Noah Brier | December 23, 2021

The Brain Edition

On connections, human behavior, and fMRI

Noah here. When I was a kid, I told my mom that my brain worked like a file manager with folders and subfolders to organize things. This conception, as I would later learn, was very incorrect. However, it did fall into a long tradition of people mistakenly using whatever was the technology of the moment as an analogy for how their brain functions. 

Why is this interesting?

Misunderstanding how the brain works and applying imperfect metaphors seems to be a basic part of humanity. “Our shoddy thinking about the brain has deep historical roots,” an excellent Aeon piece from a few years ago explained, “but the invention of computers in the 1940s got us especially confused. For more than half a century now, psychologists, linguists, neuroscientists and other experts on human behaviour have been asserting that the human brain works like a computer.” 

All of this has led me to great skepticism for most things neuroscience. Whenever someone trots out the results of the latest brain study to try to explain some basic human behavior, I can’t help but roll my eyes. Most of these use functional magnetic resonance imaging (fMRI) to measure blood flow in the brain. The media loves to cover new fMRI results, and the business world is hungry for the consumer brain to be decoded. “A number of recent studies suggest that neural data recorded from relatively small groups of people (<30) can not only predict market-level behavior,” a 2015 Harvard Business Review article explained, “but can predict it better than traditional marketing tools. Data from fMRI scans has been shown to outperform behavioral data in predicting market-level music sales, charity donations, and even the relative persuasiveness of anti-smoking ad campaigns.” 

While there’s potential in fMRI, we’re still reasonably far from finding it. If it were to appear on the hype cycle, it would probably be on the downward slope after the peak of inflated expectations. Unfortunately, studies are often tiny because of the high costs of running the machine and interpreting the results and the data can be tough to interpret. A 2009 study of a salmon, for instance, showed the “fish’s brain exhibited increased activity for emotional images.” The only problem? The fish was dead. “Even in a dead salmon’s brain,” explains Kelsey Ichikawa in a new Nautilus piece, “the MRI scanner detected enough noise that some voxels exhibited statistically significant correlations. By failing to correct for multiple comparisons, Bennett and his colleagues ‘discovered’ illusory brain activity.”

How could this happen? Here’s Ichikawa again:

For a sense of scale, the brain contains an estimated 100 billion neurons, and a single voxel in the human cortex can cover over 500,000 neurons. Those neurons may be doing any number of things—exciting each other, inhibiting each other, or firing in different patterns within sub-populations—but all that fMRI can detect is the net change in oxygenated blood over that whole voxel space every 2 seconds. This is like trying to determine the average opinion on foreign policy from 500,000 different people arguing, agreeing, and debating simultaneously.

In the end, we will surely find value from fMRI (and have already, however narrowly). Still, it also feels like another case of “we shape our tools and thereafter our tools shape us” in as much as the pictures that emerge from the machines are now becoming a popular model for brain function. 

To some extent, the concept of certain regions “lighting up” is a function of the images the machines spit out. “The eye-catching blobs and connectivity maps exist because of the particular way in which neuroscientists, magnetic resonance physicists, and data scientists decided to visualize and represent data from the brain,” Ichikawa concludes. The perfect picture, in other words, continues to elude us. (NRB)

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Thanks for reading,

Noah (NRB) & Colin (CJN)

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