Bayesian inference of whole-brain structural connections
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2021-06-18
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en
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Abstract
Structural brain connectivity is used create models of white-matter tract networks within
the brain. The location and direction of these structural connections can be estimated
by using di usion-weighted imaging data(DWI). Current methods have failed to pro-
vide a de nitive model of the structural connectivity of the brain from the data that
DWI produces. Previous approaches have often relied on the selection of an arbitrary
threshold. In this thesis I provide four di erent generative models based on biophysical
characteristics found in the brain. The characteristics examined are e ciency of con-
nections, modularity of the brain and similarity across human brains. These models can
describe how the structural connectivity and the prior lead to observed and predicted
streamline counts from DWI data, which are validated using the predictive power of the
generative models. It is shown that using the biophysically motivated characteristics
provide a signi cant improvement of predictive power over random models.
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Faculteit der Sociale Wetenschappen