Figure 2.

Spatial activation patterns (whose sizes range from 1 to 9 pixels) were generated in a 32 × 32 pixel region (top left). Realistic pixel time series for the activation patterns were obtained from motor activation data that were thresholded at P = 10^‐6 to obtain time series that are almost sure to be active with respect to the contrast “motor activation‐fixation”. To achieve a specific CNR, null data using wavelet resampled resting‐state data with intact correlation in time and space were added to all active and nonactive pixels of the simulated data in the 32 × 32 region. Spatial activation patterns are shown for CNR = 0.75 for different methods with P < 0.01 (uncorrected for multiple comparisons). Note the blurring of patterns for single voxel with Gaussian smoothing (FWHM = 2.24 pixels), strong bleeding and block artifacts for unconstrained CCA, block artifacts for cCCA with the simple constraint, but very small artifacts and accurate representation of most patterns by cCCA with the sum constraint, and good accuracy but some bleeding artifacts of cCCA with the maximum constraint.