A paper demonstrating a new technique for "ultrafast fMRI" has been getting some buzz on the blogosphere. Although movies often depict fMRI showing real-time activity in the brain, in fact typical methods only collect from one slide of the brain at a time, taking a fair amount of time to cover the entire brain (Neuroskeptic puts this at about 2-3 seconds). This new technique (GIN) can complete the job in 50 ms, and without sacrificing spatial resolution (which is the great advantage of fMRI relative to other neuroimaging techniques like EEG or MEG).
Does this mean fMRI is about to get 50 times faster?
Not exactly. What fMRI is measuring is the change in blood oxygenation in areas of your brain. When a particular area starts working harder, more oxygen-rich blood is sent in its direction, and that can be detected using MRI. The limitation is that it takes a while for this blood to actually get there (around 5-10 seconds). One commenter on the Neuroskeptic post (which is where I heard about this article) wrote "making fMRI 50 times faster is like using an atomic clock to time the cooking of a chicken."
The basic fact is that fMRI is never going to compete with EEG or MEG in terms of temporal resolution, because the latter directly measure the electrical activity in the brain and can do so on very fine time-scales. But that doesn't mean that speeding up fMRI data acquisition isn't a good idea. As the authors of the paper write:
But that's not going to stop me from speculating as to how faster data-acquisition might improve fMRI. (Any readers who know more about fMRI should feel free to step in for corrections/additions).
Speculations
The basic problem is that what you want to do is model the hemodynamic response (the change in blood oxygenation levels) due to a given trial. This response unfolds over a time-course of 5-10 seconds. If you are only measuring what is happening every couple seconds, you have pretty sparse data from which to reconstruct that response. Here's an example of some reconstructed responses (notice they seem to be sampling once every second or so):
Much faster data-collection would help with this reconstruction, leading to more accurate results (and conclusions). The paper also mentions that their technique helps with motion-correction. One of the basic problems in fMRI is that if somebody moves their head/brain even just a few millimeters, everything gets thrown off. It's very hard to sit in a scanner for an hour or two without moving even a smidge (one technique, used by some hard-core researchers, is a bite bar, which is perfectly fitted to your jaw and keeps you completely stabilized). Various statistical techniques can be used to try to mitigate any movement that happens, but they only work so well. The authors of the paper write:
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Boyacioğlu, R., & Barth, M. (2012). Generalized iNverse imaging (GIN): Ultrafast fMRI with physiological noise correction Magnetic Resonance in Medicine DOI: 10.1002/mrm.24528Does this mean fMRI is about to get 50 times faster?
Not exactly. What fMRI is measuring is the change in blood oxygenation in areas of your brain. When a particular area starts working harder, more oxygen-rich blood is sent in its direction, and that can be detected using MRI. The limitation is that it takes a while for this blood to actually get there (around 5-10 seconds). One commenter on the Neuroskeptic post (which is where I heard about this article) wrote "making fMRI 50 times faster is like using an atomic clock to time the cooking of a chicken."
The basic fact is that fMRI is never going to compete with EEG or MEG in terms of temporal resolution, because the latter directly measure the electrical activity in the brain and can do so on very fine time-scales. But that doesn't mean that speeding up fMRI data acquisition isn't a good idea. As the authors of the paper write:
fMRI studies, especially related to causality and connectivity, would benefit from reduced repetition time in terms of better statistics and physiological noise characteristics...They don't really say *how* these studies would achieve this benefit. The rest of the discussion is mostly about how their technique improves on other attempts at ultra-fast fMRI, which tend to have poor spatial resolution. They do mention that maybe ultra-fast fMRI would help simultaneous EEG-fMRI studies to strengthen the link between the EEG signal and the fMRI signal, but it's obvious to me just how helpful this would be, given the very different timing of EEG and fMRI.
But that's not going to stop me from speculating as to how faster data-acquisition might improve fMRI. (Any readers who know more about fMRI should feel free to step in for corrections/additions).
Speculations
The basic problem is that what you want to do is model the hemodynamic response (the change in blood oxygenation levels) due to a given trial. This response unfolds over a time-course of 5-10 seconds. If you are only measuring what is happening every couple seconds, you have pretty sparse data from which to reconstruct that response. Here's an example of some reconstructed responses (notice they seem to be sampling once every second or so):
Much faster data-collection would help with this reconstruction, leading to more accurate results (and conclusions). The paper also mentions that their technique helps with motion-correction. One of the basic problems in fMRI is that if somebody moves their head/brain even just a few millimeters, everything gets thrown off. It's very hard to sit in a scanner for an hour or two without moving even a smidge (one technique, used by some hard-core researchers, is a bite bar, which is perfectly fitted to your jaw and keeps you completely stabilized). Various statistical techniques can be used to try to mitigate any movement that happens, but they only work so well. The authors of the paper write:
Obviously, all InI-based and comparable imaging methods are sensitive to motion especially at the edges of the brain with possible incorrect estimation of prior information. However, due to the large amount of data, scan times are currently short (4 min in teh current study), which mitigates the motion problem.I take this to mean that because their ultra-rapid scanning technique collects so much data from each trial, you don't need as many trials, so the entire experiment can be shortened. Note that they are focused on the comparison between their technique and other related techniques, not the comparison between their technique and standard fMRI techniques. But it does seem reasonable that more densely sampling the hemodynamic response for an individual trial should mean you need fewer trials overall, thus shortening experiments.
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