Basic Detection of Change

The first goal of any analysis of fMRI-based data is to determine whether the experimental manipulation has resulted in a measurable change in the MR signal and to specify where in the brain and when (in time) that change has occurred. In principle, any statistical method that can be applied to a time series can be used with fMRI data. In practice, the demands of the experimental paradigm, limitations of the tool, and the capabilities of distributed software packages constrain the sorts of analyses that are typically performed. A few broad classes of common data analysis options are detailed here, although the presentation is not comprehensive. With the exception of principle component analysis (PCA) and other multivariate techniques, each of these tests is applied at the voxel level. When these statistics are computed for each voxel in the brain, and the resulting collection of statistics is presented in the form of an image in which color or intensity is used to represent the value of that statistic, the result is called a statistical map of brain activation.

High-speed imaging (e.g., EPI) is used to collect many images of the brain during each of the experimental conditions designed. The simplest (and, historically, the first) comparison was obtained by subtracting the average of all the images collected during one condition from the average of all the images collected during another condition. The resulting difference image clearly showed areas that were brighter, indicating greater MR signal during one condition than another.

statistical map color look-up table transformation threshold thresholded activation map rJ

Gray Scale anatomical map

Color Scale (shown here in black and white)

final overlaid activation map

Figure 4 Production of a color-coded activation map. After preprocessing, comparisons of the values of the functional MR images collected during different experimental conditions are used to generate a statistical map. The underlying question is whether the collection of values from one condition is likely to have been generated by statistically different levels of brain activity, when compared with the collection of values obtained during a second condition. These statistics are computed on a voxel-by-voxel basis, resulting in a spatial map (left, top) in which grayscale intensity indicates magnitude of the statistic. The midgray area represents little difference between the two experimental conditions in question; bright regions occur where the first condition elicited much stronger MR signals than the second condition; and darker regions occur where the first condition elicited much weaker MR signals than the second condition. Because of the need to collect many functional images in a short period of time, functional images typically have substantially less spatial resolution than structural (anatomical) images. (These differences are a consequence of the different pulse sequences that are used to collect functional versus structural MRI data.) In order to combine information from the statistical map with the higher resolution structural images, two transformations are applied to the statistical map. First, the gray-scale intensities used to represent the statistics are mapped into a color scale. Second, a threshold is applied so that statistical values that are within a user-defined range close to zero are mapped to transparency. This permits the combining of the two maps (thresholded pseudocolor map of statistics and high-resolution gray-scale map of anatomy) by overlaying the color map on top of the structural map. In this way, it is possible to get a better sense of the location of the changes in neural activity associated with the different experimental conditions.

This sort of comparison is the only one that, strictly speaking, is "subtraction," although the term is sometimes used informally during discussions of contrasts between conditions, even if those contrasts are based on some other statistic. Instead of subtracting (on a voxel by voxel basis) the averaged data from different conditions, the more general approach is to compute a statistic based on the collection of values at each specific spatial voxel, collected across the times of the many images. Such generic statistical computations on grouped images are more accurately described as "contrasts" or "comparisons" rather than subtractions.

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