Universiti Teknologi Malaysia Institutional Repository

Estimation of high-dimensional brain connectivity from FMRI data using factor modeling

Shaikh Salleh, Sheikh Hussein and Ting, Chee-Ming and Seghouane, Abd. Krim and Mohd. Noor, A. B. (2014) Estimation of high-dimensional brain connectivity from FMRI data using factor modeling. IEEE Workshop on Statistical Signal Processing Proceedings . pp. 73-76.

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1109/SSP.2014.6884578

Abstract

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 modeling, to enable effective and efficient high-dimensional VAR analysis of large network connectivity. We derive a subspace VAR (SVAR) model from the factor model (FM) in which the observations are driven by a lower dimensional subspace of common latent factors, following an autoregressive dynamics. We consider the principal components (PC) method which can produce consistent estimators for the FM, and the resulting SVAR model, even when the dimension is large. This leads to robust large network analysis. Besides, estimates based on the main principal subspace can reveal global connectivity structure. Evaluation on a realistic simulated fMRI dataset shows that the proposed SVAR model with PC estimation can accurately detect the presence of connections and reasonably identify their causal directions, even for a large network.

Item Type:Article
Uncontrolled Keywords:vector autoregressive model, factor model, brain effective connectivity, fMRI
Subjects:Q Science > QH Natural history
Divisions:Biosciences and Medical Engineering
ID Code:52739
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:01 Feb 2016 11:54
Last Modified:30 Jun 2018 08:26

Repository Staff Only: item control page