.

Validation of network communicability metrics for the analysis of brain structural networks.

Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

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.