Ulster University Logo

Ulster Institutional Repository

A model selection method for nonlinear system identification based fMRI effective connectivity analysis.

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Li, X, Coyle, Damien, Maguire, Liam, McGinnity, TM and Benali, H (2011) A model selection method for nonlinear system identification based fMRI effective connectivity analysis. IEEE Transactions on Medical Imaging, 30 (7). pp. 1365-1380. [Journal article]

Full text not available from this repository.

URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5714750

DOI: 10.1109/TMI.2011.2116034

Abstract

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic casual model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:17408
Deposited By:Dr Damien Coyle
Deposited On:29 Mar 2011 14:26
Last Modified:04 Jul 2011 14:30

Repository Staff Only: item control page