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.
Kamba
Masayuki
M
Ogawa Laboratories for Brain Function Research, Hamano Life Science Research Foundation, 12 Daikyo-cho, Shinjuku, Tokyo 160-0015, Japan. kamba@hlsrf.or.jp
Sung
Yul-Wan
YW
Ogawa
Seiji
S
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D008959:Models, Neurological; D010775:Photic Stimulation