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Statistical contrasts at the single-subject level were computed as weighted sums of the estimated beta coefficients divided by an estimate of the standard error, yielding a t-statistic for each voxel in the image volume.
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In each subject, the model was applied to the time series at each voxel in the brain image, yielding a parameter estimate for each experimental condition.
In each subject, the model was applied to the time series at each voxel in the brain image, yielding a parameter estimate per voxel for each experimental condition.
Software designed by Gallant then created a receptive-field model for each voxel in fMRI images of the visual area while two subjects viewed each of the images.
For each voxel, the image time-series was high pass filtered to 1/128 Hz.
For candidate generation, "voxel" indicates that each voxel from the image was treated as one lesion candidate.
The FA for area was determined by calculating the mean FA for every voxel in the anisotropy image, which matched a voxel identified as white matter on the MRI as we have done with FDG images [ 23– 25].
For each voxel c1.N in the fixed local clique, we extracted the voxel intensity from each image, yielding an N-dimensional pattern vector for each image.
For conjunction analyses, we utilized an in-house tool called mincmath to find the minimum t-statistic at each voxel across the images for all singing tasks.
Subsequently the FA was computed for each separate voxel of each image in the fDTI set.
For each subject, all voxels in the PiB-PET image were divided by the median PiB uptake of the cerebellum to form uptake ratio images.
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