Guest Post by Nick Wan, Utah State University
Imagine driving in a simulator while undergoing an fMRI. No, you won’t be lying down — this is not your typical large, chamber-like scanner. An instrument called functional near-infrared spectroscopy, or fNIRS, is using a smaller, more portable design to record brain activity in more real-world settings.
“It’s really that in-between,” said Daniel Belyusar from MIT’s AGELAB, when I sat down with him at the Society for Neuroscience (SfN) meeting last month in Washington, D.C. fNIRS is a non-invasive tool that has the ability to take measurements of oxygen concentration levels from the brain, similar to how functional magnetic resonance imaging (fMRI) is able to measure oxygen concentration levels.
But unlike fMRIs, fNIRS is portable and has temporal resolution more similar to electroencephalography (EEG): Whereas fMRI may record one sample per 2 seconds, fNIRS can record 10 samples per 1 second. So, the fNIRS seems to be the best of both worlds — better spatial resolution than EEG and better temporal resolution than fMRI. The in-between.
Belyusar presented preliminary data involving fNIRS work at SfN, including measuring oxygenation concentration while people are driving a car and performing cognitive tasks at the same time. Drivers performed a numerical n-back task while trying to steer their vehicle during a driving simulation. Behaviorally, as the task difficulty increased, the driving performance decreased. The fNIRS data showed an overshoot in oxygen level as the task difficulty also changed.
This overshoot in oxygen is common in cognitive processes. When a task begins, the brain consumes oxygen. As task demands increase, an overshoot of oxygen occurs in order to compensate for the consumed oxygen supply. fMRI only measures how much oxygen is consumed, not how much oxygen is available in the area, which makes fNIRS uniquely positioned for understanding the oxygen overshoot phenomenon.
One possibility is that this overshoot of oxygen eventually “levels out;” as you continue to perform the task, the prefrontal cortex may not need as much oxygen as it is initially overshooting. Think of preparing a holiday dinner for your friends: you may overshoot the amount of food necessary the first year, but the following year, you better know how much everyone eats, so you cook less food.
fNIRS is in its infancy in terms of different ways to analyze data. Most fNIRS methods use the exact same software fMRI analysts use, but Belyusar said he thinks that time-series analysis, a data analysis technique used in EEG, is the likely next step. This makes sense, as fNIRS operates on EEG-like timescales.
fMRI lacks the ability to see precisely when an area of the brain is consuming oxygen. With fNIRS, you can more accurately see when the consumption of oxygen is occurring, as well as when more oxygen enters an area. Whereas fMRI would say “activation is occurring during this task,” fNIRS can say “there is greater activity during this time of the task compared to this other time,” simply due to the greater samples per second.
Still, the temporal quality of fNIRS is not as good as EEG. fNIRS takes 10 samples per second, which is trumped by EEG’s 500 to 1000 samples per second. And the spatial resolution is not as good as fMRI. For example, fMRI can image subcortical brain regions, while fNIRS cannot analyze past the cortex, unable to capture any subcortical activation. Indeed, many researchers who presented their fNIRS at SfN are using the instrument as a supplement to their EEG or fMRI data.
So what’s the advantage of fNIRS? “We’re able to do more naturalistic things with fNIRS than with fMRI,” Belyusar said. An fMRI wouldn’t fit into a realistic car-driving simulator. Current fNIRS devices are about the size of one of the airplane drink caddies — standing about 4 feet tall with a computer device on top, and with a width and depth of about 3 feet. Some, like the Hitachi ETG-4000, were designed to be “portable,” which is indicative by the wheels attached below the unit.
fNIRS also is much less sensitive to “artifacts” that riddle both fMRI and EEG. Moving during fMRI recordings distort the fMRI data, generally rendering that particular scan unreliable and thus useless. In EEG, a similar problem occurs from motion, where movement can induce activation that seems real but is actually just generated from the movement of the recording device.
fNIRS is a bit more robust due to the use of near-infrared light rather than magnetic fields or electrical activity. While small movements, like talking, could destroy datasets for fMRI and EEG, they are welcome in fNIRS designs that target more naturalistic speech and interaction.
For researchers like Belyusar, fNIRS is not just a supplement to other data collected from fMRI or EEG — it is the primary tool to answer questions that EEG and fMRI cannot, such as what is happening more precisely in the brain while driving with distractions. And with new analysis techniques on the horizon and an emphasis on experiments that were once too sensitive for EEG or fMRI, the future of fNIRS research is one we should all be excited about.
Nick Wan is a graduate student at Utah State University. He regularly writes about neuroscience and academia at True Brain. Follow him on Twitter: @nickwan
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