Frequently Asked Questions - fMRI Scanning
ScanningPapers has some nice handouts from Gary Glover and Philippe, summarizing some of these articles and (very informally and clearly) addressing questions about TR length, signal-to-noise tradeoffs, what k-space is exactly, etc. Definitely check those out.
- Spiral: Properly weighted and combined, spiral in-out shows significantly less signal drop-out and shows significantly greater activations in many areas of the brain, including ventromedial PFC, medial temporal lobe, etc. Effect is even more pronounced at higher field strengths (see Preston et. al on ScanningPapers). However, it is less widely supported, and Gary's trademark spiral i-o sequence may not even be physically possible on some other institutions' equipment.
2. What should your TR be? What are the tradeoffs, and what's the best tradeoff of coverage vs. speed for different types of analysis?
Bottom line: TR should be as short as possible, given how many slices you want to cover and the limits of your task. Gary's handout and monograph on ScanningPapers speak best to this issue, and are good quick reads. Decreasing your TR decreases your signal-to-noise ratio (SNR) in any one functional image, but because you have more images to work with, your overall SNR increases with decreasing SNR. Your TR, however, is limited by how many slices you want to take. On the 3T scanner here at Stanford, using spiral in-out, each slice takes approximately 65 msec/slice (TE = 30 msec), so you can get 15 full slices in 1 second (on the 1.5T, slices take about 75 msec, so you can get 13 full slices in 1 sec.). Your tradeoff is that with fewer slices, you have to either accept less coverage of the brain, or thicker slices, which will have poorer resolution in the z direction.
For certain experiments, then - ones focused on primary sensory on motor cortices, when you don't care about the rest of the brain - you can buy yourself shorter TRs by decreasing your number of slices, or you can increase your number of slices and make them smaller, while keeping your TR constant. Assuming you need full coverage of the brain, you can only decrease your TR by making your slices thicker, which you should do as much as possible within the constraints of your desired resolution.
3. What should your slice thickness be?
4. What should your slice resolution / voxel size be?
64 x 64 is standard around here for full-brain coverage. With experiments focusing on smaller areas - primary motor and/or sensory cortices - something else (like 128 x 128) may be useful to get better resolution in a smaller area.
5. Should you acquire axially/coronally/something else? How come?
6. BOLD vs. perfusion: what are pros and cons of each? What sorts of experiments would you use perfusion for?
Perfusion imaging - in which arterial blood is magnetically 'labeled' with an RF pulse, and then tracked as it moves through the brain - has two main advantages we discussed, one of which is thoroughly discussed in the Aguirre article in ScanningPapers. That advantage is the relatively different noise profile present within the perfusion signal. Unlike BOLD, perfusion noise doesn't have very much autocorrelation, which isn't by itself anything special, but means that perfusion contains much, much less noise relative to BOLD at very low experimental frequencies. There is more noise in general, though, in perfusion imaging, so in general SNR ratios are better for BOLD. But for experiments with very low task-switching frequency - say, blocks of 60s or more, even up to many minutes or hours - BOLD is almost useless, due to the preponderance of low-frequency noise, whereas the perfusion signal is unchanged. This means that with block lengths of longer than a minute, perfusion imaging is probably a better way to go, and experiment which previously weren't possible - block lengths of several minutes, or task switching taking place over several days - might be designed with perfusion.
Another feature of the noise in perfusion imaging is that it appears to be more reliable across subjects. While SNR within a given subject is higher for BOLD, group SNR appears (with limited data in Aguirre on ScanningPapers) to be higher in perfusion imaging. More research is needed on this subject, but this relative SNR advantage may be useful for experiments with small numbers of subjects, as across-subject variability is always the largest noise source for BOLD experiments, often by a huge factor.