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...
| Main Authors: | , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
2009
|
| _version_ | 1797052436730347520 |
|---|---|
| author | Jbabdi, S Woolrich, M Behrens, T |
| author_facet | Jbabdi, S Woolrich, M Behrens, T |
| author_sort | Jbabdi, S |
| collection | OXFORD |
| description | 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 mixture of multivariate Gaussian distributions, with a Dirichlet process prior on the cluster parameters. This type of prior allows us to conveniently model the number of clusters and estimate its posterior distribution directly from the data. An important benefit of using Bayesian modelling is the extension to multiple subjects clustering via a hierarchical mixture of Dirichlet processes. Data from different subjects are used to infer on class parameters and the number of classes at individual and group level. Such a method accounts for inter-subject variability, while still benefiting from combining different subjects data to yield more robust estimates of the individual clusterings. |
| first_indexed | 2024-03-06T18:31:32Z |
| format | Journal article |
| id | oxford-uuid:09cf9c1f-f4b8-4d9f-9151-809c63f62da6 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T18:31:32Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | oxford-uuid:09cf9c1f-f4b8-4d9f-9151-809c63f62da62022-03-26T09:20:20ZMultiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:09cf9c1f-f4b8-4d9f-9151-809c63f62da6EnglishSymplectic Elements at Oxford2009Jbabdi, SWoolrich, MBehrens, TWe 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 mixture of multivariate Gaussian distributions, with a Dirichlet process prior on the cluster parameters. This type of prior allows us to conveniently model the number of clusters and estimate its posterior distribution directly from the data. An important benefit of using Bayesian modelling is the extension to multiple subjects clustering via a hierarchical mixture of Dirichlet processes. Data from different subjects are used to infer on class parameters and the number of classes at individual and group level. Such a method accounts for inter-subject variability, while still benefiting from combining different subjects data to yield more robust estimates of the individual clusterings. |
| spellingShingle | Jbabdi, S Woolrich, M Behrens, T Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. |
| title | Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. |
| title_full | Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. |
| title_fullStr | Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. |
| title_full_unstemmed | Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. |
| title_short | Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. |
| title_sort | multiple subjects connectivity based parcellation using hierarchical dirichlet process mixture models |
| work_keys_str_mv | AT jbabdis multiplesubjectsconnectivitybasedparcellationusinghierarchicaldirichletprocessmixturemodels AT woolrichm multiplesubjectsconnectivitybasedparcellationusinghierarchicaldirichletprocessmixturemodels AT behrenst multiplesubjectsconnectivitybasedparcellationusinghierarchicaldirichletprocessmixturemodels |