Kelsey Ichikawa has just published a superb essay about the pitfalls of functional magnetic resonance imaging (fMRI) of the brain. Ms. Ichikawa, who describes herself as a “budding” neuroscientist who graduated last year from Harvard, discusses the snares into which misinterpretation can lead us.
fMRI brain scanning is a relatively new technology in which researchers and clinicians use magnetic resonance images (MRI) of the brain to detect brain activity almost as it happens. The technique is widely used, both for clinical care of patients (neurosurgeons use it to map sensitive parts of the brain prior to surgery) and for research purposes. A major thrust of neuroscience research in the last couple of decades has been the use of fMRI to correlate brain activity with thinking and to draw conclusions about the physical basis of the mind.
A few points about fMRI imaging are important to note.
- fMRI imaging doesn’t see brain activity directly. fMRI imaging detects changes in regional blood flow in the brain, and we know from research over a century ago that activity in a part of the brain correlates more or less with changes in blood flow to that brain part. When neurons in a region of the brain become active, blood flow in that region increases.
- The changes in blood flow do not occur simultaneously with the brain activity. There is a lag of anywhere from a few seconds to upwards of a minute from the neuronal activity to the uptick in blood flow. The time resolution of fMRI imaging for brain activity is not particularly good.
- fMRI imaging produces rather fuzzy pictures of the brain — the spatial resolution of fMRI, compared with ordinary MRI, is rather poor, although it is improving.
Furthermore, fMRI requires a lot of signal-processing, which means that researchers must make choices about which data points are important and which are noise. Such decisions inherently introduce bias into the research. The researchers’ processing “smudges” the images, making interpretation considerably more difficult and unreliable.
Ichikawa gives an example of the imprecision and potential for bias in fMRI imaging:
The most common analysis procedure in fMRI experiments, null hypothesis tests, require that the researcher designate a statistical threshold. Picking statistical thresholds determines what counts as a significant voxel — which voxels end up colored cherry red or lemon yellow. Statistical thresholds make the difference between a meaningful result published in prestigious journals like Nature or Science, and a null result shoved into the proverbial file drawer.
This opens the door for data manipulation that, while not deliberately deceptive, can seriously skew results.
Read the rest at Mind Matters News, published by Discovery Institute’s Walter Bradley Center on Natural and Artificial Intelligence.