Neurodegenerative diseases present progressive loss of structure or function of neurons (cognitive impairment), which eventually leads to neuron death. Neurodegeneration affects brain connectivity, leading to disrupted communication between different regions. Brain itself is an extremely complex structure and thanks to cutting-edge imaging modalities; one way to picture it as a study framework is through a set of discrete gray matter regions connected structurally and functionally to each other (brain connectomics). Studying neurodegeneration using graph theory and diffusion models involves understanding how brain networks deteriorate over time and how this deterioration can be quantified and analyzed. This work focuses on (graph) network diffusion models, on how they blueprint the diffusion and aggregation of matter across the connectome.

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Network Diffusion Modeling in Computational Neuroscience

  • Dionysios G. Cheirdaris

摘要

Neurodegenerative diseases present progressive loss of structure or function of neurons (cognitive impairment), which eventually leads to neuron death. Neurodegeneration affects brain connectivity, leading to disrupted communication between different regions. Brain itself is an extremely complex structure and thanks to cutting-edge imaging modalities; one way to picture it as a study framework is through a set of discrete gray matter regions connected structurally and functionally to each other (brain connectomics). Studying neurodegeneration using graph theory and diffusion models involves understanding how brain networks deteriorate over time and how this deterioration can be quantified and analyzed. This work focuses on (graph) network diffusion models, on how they blueprint the diffusion and aggregation of matter across the connectome.