Estimation of high-dimensional connectivity in FMRI data via subspace autoregressive models

• Main Authors: Ting, C. M., Seghouane, A. K., Salleh, S. H.
• 格式: Conference or Workshop Item
• 出版: IEEE Computer Society 2016
• 主題: QH Natural history
• 在線閱讀: http://eprints.utm.my/id/eprint/73097/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84987892034&doi=10.1109%2fSSP.2016.7551799&partnerID=40&md5=32b51c62976b05b5c2a2865f1f2a45e3

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...