Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

ACKNOWLEDGMENTS MW and RVD have been supported by the German Federal Ministry for Education and Research (BMBF) via the Young Investigators Group CoSy-CC2 (grant no. 01LN1306A). JFD thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. JK acknowledges the IRTG 1740 funded by DFG and FAPESP. MT Gastner is acknowledged for providing his data on the airline, interstate, and Internet network. P Menck thankfully provided his data on the Scandinavian power grid. We thank S Willner on behalf of the entire zeean team for providing the data on the world trade network. All computations have been performed using the Python package pyunicorn [41] that is available at https://github.com/pik-copan/pyunicorn.Peer reviewedPreprin

Spatial networks with wireless applications

Many networks have nodes located in physical space, with links more common between closely spaced pairs of nodes. For example, the nodes could be wireless devices and links communication channels in a wireless mesh network. We describe recent work involving such networks, considering effects due to the geometry (convex,non-convex, and fractal), node distribution, distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

We introduce the \texttt{pyunicorn} (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure

The Molecular-enriched Functional Circuits Underlying Consciousness and Cognition

Homo Sapiens consist of trillions of atoms, each inanimate, yet somehow collectively constituting a conscious being. The fundamental question of how organisms are organised to beget consciousness and cognition has largely been approached through independent examination of the structure and function of the nervous system at varying levels of granularity. As neuroscience progresses, it has thus increasingly fragmented into separate streams of research which study the brain at these different scales. This has resulted in the field becoming “data rich, but theory poor”, which is largely attributable to the paucity of methods which bridge these levels of analysis to provide novel trans-hierarchical insights and inform unified theories. The research in this doctoral thesis therefore aims to explore how a specific type of multimodal analysis - Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) – can begin to bridge the theoretic void between molecular level mechanisms and systems levels dynamics to provide novel perspectives on the function and dysfunction of the brain. First, I provide a narrative synthesis of the challenges precluding a meaningful understanding of the human brain utilising conventional functional neuroimaging and outlining how incorporation of molecular information may help overcome these limitations. Specifically, by embedding functional dynamics in the molecular landscape of the brain, we can begin to move from the simple characterisation of “where” cognitive phenomena may be within the brain towards mechanistic accounts of “how” they are produced. Additionally, this offers enticing opportunities to link pharmacological treatments to novel molecular-network based biomarkers. Second, I explore how networks enriched with the spatial configurations of serotonergic and dopaminergic receptor subtypes are modulated by lysergic acid diethylamide (LSD) as compared to placebo in healthy participants. The results highlight the challenges of disentangling pharmacodynamics of drugs exhibiting rich pharmacology as well as identifying differential relationship between serotonergic and dopaminergic networks and phenomenological sub- components of psychedelic state. Third, I expand the remit of molecular-enriched network analyses beyond pure psychopharmacology to examine the direct and indirect actions of propofol anaesthesia on inhibitory and modulatory neurotransmission at both rest as well as during a naturalistic listening task. This work demonstrates for the first time that these molecular-networks can capture broader perceptual and cognitive-driven network reconfigurations as well as indirect pharmacological actions on neuromodulatory systems. Moreover, it provides evidence that the effects of propofol on consciousness are enacted through both direct inhibitory as well as indirect neuromodulatory mechanisms.Finally, I produce normative models of networks enriched with the principal neuromodulatory, excitatory, and inhibitory transmitter systems, testing their capacity to characterise neural dysfunction within and across several neuropsychiatric disorders. This work provides a computational foundation for large scale integration of molecular mechanisms and functional imaging to provide novel individualised biomarkers for neuropsychiatric disorders. Collectively, this thesis offers methodological and theoretical progress towards a trans-hierarchical characterisation of the human brain, providing insights into the neural correlates of both conscious contents and level as well as the perturbations underlying key neuropsychiatric conditions