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

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 x 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.