Preprocessing

Before the essential part of data analysis can begin, a number of preliminary steps are typically taken. Some spatial smoothing and temporal smoothing may be applied, but the first and most important step is the assessment of subject head movement during the imaging session.

The problem of subject motion is a pervasive one in fMRI and arises not only in constraining experimental design but also in the analysis of images of a brain that may have moved over the course of an experiment. The high-speed imaging techniques used in fMRI typically minimize the effects of movement in any one image. However, many images are collected in each run, which are typically 2-8 min long, and there are many runs in a typical 2-h session, representing more than 1000 brain volumes per session. Because the fMRI-based signal modulation is intrinsically small (typically 0.5-5%), data from all the images collected in these long runs are normally needed to gain statistical power. Thus, it is important that the images within a run and across runs are properly aligned. Head motion makes this process a challenge. In addition, even if the skull were perfectly immobile, the brain still moves. The pulsatile flow of arterial blood causes movement virtually everywhere in the brain, particularly in subcortical structures.

For all these reasons, the data analytic approach to motion detection and motion correction has been based on the brain images rather than on monitoring head movement externally. Efforts are made to minimize subject head movement, and it is not currently possible to correct for severe or rapid movement. All the current algorithms for correcting head movement assume rigid motion of the head. Although a single slice of brain imaging data is collected very rapidly compared to most head movement, the time needed to collect an entire brain volume—consisting of 20 or more slices—is much longer than many head movements. Such motion cannot be corrected with these algorithms. However, if the movement is not too great in amplitude and not too rapid, there are algorithms available in most fMRI data analysis packages that are adequate to detect the motion and to transform the data in an attempt to compensate for the effects of that motion.

A key feature of these algorithms is that they automatically reveal many kinds of movement, including stimulus-correlated movement. If a subject moves every time he or she is supposed to start a task, the movement could create MR signal artifacts that appear as a false activation signal. There is no good way to correct for such data, and these date must be detected and discarded.

Subject movement is generally regarded as the major problem for getting consistent data in fMRI-based experiments. Experienced, well-motivated subjects

Raw Anatomical Images

Raw Functional Scans

3D Anatomical Volume

Anatomical Volume in Talairach Space

3D Overlaid Activation Map

Raw Anatomical Images

3D Anatomical Volume

Anatomical Volume in Talairach Space

Statistical Tests

Raw Functional Scans

Assembled Functional Images

3D Functional Data

Data Preprocessing (motion correction temporal and spatial smoothing)

Figure 3 Highlights of an fMRI data processing stream. Data from fMRI-based experiments are analyzed in many steps. The number and order of these steps are still a topic of controversy, with variations across laboratories and software packages. This is a simplified representation of the generic steps. Both high-resolution structural MRI images, and lower resolution functional images pass through a variety of preprocessing steps. Early steps may transform the raw anatomical images from individual (two-dimensional) slices of the brain into volumetric (multislice, three-dimensional) arrays that are more suitable for the detection of head movement. At the same time, data are often transformed to a standard three-dimensional orientation and overallsize scale (Talairach coordinates). In addition to the essential step of detecting the presence of head motion in the data, several other (arguably optional) steps may be applied. Motion correction algorithms can help if head movement is not too much (although these algorithms are sometimes unnecessary or counterproductive if motion is little). There are both theoretical and practical reasons for smoothing the data in the spatial and/or temporal domains, although some investigators argue against performing these steps. Finally, statistical tests are performed contrasting the data collected during different experimental conditions. The resulting statistical maps are typically thresholded and overlaid on anatomical images, as indicated in Fig. 4. Further considerations are attendant to the comparisons across the brains of different subjects, as discussed in the text.

Assembled Functional Images

- 2D-3D Functional-Anatomical Alignment - Talairach transformation

Statistical Tests

3D Functional Data

Data Preprocessing (motion correction temporal and spatial smoothing)

Figure 3 Highlights of an fMRI data processing stream. Data from fMRI-based experiments are analyzed in many steps. The number and order of these steps are still a topic of controversy, with variations across laboratories and software packages. This is a simplified representation of the generic steps. Both high-resolution structural MRI images, and lower resolution functional images pass through a variety of preprocessing steps. Early steps may transform the raw anatomical images from individual (two-dimensional) slices of the brain into volumetric (multislice, three-dimensional) arrays that are more suitable for the detection of head movement. At the same time, data are often transformed to a standard three-dimensional orientation and overallsize scale (Talairach coordinates). In addition to the essential step of detecting the presence of head motion in the data, several other (arguably optional) steps may be applied. Motion correction algorithms can help if head movement is not too much (although these algorithms are sometimes unnecessary or counterproductive if motion is little). There are both theoretical and practical reasons for smoothing the data in the spatial and/or temporal domains, although some investigators argue against performing these steps. Finally, statistical tests are performed contrasting the data collected during different experimental conditions. The resulting statistical maps are typically thresholded and overlaid on anatomical images, as indicated in Fig. 4. Further considerations are attendant to the comparisons across the brains of different subjects, as discussed in the text.

who use bite bars in the scanner can routinely be expected to yield data free of serious motion artifact. In contrast, in studies with clinical patients or other difficult subjects, as much as 20-30% of data may need to be discarded because of subject motion. There are methods for helping to minimize physical motion during data acquisition and to detect and possibly compensate for it after data acquistion, but none are fully satisfactory.

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