Abstract #301461

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JSM 2003 Abstract #301461
Activity Number: 251
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #301461
Title: State-Space Modeling of Effective Connectivity in fMRI Experiments
Author(s): Moon-ho Ringo Ho*+ and Hernando Ombao and Robert H. Shumway
Companies: University of Illinois at Urbana-Champaign and University of Illinois and University of California
Address: Psychology, Champaign, IL, 61820,
Keywords: functional magnetic resonance imaging ; brain connectivity ; state-space models ; Kalman filter ; random effects
Abstract:

Effective connectivity (EC) analysis aims to study the influence of one neuronal system over another. Structural equation modeling (SEM) and time-varying parameter regression (TVPR) have been used in the fMRI studies for modeling EC. Usually in SEM, a within-subject covariance matrix of the regions-of-interest (ROI) is derived, and a path model is then fitted to this matrix. This approach ignores the temporal correlation in the data and assumes connectivity to be time-invariant. TVPR relaxes such assumption to allow time-varying connectivity, but extensions to handle multiple brain regions have not been discussed. Both methods use observed fMRI signals in the connectivity analysis with the noise confounded in them. To handle these limitations, a new time series model will be proposed which can (1) handle multiple ROI, (2) use the fMRI signal without noise confounded, (3) allow modeling temporal correlation, and (4) allow connectivity to vary over different experimental conditions or time. This new model has a state-space (SS) representation and can be estimated by maximum likelihood via Kalman filter. Extension to incorporate subject random effect within SS modeling framework will be discussed.

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