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Neurofeedback is a technique that has captured the public’s imagination in recent years. The idea is simple: you learn to control your brain activity and that, in theory, leads to long-term behavioural changes. Again, in theory, the potential is limitless. The technique could be used for pretty much everything, from the more noble goal of treating mental disorders using a method that has minimal to no side effects, to the potentially questionable goal of “boosting brain positivity” using a method that has a high potential for relieving customers of their hard-earned cash.

We’ve previously written extensively on the topic of EEG-based neurofeedback (EEGnf) and if you haven’t done so already, I highly recommend that you also check out that article. But today we’re turning our attention to a less commercial, yet equally interesting type of neurofeedback: the one based on fMRI. The main idea is the same: the machine measures some metric of brain activity, which is then showed to the participant on a screen. Their task is then to focus and modify this metric until it reaches a predefined target. But there are some considerations here.

fMRI – the donut-shaped elephant in the room

You’ve probably already heard about this poster-child method of neuroscience, namely functional magnetic resonance imaging or, in short, fMRI. fMRI uses large magnets to measure something called blood-oxygen level dependent (BOLD) signal. In brief, hemoglobin, the molecule that lugs all that oxygen and carbon dioxide around for you, has different magnetic properties depending on what it carries. And because neurons need a lot of oxygen when we think, there is usually a large influx of oxygenated hemoglobin that we can use to track what different brain regions are up to at certain moments.

Because of the underlying working principles, fMRI has an advantage over EEG: it provides much better spatial resolution, on the order of millimeters. From a neurofeedback perspective, this is quite advantageous, as in theory, it allows for control of finer-grained brain regions.

Of course, fMRI comes with some disadvantages as well. Because changes in blood oxygenation are somewhat slow compared to neural activity itself (we’re talking seconds vs. milliseconds), participants need to be made aware not to expect instantaneous changes. What’s more, given that the magnets are incredibly large and the whole machine needs to be kept in special conditions and requires trained personnel to maintain and operate, it’s quite pricey and you will never be able to fit one into your living room.

Does it work?

Like EEGnf, fMRI-based neurofeedback studies suffer from eerily familiar limitations, including study design variability and lack of a sufficient amount of randomized control trials (for a great explanation of those, check this out). But despite its limitations, this method shows potential and has also enjoyed some success. However, how this is quantified depends on which perspective we adopt.

The research perspective

Neurofeedback is considered a perturbative approach: basically, you use it to change something, then you see what happens. From a research perspective, this can be used to investigate quite a few aspects related to cognition. One example is the localization of various functions to specific brain areas. If a certain brain region is responsible for a certain cognitive function, decreasing activity in that area should impair performance, whereas increasing it should have the opposite effect.

This is a pretty cool idea, but in recent years, there has been a paradigm shift in how we think about the brain. More specifically, the dominant view has switched from considering the brain to be a quirky quilt made up of independent and loosely interconnected regions to treating it as a complex distributed network in which regions influence each other in nontrivial ways. Whereas direct cause-effect relationships are reasonable to expect in the first case, they are not so obvious in the second one. Due to nonlinear network effects, changing activity in one region might have unexpected effects on other regions. And it is specifically this case where fMRI neurofeedback could be particularly useful.

In a perspective paper published in 2017, Prof. Dani S. Bassett (an authority in network control), outlines how that might work. Building on mathematical and theoretical physics ideas, the paper argues that fMRI neurofeedback could offer a useful tool for empirically testing hypotheses related to network dynamics: for example, whether up-regulation of a specific region is capable of pushing the brain network into a new state and whether that has any quantifiable effect on behaviour.

The clinical perspective

While the research perspective might seem somewhat abstract, clinically, things are more straightforward, both in terms of methods, as well as goals. Basically, we want to change brain activity in regions associated with specific brain disorders, hoping that restoring brain function will also restore behaviour.

fMRI neurofeedback has enjoyed some success in this respect, with studies reporting improvements in a wide array of conditions, from anxiety and depression to addiction, chronic pain, or tinnitus. Unfortunately, all of these studies suffer from the same caveat: they included very few participants. So they need to be replicated in larger sample sizes before we conclude that this is a viable method which should become routine in clinical practice.

Summing up

fMRI-based neurofeedback is a potentially viable tool both from a research and a clinical perspective. The main advantage over EEGnf is its high spatial resolution, which allows for much finer-grained control. Just as EEGnf, however, this method still needs to be researched more extensively. And whereas its high costs and technical demands slow down this process, they also protect the general public from being exposed to a half-baked product.

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Further reading

Bassett, D. S., & Khambhati, A. N. (2017). A network engineering perspective on probing and perturbing cognition with neurofeedback. Annals of the New York Academy of Sciences, 1396(1), 126-143.

Sulzer, J., Haller, S., Scharnowski, F., Weiskopf, N., Birbaumer, N., Blefari, M. L., … & Sitaram, R. (2013). Real-time fMRI neurofeedback: progress and challenges. Neuroimage, 76, 386-399.

Tursic, A., Eck, J., Lührs, M., Linden, D. E., & Goebel, R. (2020). A systematic review of fMRI neurofeedback reporting and effects in clinical populations. NeuroImage: Clinical, 28, 102496.