Estimation of high-dimensional connectivity in FMRI data via subspace autoregressive models
We consider the challenge in estimating effective connectivity of brain networks with a large number of nodes from fMRI data. The classical vector autoregressive (VAR) modeling tends to produce unreliable estimates for large dimensions due to the huge number of parameters. We propose a subspace esti...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE Computer Society
2016
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| Subjects: | |
| Online Access: | http://eprints.utm.my/73097/ http://eprints.utm.my/73097/ |
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