Abstract #2846

Voss H, Tabelow K, Polzehl J, Spokoiny V, Heier L, Dyke J

We applied a structurally adaptive smoothing method, Adaptive Weights Smoothing (AWS), to presurgical fMRI data of 26 epilepsy/tumor patients. AWS adapts to different shapes of activation areas by generating a spatial structure corresponding to similarities and differences between time series in neighbored locations. In numerical simulations, AWS has been shown to be superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and less false detections. We found that AWS and Gaussian smoothing yield occasionally significantly different results, and we present examples showing different aspects of the superiority of AWS in presurgical planning.