Adaptive spatiotemporal modelling and estimation of the event-related fMRI responses

• Published in: Signal processing Vol. 87; no. 11; pp. 2810 - 2822
• Main Authors: Luo, Huaien, Puthusserypady, Sadasivan
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2007; Elsevier Science
• Subjects: Activation detection; Applied sciences; Biological and medical sciences; Canonical correlation analysis (CCA); Computerized, statistical medical data processing and models in biomedicine; Detection, estimation, filtering, equalization, prediction; Event-related fMRI; Exact sciences and technology; General linear model (GLM); Information, signal and communications theory; Least mean square (LMS) algorithm; Medical management aid. Diagnosis aid; Medical sciences; Receiver operating characteristic (ROC); Signal and communications theory; Signal, noise; Telecommunications and information theory; Event-related fMRI; General linear model (GLM); Receiver operating characteristic (ROC); Activation detection; Least mean square (LMS) algorithm; Canonical correlation analysis (CCA); Data analysis; Adaptive algorithm; Spatiotemporal filter; Functional analysis; Adaptive filter; Nuclear magnetic resonance imaging; Modeling; Adaptive method; Canonical correlation; Linear model; Correlation method; Canonical analysis; Accuracy; Linear system; Correlation analysis; Medical imagery; Functional imaging; Least mean squares methods
• ISSN: 0165-1684; 1872-7557; 1872-7557
• DOI: 10.1016/j.sigpro.2007.05.020

Functional magnetic resonance imaging (fMRI) data analysis is a challenging problem due to the underlying physiological complexity of the brain and the scanning process. From engineering perspective, the fMRI data analysis can be viewed as a system modelling problem. In this paper, assuming the fMRI signal as the output of an unknown linear time-invariant system, a spatiotemporal adaptive filter is proposed to model the spatial activation patterns as well as the haemodynamic response (HDR) to the event-related stimulus. The well-known least mean square adaptive algorithm is used for estimating the coefficients of the spatiotemporal filter. The proposed method is shown to be equivalent to the canonical correlation analysis method. It is then extended to multiple event type scenarios to estimate the HDRs of each event type. Results from simulated as well as real fMRI data show that these adaptive modelling schemes can capture the variations of the HDR at different regions of the brain and hence enhance the estimation accuracy of the activation patterns.