fMRI is the hottest technology in neuroimaging. Here’s an ELI5.
What you need to know about the method that’s revolutionizing our understanding of the brain
In 2011, a group of neuroscientists at Berkeley scanned people with fMRI while they watched movie trailers. Using just signals from the brain, the researchers configured a set of machine learning algorithms to reconstruct estimates of the trailers that people had seen. Are the clips perfect? No. But the sci-fi-level potential is there.
From medicine to research, fMRI is providing us scientists an unprecedented level of detail in deciphering the complex code by which the brain operates. In this article, I’ll explain the basics of what fMRI is and how it works (in case you’re wondering if we can turn your dreams into movies…well, not yet, but maybe soon.)
MRI (Magnetic Resonance Imaging) is used in both research and medical practice to take 3D “images” of the inside of the body. These images can reveal the structure of organs, bones, ligaments, nerves, and other types of tissue, making them powerful diagnostic tools.
But traditional MRI has its limitations: an MR image is static — like a photograph, it captures its subject (the body) at a fixed moment in time. This isn’t a problem in most cases: if a radiologist wants to diagnose a tumor in a patient’s knee, for example, one image is all she needs to locate it. Other physiological functions, however, inherently unfold over time: the heart’s beat, the lungs’ inhale. Mapping these types of processes requires 4D images — that is, a sort of MR movie rather than one static image.
The brain is one such dynamic organ. At any moment in time, millions of neurons fire in complex patterns that — when taken together — not only control our organs and limbs, but also produce our conscious, sensory experience of the world.
To try and comprehend this process, researchers at Mass General Hospital in the early 1990s developed what is now the gold-standard technique in neuroscience research: fMRI. At the time, everyone understood that traditional MRI — often called “structural MRI” — is useful in physically locating tissue in the brain; however, it provides little help in mapping its real-time processes that fluctuate over time. In any case, neural firing — carried out via electrical signaling — doesn’t show up in brain scans.
The pioneers of fMRI figured out that they could rapidly take a series of MR images of the brain and track changes in blood flow over time. Crucial to this technique is that brain regions use more blood when they are more active. Therefore, we can use the dynamics of blood flow to estimate neural firing. This MR-movie method came to be known functional MRI because it helps decipher the function, or role, of particular brain regions while people are engaged in various activities.
A classic fMRI experiment, for example, is to compare a person’s brain activity while they tap their finger to their brain activity while they’re at rest. Researchers collect a 4D “movie” of MR images while subjects undergo these two activities. Then, they subtract the resting brain activity — which mostly involves “background” processes like respiration and organ control — from the finger-tapping brain activity. On average, the difference in brain activity between the two tasks yields only the brain activity involved in finger-tapping (since all of the background processes subtract out.) This particular experiment would reveal that the motor cortex is highly active during finger-tapping but not during rest. Generally speaking, these types of task-based fMRI paradigms have allowed researchers to gain a much better understanding of the function of different brain regions than ever before.
If you’re wondering, flashier applications of this technology are definitely possible in the future. Acquiring a large amount of fMRI data from participants allows researchers to understand the meaning of complex patterns of activity. Imagine that we observe a pattern of brain activity that only occurs while a participant watches tennis. If we then scan this person with fMRI while they’re sleeping and observe the pattern, we could hypothesize that they were dreaming about tennis. As time goes on, our understanding of even finer grain patterns may improve to the point where we can convincingly reconstruct dreams from brain scans. But if you’re anything like me, it might be best if we kept those private…
Notes:
fMRI is just one of many techniques being used in neuroscience. While it’s popular in human neuroimaging at the moment, other methods like EEG and TMS are also widely used. Animal models afford even better methods since researchers can record neurons more directly; however, there is an obvious gap in cognition between, e.g., rodents and humans.
Finally, fMRI has many limitations, too many of which to list here. We are still at a relatively early stage in our use and application of this technology.
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