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Challenges for Graph Theory in Human Neuroscience
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Katelyn Arnemann (Neuroscience, UC Berkeley)
Graph theory has been applied widely to studies of the human brain, advancing understanding of cognition and neurological diseases. However, the application of graph theory to human neuroscience faces many challenges. We will briefly overview examples of the motivation for and insights enabled by modeling the brain as a graph. We will then explore challenges for graph theory in human neuroscience, including challenges with defining the elements of the graphs, comparing graphs, and calculating and computing informative graph properties. Examples will be drawn from my work applying graph theory to neuroimaging (MRI and PET) to study human aging and Alzheimer’s disease.
License: CC BY-NC-SA 4.0
Graph theory has been applied widely to studies of the human brain, advancing understanding of cognition and neurological diseases. However, the application of graph theory to human neuroscience faces many challenges. We will briefly overview examples of the motivation for and insights enabled by modeling the brain as a graph. We will then explore challenges for graph theory in human neuroscience, including challenges with defining the elements of the graphs, comparing graphs, and calculating and computing informative graph properties. Examples will be drawn from my work applying graph theory to neuroimaging (MRI and PET) to study human aging and Alzheimer’s disease.
License: CC BY-NC-SA 4.0
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