Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure
10.1016/j.neuroimage.2020.116611
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Academic Press Inc.
2021
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| الوصول للمادة أونلاين: | https://scholarbank.nus.edu.sg/handle/10635/198745 |
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sg-nus-scholar.10635-1987452024-11-11T09:07:27Z Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure Pavlović, D.M. Guillaume, B.R.L. Towlson, E.K. Kuek, N.M.Y. Afyouni, S. Vértes, P.E. Yeo, B.T.T. Bullmore, E.T. Nichols, T.E. BIOMEDICAL ENGINEERING PHYSIOLOGY ELECTRICAL AND COMPUTER ENGINEERING LIFE SCIENCES INSTITUTE Community detection Firth estimation Integrated classification likelihood criterion Likelihood ratio Mixture models Modularity Multi-subject network analysis Network analysis Permutation test Stochastic block model Stochastic blockmodel Variational approximation Wald test 10.1016/j.neuroimage.2020.116611 NeuroImage 220 116611 2021-08-23T03:23:31Z 2021-08-23T03:23:31Z 2020-10-15 Article Pavlović, D.M., Guillaume, B.R.L., Towlson, E.K., Kuek, N.M.Y., Afyouni, S., Vértes, P.E., Yeo, B.T.T., Bullmore, E.T., Nichols, T.E. (2020-10-15). Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure. NeuroImage 220 : 116611. ScholarBank@NUS Repository. https://doi.org/10.1016/j.neuroimage.2020.116611 10538119 https://scholarbank.nus.edu.sg/handle/10635/198745 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ Academic Press Inc. Scopus OA2020 |
| institution |
National University of Singapore |
| building |
NUS Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NUS Library |
| collection |
ScholarBank@NUS |
| topic |
Community detection Firth estimation Integrated classification likelihood criterion Likelihood ratio Mixture models Modularity Multi-subject network analysis Network analysis Permutation test Stochastic block model Stochastic blockmodel Variational approximation Wald test |
| spellingShingle |
Community detection Firth estimation Integrated classification likelihood criterion Likelihood ratio Mixture models Modularity Multi-subject network analysis Network analysis Permutation test Stochastic block model Stochastic blockmodel Variational approximation Wald test Pavlović, D.M. Guillaume, B.R.L. Towlson, E.K. Kuek, N.M.Y. Afyouni, S. Vértes, P.E. Yeo, B.T.T. Bullmore, E.T. Nichols, T.E. Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| description |
10.1016/j.neuroimage.2020.116611 |
| author2 |
BIOMEDICAL ENGINEERING |
| author_facet |
BIOMEDICAL ENGINEERING Pavlović, D.M. Guillaume, B.R.L. Towlson, E.K. Kuek, N.M.Y. Afyouni, S. Vértes, P.E. Yeo, B.T.T. Bullmore, E.T. Nichols, T.E. |
| format |
Article |
| author |
Pavlović, D.M. Guillaume, B.R.L. Towlson, E.K. Kuek, N.M.Y. Afyouni, S. Vértes, P.E. Yeo, B.T.T. Bullmore, E.T. Nichols, T.E. |
| author_sort |
Pavlović, D.M. |
| title |
Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| title_short |
Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| title_full |
Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| title_fullStr |
Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| title_full_unstemmed |
Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| title_sort |
multi-subject stochastic blockmodels for adaptive analysis of individual differences in human brain network cluster structure |
| publisher |
Academic Press Inc. |
| publishDate |
2021 |
| url |
https://scholarbank.nus.edu.sg/handle/10635/198745 |
| _version_ |
1821196092439527424 |