Multiresolution analysis in fMRI: sensitivity and specificity in the detection of brain activation

Multiresolution analysis of fMRI studies using wavelets is a new approach, previously reported to yield higher sensitivity in the detection of activation areas. No data are available, however, in the literature on the analytic approach and wavelet bases that produce optimum results. The present study was undertaken to assess the performance of different wavelet decomposition schemes by making use of a "gold standard," a realistic computer-simulated phantom. As activation areas are then known "a priori," accurate assessments of sensitivity, specificity, ROC curve area and spatial resolution can be obtained. This approach has allowed us to study the effect of different factors: the size of the activation area, activity level, signal-to-noise ratio (SNR), use of pre-smoothing, wavelet base function and order and resolution level depth. Activations were detected by performing t-tests in the wavelet domain and constructing the final image from those coefficients that passed the significance test at a given P-value threshold. In contrast to previously reported data, our simulation study shows that lower wavelet orders and resolution depths should be used to obtain optimum results (in terms of ROC curve area). The Gabor decomposition offers the maximum fidelity in preserving activation area shapes. No major differences were found between other wavelet bases functions. Data pre-smoothing increases ROC area for all but very small activation region sizes.