In Defense of Brain Imaging
Brain imaging has fared pretty well in its three decades of existence, all in all. A quick search of the PubMed database for one of the most popular methods, functional magnetic resonance imaging (fMRI), yields some 22,000 studies. In 2010 the federal government promised $40 million for the Human Connectome Project, which aims to map all of the human brain’s connections. And brain imaging will no doubt play a big part in the president’s new, $4.5 billion BRAIN Initiative. If you bring up brain scanning at a summer BBQ party, your neighbors may think you’re weird, but they’ll be somewhat familiar with what you’re talking about. (Not so for, say, calcium imaging of zebrafish neurons…)
And yet, like any youngster, neuroimaging has suffered its share of embarrassing moments. In 2008, researchers from MIT reported that many high-profile imaging studies used statistical methods resulting in ‘voodoo correlations’: artificially inflated links between emotions or personality traits and specific patterns of brain activity. The next year, a Dartmouth team put a dead salmon in a scanner, showed it a bunch of photos of people, and then asked the salmon to determine what emotion the people in the photos were feeling. Thanks to random noise in the data, a small region in the fish’s brain appeared to “activate” when it was “thinking” about others’ emotions. Books like Brainwashed, A Skeptic’s Guide to the Mind, Neuro: The New Brain Sciences and the Management of the Mind, and the upcoming The Myth of Mirror Neurons have all added fuel to the skeptical fire.
There are many valid concerns about brain imaging — I’ve called them out, on occasion. But a new commentary in the Hastings Center Report has me wondering if the criticism itself has gone a bit overboard. In the piece, titled “Brain Images, Babies, and Bathwater: Critiquing Critiques of Functional Neuroimaging,” neuroscientist Martha Farah makes two compelling counterpoints. One is that brain imaging methods have improved a great deal since the technology’s inception. The second is that its drawbacks — statistical pitfalls, inappropriate interpretations, and the like — are not much different from those of other scientific fields.
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