Estimation of high-dimensional brain connectivity from FMRI data using factor modeling
We consider identifying effective connectivity of brain networks from fMRI time series. The standard vector autoregressive (VAR) models fail to give reliable network estimates, typically involving very large number of nodes. This paper adopts a dimensionality reduction approach based on factor model...
Saved in:
Main Authors: | Shaikh Salleh, Sheikh Hussein, Ting, Chee-Ming, Seghouane, Abd. Krim, Mohd. Noor, A. B. |
---|---|
Format: | Article |
Published: |
IEEE Xplore Digital Library
2014
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/52739/ http://dx.doi.org/10.1109/SSP.2014.6884578 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Estimation of high-dimensional connectivity in FMRI data via subspace autoregressive models
by: Ting, C. M., et al.
Published: (2016) -
Estimating effective connectivity from fMRI data using factor-based subspace autoregressive models
by: Ting, Chee-Ming, et al.
Published: (2015) -
fMRI hemodynamic response function estimation in autoregressive noise by avoiding the drift
by: Seghouane, A. K., et al.
Published: (2017) -
Is first-order vector autoregressive model optimal for fMRI data?
by: Ting, Chee-Ming, et al.
Published: (2015) -
Estimating dynamic cortical connectivity from motor imagery EEG using KALMAN smoother & EM algorithm
by: Samdin, Siti Balqis, et al.
Published: (2014)