A dynamic system model-based technique for functional MRI data analysis

• Published in: NeuroImage (Orlando, Fla.) Vol. 22; no. 1; pp. 179 - 187
• Main Authors: Kamba, Masayuki, Sung, Yul-Wan, Ogawa, Seiji
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2004; Elsevier Limited
• Subjects: Adult; Algorithms; Brain; Brain activation; Brain research; Computer Simulation; Dynamic system; Echo-Planar Imaging; Female; fMRI; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - statistics & numerical data; Male; Models, Neurological; NMR; Nuclear magnetic resonance; Photic Stimulation; Japan; fMRI; Brain activation; Dynamic system
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2003.12.037

Signals in functional magnetic resonance imaging (fMRI) are influenced by physiological fluctuations in addition to local brain activity. We have proposed a dynamic system model-based technique for separation of signal changes related to brain activation inputs from those related to physiological fluctuations. We applied this technique to a visual fMRI experiment to determine the validity and feasibility of this technique for fMRI data analyses. Gradient-echo echo planar images were obtained from 12 healthy volunteers with a Siemens ALLEGRA operating at 3 T, with a repetition time of 500 ms, echo time of 20 ms, field of view of 200–210 mm, matrix size of 64 × 64, and slice thickness of 5 mm. Twelve runs with two stimulation periods of varied duration (2–8 s) with 8-Hz flickering illumination were obtained for each subject. Local signal changes were modeled by an autoregressive model with two exogenous inputs, a visual stimulation input and a global reference signal. Local signal changes were appropriately predicted not only for stimulation periods but also resting periods. A significant linear relationship was found between model static gain based on the dynamic system modeling and beta coefficient based on a general linear model (GLM) analysis for active voxels in the primary visual cortex (analysis of covariance [ANCOVA], P < 0.001; estimated parameter, 0.967; 95% confidence interval, 0.734–1.201). This dynamic system model-based technique is sufficiently accurate and feasible for use in extracting signal changes related to brain activation inputs from measured signals with physiological fluctuations.