HBM functional imaging analysis contest data analysis in wavelet space.
An analysis of the Functional Imaging Analysis Contest (FIAC) data is presented using spatial wavelet processing. This technique allows the image to be filtered adaptively according to the data itself, rather than relying on a predetermined filter. This adaptive filtering leads to better estimation of the parameters and contrasts in terms of mean squared error. It will be shown that by introducing a slight bias into the estimation, a large reduction in the variance can be achieved, leading to better overall mean squared error estimates. As no single filter needs to be preselected, results containing many scales of information can be found. In the FIAC data, it is shown that both small-scale and large-scale (smoother, more dispersed) effects occur. The combination of small- and large-scale effects detected in the FIAC data would be easy to miss using conventional single filter analysis.
Aston
John A D
JA
Institute of Statistical Science, Academia Sinica, Taiwan. jaston@stat.sinica.edu.tw
Turkheimer
Federico E
FE
Brett
Matthew
M
D000465:Algorithms; D016477:Artifacts; D015982:Bias; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000639:trends; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000639:trends; D009415:Nerve Net / Q000033:anatomy & histology / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014705:Verbal Behavior / Q000502:physiology