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Like all natural systems, the mammalian brain and its neurons are governed by the fundamental principles of physics. Simplified computer simulations based on such principles help us see through the high complexity of especially the human brain and ultimately figure out how it works. In the scope of this thesis, two biologically realistic models were created focusing on, firstly the macroscopic and secondly the microscopic structure of the brain. The key component to both of these morphological simulations is the principle of wiring optimisation. First, combining dimensionality reduction methods and biologically inspired modelling based on optimal wiring, this thesis develops a method to simulate how the gyrification pattern of the mammalian brain emerges, differs from species to species, and changes due to pathological changes in neuron connectivity. The gyrification model is based on two biology-driven key principles: First, neuron placement follows wiring optimisation requirements and second, local connectivity between neurons is strong while long range connectivity is sparse as observed in the mammalian cortex. Many studies from the past saw the formation of gyri and sulci as the result of the surface of the cortex trying to expand in the limited cavity of the skull. The simulation described here shows that even without the constraint of the skull, gyrification still emerges when applying a biological neural connectivity distribution in addition to wiring optimisation. The first model provides new insights into the macroscopic structure of the brain but lacks microscopic detail. The second model is also based on optimal wiring but focused on reproducing the anatomical neuronal structure at the level of single cells. It provides a new algorithm and a tool to repair and preserve the microscopic structure of neuron morphology reconstructions. This is especially relevant for human neurons since here, data is extremely hard to come by, and the data that is available mostly originates from patients with diseases like severe epilepsy. Since the reconstruction process is a delicate procedure, the anatomical structure of reconstructed neurons is oftentimes severed by dendrites accidentally being cut. The recovered anatomy of neuronal dendrites is, however, pivotal to study the functionality of human and nonhuman neurons, which is further illustrated by analysing passive electrophysiological differences between human and mouse neurons. In summary, the thesis shows that optimal wiring is a useful guiding principle to simulate and better understand macroscopic and microscopic anatomical structure of the brain at the level of cortical folding as well as individual dendritic trees of nerve cells.

The wiring of the brain provides the anatomical skeleton for cognition and behavior. Connections among brain regions have a diverse and characteristic strength. This strength heterogeneity is captured by the wiring cost and homophily principles. Moreover, brains have a characteristic global network topology, including modularity and short path lengths. However, the mechanisms underlying the inter-regional wiring principles and global network topology of brains are unknown. Here, we address this issue by modeling the ontogeny of brain connectomes. We demonstrate that spatially embedded and heterochronous neurogenetic gradients, without the need of axonal-guidance molecules or activity-dependent plasticity, can reconstruct the wiring principles and shape the global network topology observed in adult brain connectomes. Thus, two fundamental dimensions, that is, space and time, are key components of a plausible neurodevelopmental mechanism with a universal scope, encompassing vertebrate and invertebrate brains.

Mild traumatic brain injury (mTBI) may affect normal cognition and behavior by disrupting the functional connectivity networks that mediate efficient communication among brain regions. In this study, we analyzed brain connectivity profiles from resting state Magnetoencephalographic (MEG) recordings obtained from 31 mTBI patients and 55 normal controls. We used phase-locking value estimates to compute functional connectivity graphs to quantify frequency-specific couplings between sensors at various frequency bands. Overall, normal controls showed a dense network of strong local connections and a limited number of long-range connections that accounted for approximately 20% of all connections, whereas mTBI patients showed networks characterized by weak local connections and strong long-range connections that accounted for more than 60% of all connections. Comparison of the two distinct general patterns at different frequencies using a tensor representation for the connectivity graphs and tensor subspace analysis for optimal feature extraction showed that mTBI patients could be separated from normal controls with 100% classification accuracy in the alpha band. These encouraging findings support the hypothesis that MEG-based functional connectivity patterns may be used as biomarkers that can provide more accurate diagnoses, help guide treatment, and monitor effectiveness of intervention in mTBI., Highlights • We analyzed resting state connectivity profiles in 31 mTBI patients and 55 controls. • We quantified frequency-specific connectivity couplings using phase-locking values. • Normal control networks showed dense local and sparse long-range connections. • TBI patient networks showed sparse local and dense long-range connections. • Tensor subspace analysis could classify subjects with 100% accuracy in the α band

The study of connectivity is central in the diverse disciplines of neuroscience. On one hand, the structured definition of network connectivity is an essential step in network simulations. On the other hand, we can derive connectivity information from experimental data and various theoretical models at multiple scales. However, the connectivity information in these two contexts is represented differently. This results in a language gap limiting the flow of knowledge learned at different levels of abstraction. In this work, we present a first step in the creation of a shared visual language to bridge this gap between model based and empirical neuroscience, allowing us to work towards a single integrated representation of the brain.We have developed a visual and source-agnostic interactive interface to generate connectivity in neural networks at various scales. Based on NeuroScheme [1] and the Connection Set Algebra (CSA)[2], we can generate connectivity and use it in simulator-specific scripts to later perform simulations of the dynamics of the network. Our approach allows us to interactively create, explore and visualize connectivity even for large scale networks where probability based connections are used to describe the synapse generation. Here we show initial results of the tool applied to Potjan's and Diesmann microcircuit model as an initial use case for describing and exploring the connectivity.With this approach, we offer the neuroscientific community a generic tool for the easy generation and exploration of connectivity. The lack of dependency on a specific simulator makes this tool a good starting point for validation of complex neural network models using many simulation and emulation platforms, particularly when coupled. Our future applications involve incorporating this tool to complete workflows consisting of raw data processing, interactive exploration, creation and visualization of abstract connectivity models, simulation, analysis and validation.