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Publication Detail
Crossing versus fanning: Model comparison using HCP data
© Springer International Publishing Switzerland 2016.This paper assesses the importance of modelling fiber dispersion in brain regions with complex fiber configurations using a model comparison approach. It is well known that DTI, although popular, is insufficient for describing complex fiber configurations that exist in the brain—such as crossings, bendings and fannings. “Higher order”models have been proposed to overcome this limitation bymodelling crossings with greater accuracy and recent works have reported that up to 90% of white matter voxels contain crossings. However, since these models do not account for bending and fanning, i.e. dispersion, it is unknown if some fiber configurations are better explained by dispersion or by crossing (or by both). To address this problem, we take a model comparison approach on the publicly available state-of-the-art HCP dataset. We consider compartment based single fiber, crossing fiber and dispersion models, which are fitted to the data and ranked using several model selection and validation metrics, such as AIC, BIC and k-fold cross-validation. We generate maps of the brain based on these rankings which quantify the voxels where a single fiber or crossing or dispersion is the preferred model. The results show that 45–50% of the brain’s parenchyma, including the white matter, are better explained by dispersion models, indicating the importance of modelling dispersion in addition to crossings.
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