Statistical Analysis Of Image Features

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Three different statistical methods have been developed, based on the Fourier transform, the wavelet transform, and the theory of Gaussian random fields in the spatial domain. All three methods have been applied to the analysis of PET and MR images from normal and alcoholic subjects and have identified significant differences in generally the same brain regions. Current research on these topics includes the development of a 1-D Gaussian random field method to analyze fMRI time series data. This methodology can be used to analyze fMRI data acquired from experiments designed to incorporate a long (that is long enough, as determined experimentally, to estimate the variance associated with the acquired data) baseline condition and transition to another activated state such as one produced by drug or alcohol administration. It uses the long baseline data to estimate the variance measure associated with the temporal data from a voxel within the image and sets a statistically rigorous threshold for activation in spite of the known temporal correlation in the data. This analysis technique is being validated with simulated and experimental data. Furthermore, this analysis technique is being incorporated into numerous experiments including one designed to look at the blood flow changes in the brain associated with alcoholic intake in normal subjects. This presents an ideal demonstration of this analysis technique to basically establish a response curve for alcohol intake. Finally, statistical analysis in the temporal domain based on traditional time series analysis in the Fourier domain have been developed and given similar results in terms of localization of the signal in fMRI blood flow studies to other less rigorous and generalizable techniques. This analysis methodology has the potential to (1) localize fMRI activation changes, (2) estimate or reconstruct the activated signal without the associated noise, (3) estimate the hemodynamic response function locally without prior assumptions as to its structure, and (4) detect multiple responses to multiple input stimuli. Currently this technique is being used to study experimental designs including slides of visual stimuli designed to elicit different emotions or alcohol craving. Furthermore this technique has been extended to incorporate a full complex general linear model (required in the Fourier domain for statistical testing) with up to five inputs stimuli (separately or in combinations). Further development will allow statistical comparison of multiple voxels allowing analysis for example of experiments under different conditions (drug/no-drug) within the context of this general linear model.