The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising schemes are extensions of WaveLab routines, using the symmetric orthogonal cubic spline wavelet basis. In a first study, activity in a time series is simulated by superimposing a timedependent signal on a selected region. We add noise with a known signal-to-noise ratio (SNR) and spatial correlation. After denoising, the statistical analysis, performed with SPM, is evaluated. We compare the shapes of activations detected after applying the wavelet-based methods with the shapes of activations detected after Gaussian smoothing. In a second study, the denoising schemes are applied to a real functional MRI time series, where signal and noise cannot be separated. The denoised time series are analysed with SPM, while false discovery rate (FDR) control is used to correct for multiple testing. Wavelet-based denoising, combined with FDR control, yields reliable activation maps. While Gaussian smoothing and wavelet-based methods producing smooth images work well with very low SNRs, less smoothing wavelet-based methods produce better results for time series of moderate quality.