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

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.
LanguageEnglish
Pages1365-1380
Number of pages17
JournalIEEE Transactions on Medical Imaging
Volume30
Issue number7
DOIs
Publication statusPublished - Jul 2011

Fingerprint

Nonlinear systems
Identification (control systems)
Magnetic Resonance Imaging
Nonlinear Dynamics
Dynamic models
Economic Models
Model structures
Visual Pathways
Brain
Differential equations
Economics
Experiments

Cite this

@article{78ec7a54767c4403a62d8128414416a5,
title = "A model selection method for nonlinear system identification based fMRI effective connectivity analysis.",
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.",
author = "X Li and Damien Coyle and Liam Maguire and TM McGinnity and H Benali",
year = "2011",
month = "7",
doi = "10.1109/TMI.2011.2116034",
language = "English",
volume = "30",
pages = "1365--1380",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
number = "7",

}

TY - JOUR

T1 - A model selection method for nonlinear system identification based fMRI effective connectivity analysis.

AU - Li, X

AU - Coyle, Damien

AU - Maguire, Liam

AU - McGinnity, TM

AU - Benali, H

PY - 2011/7

Y1 - 2011/7

N2 - 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.

AB - 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.

U2 - 10.1109/TMI.2011.2116034

DO - 10.1109/TMI.2011.2116034

M3 - Article

VL - 30

SP - 1365

EP - 1380

JO - IEEE Transactions on Medical Imaging

T2 - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 7

ER -