title: Separating processes within a trial in event-related functional MRI II. Analysis.
abstract: Many cognitive processes occur on time scales that can significantly affect the shape of the blood oxygenation level-dependent (BOLD) response in event-related functional MRI. This shape can be estimated from event related designs, even if these processes occur in a fixed temporal sequence (J. M. Ollinger, G. L. Shulman, and M. Corbetta. 2001. NeuroImage 13: 210-217). Several important considerations come into play when interpreting these time courses. First, in single subjects, correlations among neighboring time points give the noise a smooth appearance that can be confused with changes in the BOLD response. Second, the variance and degree of correlation among estimated time courses are strongly influenced by the timing of the experimental design. Simulations show that optimal results are obtained if the intertrial intervals are as short as possible, if they follow an exponential distribution with at least three distinct values, and if 40% of the trials are partial trials. These results are not particularly sensitive to the fraction of partial trials, so accurate estimation of time courses can be obtained with lower percentages of partial trials (20-25%). Third, statistical maps can be formed from F statistics computed with the extra sum of square principle or by t statistics computed from the cross-correlation of the time courses with a model for the hemodynamic response. The latter method relies on an accurate model for the hemodynamic response. The most robust model among those tested was a single gamma function. Finally, the power spectrum of the measured BOLD signal in rapid event-related paradigms is similar to that of the noise. Nevertheless, high-pass filtering is desirable if the appropriate model for the hemodynamic response is used.
authors.affiliation: Department of Radiology, Washington University, St. Louis, Missouri, 63110, USA. jmo@npg.wustl.edu
authors.forename: J M
authors.initials: JM
authors.lastname: Ollinger
authors.forename: M
authors.initials: M
authors.lastname: Corbetta
authors.forename: G L
authors.initials: GL
authors.lastname: Shulman
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009010:Monte Carlo Method; D010775:Photic Stimulation; D012815:Signal Processing, Computer-Assisted