We present results on representative slices having peak voxel of activation, whereas Montreal Neurological Institute (MNI) position of this most active voxel is listed in Table 2.
Furthermore, since 40P depends mainly on inclusion of the most active voxel in the region, and the calculation of the remaining volume is done automatically, it will not differ significantly between operators.
The MNI position of the most active voxel on the reconstructed data using the proposed Optshrink LR + S method (with smoothing) is same as that obtained with the original data.
This POI was then extended to the scalp position that was directly above the most active voxel in the V5 (green POI in Figure 5 ).
For each of the 12 response vectors β, amplitudes were transformed such that the nth percentile voxel was assigned a value n (i.e., the least active voxel equaled 0, the most active voxel equaled 100, the median voxel equaled 50, and so forth).
A control analysis that used data only from the most active voxels in each subject still found a reliable correlation between expertise and performance (r = −0.50, p < 0.02; 40 most active voxels).
We selected either the peak voxel for this contrast or the upper twentieth percentile of the most active voxels (yielding, on average, n = 4.5, 5, and 4.0 voxels/quadrant for V1, V2, and V3, respectively); results were qualitatively the same (and statistically significant) for either selection criterion, so for the results presented here, the latter criterion is used.
The following same procedure was used to define the three ROIs in each individual: The most significantly active voxel(s), or peak, was first identified based on a particular contrast, statistical thresholds were then set to a determined minimum (t = 3, p<0.005), and the activity up to 3375 mm around the peak was selected.
The reproducible components exhibited much less trial-to-trial variability than the raw data from even the most activated voxel.
For each subject and each ROI, the highest possible peak threshold was used (0.05 Bonferroni corrected, or 0.001, 0.005, 0.002, 0.01, and 0.05, uncorrected) to extract the first eigenvector from the most significantly active voxels.
But voxels are much larger than neurons, and, in the long run, the best way to understand the brain is probably not by asking which particular voxels are most active in a given process.
