1. Region of Interests (ROI) Extraction and Analysis in Indexing and Retrieval of Dynamic Brain Images Researcher: Xiaosong Yuan, Advisors: Paul B. Kantor and Deborah Silver Sponsored by National Science Foundation (EIA-0205178) 1. Introduction: In functional magnetic resonance imaging (fMRI) time-series analysis, Region of Interests (ROI) needs to be extracted to trace the paths of activation that are relevant to various activities of human brains. The fMRI 4-Dimensional datasets in this study comprise a group of experiments with finger-tapping in certain defined patterns. We present a fast and effective method to extract the regions which are related to the variance of blood flows in brains. The method is based on the correlation function between experimental stimuli and observation signals with noise. The statistical significance of the response to the stimulus pattern can be obtained together with its time lags. 2. Finger tapping patterns: Bimanual simultaneous opposition of thumb and four fingers at a "fast but comfortable rate" (approx. 3-6 Hz)). paradigm: 16 blocks, each 24s, tapping, rest, tapping, rest, …. scanning parameters: 132 acquisitions, TR=3s, TE=30ms, 32 axial slices. Fig. 1. Mean volume over time of the original brain dataset (32 slices) 3. Cross-correlation of stimuli and observation signals Cross correlation is a standard method of estimating the degree to which two series are correlated. Consider two series x(i) (observation over time of one voxel) and y(i) (stimuli), where i=0,1,2...N-1. The cross correlation at time lag l is defined as If the above is computed for all lags l=0,1,2,...15 (the series y(i) is periodic), among which the largest correlation value for each voxel can be found at certain time lag. The correlation coefficient is computed as Where and are the standard deviation of x and y. Next we compute the statistical test about The test statistic of correlation significance value is Where is the degree of freedom, we use time-steps N-2 here. The resulting figures are shown on the right. 4. How to generate the brain mask volume The mask (1/0 binary map) is used to classify all voxels into two groups - inside or outside of the brain. So that in the future processes we can tell whether a voxel is a brain voxel or a background voxel, such as drawing the histogram of the brain volumes, etc. The intensities inside of the brain are almost always higher than the background, therefore we can threshold for it. This will generate a binary image (mask0). The mask0 has both holes inside and islands outside the brain. Therefore we want to take away these holes and islands to make a solid sphere mask. The mask process consists of two steps: Step 1. Get rid of the holes from mask0 first (1) Pick up a seed at (0, 0, 0), flood-fill the outside space with certain value k. (2) transform every voxel with k into 0; every voxel not with k into 1. (3) Thus we get the mask1 only with a solid sphere and the islands. Step 2. Get rid of the islands from mask1 (1) Pick up a seed at the center point of the brain, flood-fill the inside space with certain value k. (2) transform every voxel with k into 1; every voxel not with k into 0. (3) Thus we get the final mask only with a solid sphere. Fig. 2. The intensity of the observation signal over time. The six voxels shown are with high correlation values Fig. 3. The distribution of the time lag in baseline (right) and finger-tapping (left) Fig. 4. The distribution of the time lag of the voxels in the brain with high confidence levels Fig. 5. The Cross-correlation Rxy(l) at maximumFig. 6. The test statistic t value histogram (left) and volume map (right) from the correlation Rxy(l) Fig. 7. The Mask0 and final Mask generated 5. Conclusion We present a method for identifying brain regions that show a lagged correlation with the presumed stimulus. This seems to be a promising alternative to the standard Statistical Parametric Mapping method. In particular, this approach gives each identified brain region a “time lag” parameter, which may make it possible to track an activation through the brain over time. The results above show that the method does not produce artifacts when there is no interesting task, the histogram of correlations shows no interesting features. March 11, 2003 in APLab, SCILS