About this Research Topic
This Research Topic is a part of the Advanced Graph Theoretical Approaches In Neuroimaging of Neurodegenerative Disorders series:
Advanced Graph Theoretical Approaches In Neuroimaging of Neurodegenerative Disorders, Volume I
Most neurodegenerative diseases present themselves as a disorder of brain connectivity. Neuroimaging facilitated the visualization of the abundant evidence of structural and functional connectivity abnormalities found in these disorders. The connectivity matrixes forming are still poorly understood, as well as their evolution in the course of the disease. In spite of a wealth of collected data for neurodegenerative diseases at different stages, the major challenges remain to understand the structural and functional architecture of these neural circuits and how their particular evolution leads to the emergence of complex changes in brain connectivity. Novel computational techniques such as graph techniques that can capture both the static as well as the dynamic aspect represent a promising tool because they will detect important theory-driven biomarkers for disease progression and prediction. These quantitative methods are believed to be of clinical importance when it comes to select treatment strategies specifically aimed at reducing factors that are associated with worse long-term clinical outcome.
The aim of this Research Topic is to present the current state of the art in the theory of advanced graph theoretical approaches in neuroimaging to study neurodegenerative diseases and provide a quantitative prediction of changes in the connectivity matrix as disease progresses. We are interested in articles that explore changes in the connectivity of neurodegenerative disorders that require advanced graph theoretical approaches. Potential techniques include, but are not limited to:
- Bayesian networks
- Static and dynamic graph analysis
- Prediction networks
- Pinning control networks
- Graph diffusion convolution networks
- Leader-follower dynamic graph networks
Keywords: Graph network, temporal graph, graph convolutional network, leader-follower graph network, neurodegenerative disease, dementia, Parkinson disease
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