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• W2904662668 abstract "The human brain is as much fascinating as complicated: this is the reason why it has always captured scientists’ attention in several fields of research, from biology to medicine, from psychology to engineering. In this context various non-invasive technologies have been optimized in order to allow the measure of signals, able to describe brain activities. These data, derived from measurement methods that largely differ in their nature, have opened the door to new characterizations of this organ, that highlighted the main features of its operating principles. Brain signals indeed have revealed to be fluctuating during time, both during a specific task, and when we are not carrying on any activities. Furthermore, a selective coordination among different regions of the brain has emerged.As engineers, we are particularly attracted by the description of our brain as a graph, whose nodes and edges can be representative of several different elements, at distinct spatial scales (from single neurons to large brain areas). In the last decades, wide attention has been devoted to reproduce and explain the complex dynamics of the brain elements by means of computational models. Graph theory tools, as well as the design of population models, allow the exploitation of many mathematical tools, helpful to enlarge the knowledge of healthy and damaged brains functioning, by means of brain networks.Interestingly, the incapability of human brains to work properly in case of disease, has found to be correlated with dysfunctions in the activity of mitochondria, the organellesthat produce large part of the cells’ energy. In particular, specific relationships have been reported among neurological diseases and impairments in mitochondrial dynamics, which refers to the continuous change in shape of mitochondria, by means of fusion and fissionprocesses. Although the existing link between brain and mitochondria is still ambiguous and under debate, the huge amount of energy required by our brain to work properlysuggests a larger mitochondrial-dependence of the brain than of the other organs.In this thesis we report the results of our research, aimed to investigate a few aspects of this complex brain-mitochondria relationship. We focus on mitochondrial dynamics and brain network, as well as on suitable mathematical models used to describe them.Specifically, the main topics handled in this work can be summarized as follows.Population models for mitochondrial dynamics. We propose a modified preypredator non-linear population model to simulate the main processes, which take part in the mitochondrial dynamics, and the ones that are strongly related to it, without neglecting the energy production process. We present two possible setups, which differ inthe inclusion of a feedback link between the available energy and the formation of new mitochondria. We discuss their dynamics, and their potential in reproducing biologicalbehaviors.Brain signals: comparison of datasets derived through different technologies. We analyze two different datasets of brain signals, recorded with various methods (functional magnetic resonance imaging, fMRI, and magnetoencephalography, MEG), both in condition of no activity and during an attentional task. The aim of the analysisis twofold: the investigation of the spontaneous activity of the brain, and the exploration of possible relationships between the two different techniques.Brain network: a Kuramoto-based description. We analyze empirical brain data by means of their oscillatory features, with the purpose of highlighting the characteristics that a computational phase-model should be able to reproduce. Hence, we use a modified version of the classic Kuramoto model to reproduce the empirical oscillatory characteristics.Analysis and control of Kuramoto networks. Most of the theoretical contribution of this thesis refers to analytical results on Kuramoto networks. We analyze the topologicaland intrinsic conditions required to achieve a desired pattern of synchronization, represented by fully or clustered synchronized configuration of oscillators." @default.
• W2904662668 created "2018-12-22" @default.
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• W2904662668 date "2018-01-14" @default.
• W2904662668 modified "2023-09-27" @default.
• W2904662668 title "Population models for complex non-linear phenomena in biology: from mitochondrial dynamics to brain networks" @default.
• W2904662668 hasPublicationYear "2018" @default.
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