Controversial take? I consider myself a neuroscientist, and I am not able to understand the usefulness of fMRI for cognitive neuroscience studies. (fRMI seems like a great tool to diagnose brain cancer, though.)
In fMRI, every voxel represents several cubic millimetres of brain tissue comprising millions of neurons; the temporal sampling is 2 seconds, when neurons fire action potentials in the ~10 millisecond range, and fast behavioural responses are in the ~300 millisecond range; and the signal measured is blood flow which is somewhat correlated with neural activity at those timescales.
fRMI studies in patients with chronically implanted electrodes (to detect the location of epileptic centres) seem to indicate that areas with low fRMI signal aren't necessarily "unimportant", on the contrary, a small percent of neurons in that area may be critical, yet their activity isn't captured in the fMRI signal as significant. Studies from Ueli Rutishauser and collaborators come to mind.
Then there's the issue of brain "areas". The study of the brain as made of compartments breaks down at close scrutiny. First, monitoring neural activity of the visual cortex in the absence of visual stimulus showed that neuron activity tracks body motion (Carsen Stringer et al. 2019 https://www.science.org/doi/abs/10.1126/science.aav7893 ); in other words multi-sensory integration is the norm. Second, high-functioning hydrocephalic cases present a greatly altered brain architecture with the grey and white matter occupying a tiny fraction of the overall volume. Third, accidents have revealed great plasticity in brain areas, with areas not being spatially stable but rather able to expand over adjacent areas that are less used because of e.g., a missing body part. Even complete absence of the entire cerebellum (cerebellar agenesis) can result in mild phenotypes (Yu et al. 2014 https://www.doi.org/10.1093/brain/awu239 ).
In other words, brain "areas" is not quite the useful abstraction we would want it to be. And therefore, fRMI imaging of blood flow changes over time across coarsely spatially and temporally sampled brains is, at best, too much of a low pass filter over the signal we'd be interested in monitoring.
Are fMRI studies a case of "there's more light here and therefore I look for my wallet here rather than overthere in the shadows where I can't see at all"? I understand that fRMI, and EEG, are all we have to study neural activity in the human brain, so there's a strong incentive to just go with that despite strong shortcomings. Am I missing something fundamental about fRMI?
The only studies using fMRI that make sense to me are longitudinal studies, where the same patient is imaged multiple times and comparisons are like to like, and have more to do with discovering structural issues related to e.g., ageing than assigning function to any subset of the brain, such as in Linda Geerligs' work (Geerligs et al. 2015 https://academic.oup.com/cercor/article-abstract/25/7/1987/462366 ). Are there any other kinds of fMRI studies that beyond doubt have contributed to our understanding of the human brain?
@albertcardona I pretty much agree with you, but I think it depends also on what you're looking for.
To use a programming/computer hardware analogy, if you want to figure out an algorithm, fMRI is useless. If you want to know whether your computation is hitting RAM hard or requires a lot of floating point operations or is engaging the graphics card a lot (and whether shaders are used), it's pretty handy.
Rather than fMRI being a case of looking for the wallet where the light is, it's more like it's showing you where to put the light to look for wallets (if it's done well).
However, the number of overinterpreted fMRI studies does rather disappoint me. The inference "we didn't see a fMRI signal in this area so it's not important in this task" seems to happen far too often.
Then again, this is one of the biggest mistakes of scientists in every area: assuming that absence of evidence is evidence of absence when the tools and/or statistics were not specifically being deployed to address that exact question (i.e. putting bounds on how absent something is).
As a rough heuristic, I find it depressingly accurate to assume that a sentence starting with "This isn't significant, so" is one that will end with an unjustified conclusion.
@albertcardona I guess I could go through a representative sample of recent fMRI papers and check. Knowing whether a process is RAM-intensive or uses shaders does give you new understanding of the function of your computational device, even if it's not an algorithm; there I think the track record is not bad (especially if you ignore the discussion section). But this is just a general impression; to get at anything like "what percentage" we'd have to actually pick out papers and score them, I think.
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