By Christoph Stippich
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Extra info for Clinical Functional MRI: Presurgical Functional Neuroimaging
The design matrix on the left contains only one predictor, the constant term. The estimated beta weight (b0) scales the constant term to the mean signal level. The design matrix in the middle adds a predictor for “green” main condition. The estimated beta weights (b0, b1) scale the predictors and the weighted sum explains more variance than the ﬁ rst model, but residual variance is still high. The third model (right) adds a predictor for “red” main condition. The estimated beta weights (b0, b1, b2) scale the predictors and the weighted sum now explains all taskrelated signal ﬂuctuations.
Another correction method incorporating the observation that neighboring voxels often activate in clusters is based on Monte Carlo simulations calculating the likelihood to obtain clusters of different sizes (Forman et al. 1995). 05 is met. This approach does not require spatial smoothing and appears highly appropriate for fMRI data. The only disadvantage is that the method is quite computer intensive.
Another temporal preprocessing step consists in temporal smoothing of voxel time courses removing high-frequency signal ﬂuctuations, which are considered as noise. While this step increases the signal-to-noise ratio, temporal smoothing is not recommended when analyzing event-related designs since it may distort estimates of temporally relevant parameters, such as the onset or width of average event-related responses. Fig. 13. Principle of temporal ﬁ ltering using Fourier analysis. The time-domain signal can be converted in an equivalent frequency-domain signal using the Fourier transform (upper row).
Clinical Functional MRI: Presurgical Functional Neuroimaging by Christoph Stippich