Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models.
We propose a hierarchical infinite mixture model approach to address two issues in connectivity-based parcellations: (i) choosing the number of clusters, and (ii) combining data from different subjects. In a Bayesian setting, we model voxel-wise anatomical connectivity profiles as an infinite mixtur...
Главные авторы: | Jbabdi, S, Woolrich, M, Behrens, T |
---|---|
Формат: | Journal article |
Язык: | English |
Опубликовано: |
2009
|
Схожие документы
-
Tractography segmentation using a hierarchical Dirichlet processes mixture model
по: Wang, Xiaogang, и др.
Опубликовано: (2020) -
Spatially constrained hierarchical parcellation of the brain with resting-state fMRI.
по: Blumensath, T, и др.
Опубликовано: (2013) -
Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.
по: Kezi Yu, и др.
Опубликовано: (2017-01-01) -
Hierarchical Dirichlet processes
по: Teh, Y, и др.
Опубликовано: (2006) -
Hierarchical Dirichlet Process Based Gamma Mixture Modeling for Terahertz Band Wireless Communication Channels
по: Erhan Karakoca, и др.
Опубликовано: (2022-01-01)