fMRI suffers from the disease of temporal uncertainty. The BOLD response is sluggish and unreliable; cognitive processes are variable and are difficult to model; and each slice of a volume is acquired at a different time. This last symptom is addressed by slice-timing correction (STC), which attempts to shift the data acquired at each slice in order to align them at the same time point. Without it, all would be lost.
"Madness!" you cry; "How can we know what happened at a time point that was not directly measured? How can we know anything? Is what I perceive the same as what everybody else perceives?" A valid criticism, but one that has already been hunted down and crushed by temporal interpolation - the estimation of a timepoint by looking at its neighbors. "But how reliable is it? Will the timecourse not be smoothed by simply averaging the neighboring points?" Then use a higher-order interpolation, whelp, and be silent.
The merits of STC have been debated, as well as when it should be used in the preprocessing stream. However, it is generally agreed that STC should be included in order to reduce estimation bias and increase sensitivity (Sladky et al, 2011; Calhoun et al, 2000; Hensen et al, 1999), and that it should occur before volume coregistration or any other spatial interpolations of the data. For example, consider a dataset acquired at an angle from the AC/PC line (cf. Deichmann et al, 2004): If STC is performed after realigning the slices to be parallel to the AC/PC line, then the corresponding slices for each part of the brain are altered and temporal interpolation becomes meaningless; that way lies darkness and suffering.
If unnecessary interpolations offend your sensibilities, other options are available, such as incorporating temporal derivatives into your model or constructing regressors for each slice (Hensen et al, 1999). However, standard STC appears to be the most straightforward approach and the lowest-maintenance relative to the other options.
Slice-Timing Correction in AFNI is done through 3dTshift. Supply it with the following:
Sample command:
More details, along with an interactive example of how STC works, can be found in the following tutorial video.
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| Figure stolen from Sladky et al (2011): Assuming that the BOLD response is roughly equivalent across slices, each successive slice samples a different timepoint. STC rectifies this by interpolating what the value at time zero would have been if all slices had been acquired simultaneously. |
"Madness!" you cry; "How can we know what happened at a time point that was not directly measured? How can we know anything? Is what I perceive the same as what everybody else perceives?" A valid criticism, but one that has already been hunted down and crushed by temporal interpolation - the estimation of a timepoint by looking at its neighbors. "But how reliable is it? Will the timecourse not be smoothed by simply averaging the neighboring points?" Then use a higher-order interpolation, whelp, and be silent.
The merits of STC have been debated, as well as when it should be used in the preprocessing stream. However, it is generally agreed that STC should be included in order to reduce estimation bias and increase sensitivity (Sladky et al, 2011; Calhoun et al, 2000; Hensen et al, 1999), and that it should occur before volume coregistration or any other spatial interpolations of the data. For example, consider a dataset acquired at an angle from the AC/PC line (cf. Deichmann et al, 2004): If STC is performed after realigning the slices to be parallel to the AC/PC line, then the corresponding slices for each part of the brain are altered and temporal interpolation becomes meaningless; that way lies darkness and suffering.
If unnecessary interpolations offend your sensibilities, other options are available, such as incorporating temporal derivatives into your model or constructing regressors for each slice (Hensen et al, 1999). However, standard STC appears to be the most straightforward approach and the lowest-maintenance relative to the other options.
Slice-Timing Correction in AFNI is done through 3dTshift. Supply it with the following:
- The slice you wish to align to (usually either the first, middle, or last slice);
- The sequence in which the slices are acquired (ascending, descending, sequential, interleaved, etc.);
- Preferred interpolation (the higher-order, the better, with Fourier being the Cadillac of interpolation methods); and
- Prefix for your output dataset.
Sample command:
3dTshift -tzero 0 -tpattern altplus -quintic -prefix tshift [[input dataset goes here]]
More details, along with an interactive example of how STC works, can be found in the following tutorial video.