Slice Timing FAQ

Frequently Asked Questions - Slice Timing Correction

1. What is slice timing correction? What's the point?

In multi-shot EPI (or spiral methods, which mimic them on this front), slices of your functional images are acquired throughout your TR. You therefore sample the BOLD signal at different layers of the brain at different time points. But you'd really like to have the signal for the whole brain from the same time point. If a given region that spans two slices, for example, all activates at once, you want to see what the signal looks like from the whole region at once; without correcting for slice timing, you might think the part of the region that was sampled later was more active than the part sampled earlier, when in fact you just sampled from the later one at a point closer to the peak of its HRF.

What slice-timing correction does is, for each voxel, examine the timecourse and shift it by a small amount, interpolating between the points you ACTUALLY sampled to give you back the timecourse you WOULD have gotten had you sampled every voxel at exactly the same time. That way you can make the assumption, in your modeling, that every point in a given functional image is the actual signal from the same point in time.

2. How does it work?

The standard algorithm for slice timing correction uses sinc interpolation between time points, which is accomplished by a Fourier transform of the signal at each voxel. The Fourier transform renders any signal as the sum of some collection of scaled and phase-shifted sine waves; once you have the signal in that form, you can simply shift all the sines on a given slice of the brain forward or backward by the appropriate amount to get the appropriate interpolation. There are a couple pitfalls to this technique, mainly around the beginning and end of your run, highlighted in Calhoun et. al below, but these have largely been accounted for in the currently available modules for slice timing correction in the major programs.

3. Are there different methods or alternatives and how well do they work?

One alternative to doing slice-timing correction, detailed below in Henson et. al, is simply to model your data with an HRF that accounts for significant variability in when your HRFs onset - i.e., including regressors in your model that convolve your data with both a canonical HRF and with its first temporal derivative, which is accomplished with the 'hrf + temporal derivative' option in SPM. In terms of detecting sheer activation, this seems to be effective, despite the loss of some degrees of freedom in your model; however, your efficiency in estimating your HRF is very significantly reduced by this method, so if you're interested in early vs. late comparisons or timecourse data, this method isn't particularly useful.

Another option might be to include slice-specific regressors in your model, but I don't know of any program that currently implements this option, or any papers than report on it...

4. When should you use it?

Slice timing correction is primarily important in event-related experiments, and especially if you're interested in doing any kind of timecourse analysis, or any type of 'early-onset vs. late-onset' comparison. In event-related experiments, however, it's very important; Henson et. al show that aligning your model's timescale to the top or bottom slice can results in completely missing large clusters on the slice opposite to the reference slice without doing slice timing correction. This problem is magnified if you're doing interleaved EPI; any sequence that places adjacent slices at distant temporal points will be especially affected by this issue. Any event-related experiment should probably use it.

5. When is it a bad idea?

It's never that bad an idea, but because the most your signal could be distorted is by one TR, this type of correction isn't as important in block designs. Blocks last for many TRs and figuring out what's happening at any given single TR is not generally a priority, and although the interpolation errors introduced by slice timing correction are generally small, if they're not needed, there's not necessarily a point to introducing them. But if you're interested in doing any sort of timecourse analysis (or if you're using interleaved EPI), it's probably worthwhile.

6. How do you know if it’s working?

Henson et. al and both Van de Moortele papers below have images of slice-time-corrected vs. un-slice-time-corrected data, and they demonstrate signatures you might look for in your data. Main characteristics might be the absence of significant differences between adjacent slices. I hope to post some pictures here in the next couple weeks of the SPM sample data, analyzed with and without slice timing correction, to explore in a more obvious way.

7. At what point in the processing stream should you use it?

This is the great open question about slice timing, and it's not super-answerable. Both SPM and AFNI recommend you do it before doing realignment/motion correction, but it's not entirely clear why. The issue is this:

  • If you do slice timing correction before realignment, you might look down your non-realigned timecourse for a given voxel on the border of gray matter and CSF, say, and see one TR where the head moved and the voxel sampled from CSF instead of gray. This would results in an interpolation error for that voxel, as it would attempt to interpolate part of that big giant signal into the previous voxel.
  • On the other hand, if you do realignment before slice timing correction, you might shift a voxel or a set of voxels onto a different slice, and then you'd apply the wrong amount of slice timing correction to them when you corrected - you'd be shifting the signal as if it had come from slice 20, say, when it actually came from slice 19, and shouldn't be shifted as much.

    There's no way to avoid all the error (short of doing a four-dimensional realignment process combining spatial and temporal correction - possibly coming soon), but I believe the current thinking is that doing slice timing first minimizes your possible error. The set of voxels subject to such an interpolation error is small, and the interpolation into another TR will also be small and will only affect a few TRs in the timecourse. By contrast, if one realigns first, many voxels in a slice could be affected at once, and their whole timecourses will be affected. I think that's why it makes sense to do slice timing first. That said, here's some articles from the SPM e-mail list that comment helpfully on this subject both ways, and there are even more if you do a search for "slice timing AND before" in the archives of the list.

  • Thread from Rik Henson
  • Argument from Geoff Aguirre
  • Response to Aguirre from Ashburner

8. How should you choose your reference slice?

You can choose to temporally align your slices to any slice you've taken, but keep in mind that the further away from the reference slice a given slice is, the more it's being interpolated. Any interpolation generates some small error, so the further away the slice, the more error there will be. For this reason, many people recommend using the middle slice of the brain as a reference, minimizing the possible distance away from the reference for any slice in the brain. If you have a structure you're interested in a priori, though - hippocampus, say - it may be wise to choose a slice close to that structure, to minimize what small interpolation errors may crop up.

9. Is there some systematic bias for slices far away from your reference slice, because they're always being sampled at a different point in their HRF than your reference slice is?

That's basically the issue of interpolation error - the further away from your reference slice you are, the more error you're going to have in your interpolation - because your look at the "right" timepoint is a lot blurrier. If you never sample the slice at the top of the head at the peak of the HRF, the interpolation can't be perfect there if you're interpolating to a time when the HRF should be peaking - but hopefully you have enough information about your HRF in your experiment to get a good estimation from other voxels. It's another argument for choosing the middle slice in your image - you want to get as much brain as possible in an area of low interpolation error (close to the reference slice).

10. How can you be sure you're not introducing more noise with interpolation errors than you're taking out with the correction?

Pretty good question. I don't know enough about signal processing and interpolation to say exactly how big the interpolation errors are, but the empirical studies below seem to show a significant benefit in detection by doing correction without adding much noise or many false positive voxels. Anyone have any other comments about this?


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