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 Thank you for this post! I do enjoy thinking about this topic. There's a lot to respond to here, so perhaps I can do it in pieces.
1) The BOLD signal (captured by fMRI) is only "somewhat correlated with neural activity"
It is correlated with neural activity, albeit there is a low-pass filter on top. It is most correlated with LFP signals in monkeys (Logothetis et al, 2001, https://www.nature.com/articles/35084005 ) and humans (Nir et al, 2007, https://www.cell.com/fulltext/S0960-9822(07)01635-1 ).
The BOLD signal is fairly slow, but you can deconvolve it and get temporal precision on the order of 0.5s or so (also subject to your sampling time). The lower temporal precision and indirect aspect of fMRI compared to ephysn is not so different from GCaMP sensors (especially earlier ones), which have been much less controversial.
For studying cognitive neuroscience, 0.5s can actually be really powerful, as you can set up much more complex tasks in humans and jitter stimulus presentation.
2) Each voxel is too large to see anything interesting
If decades of fMRI research have taught us anything, it's that brain areas do get engaged in a grouped enough fashion in order to measure it. Reward signals, for instance, are particularly salient in fMRI. Just that fact has been enough to get researchers excited about connecting economic rewards to their neural instantiation.
One of my favorite studies showing the power of fMRI, despite these bigger voxels, is a clever analysis showing how visual and language representations are right up against each other in the cortex (Popham et al, 2021, https://www.nature.com/articles/s41593-021-00921-6 ). In what other modality could you get such a result??
Besides, MRI technology is getting better and better. You can already get better spatial resolution if you image a specific brain region. There's also efforts to increase the spatial resolution overall by redesigning scanner components and improving signal processing, leading voxels that can image individual columns:
https://vcresearch.berkeley.edu/news/134-million-build-next-gen-mri-brain-scanner-uc-berkeley
3) Brain areas are plastic, so does it make sense to build a science around regions that generalizes across people?
The most general form of this argument makes neuroscience feel hopeless. Yes, if you wire visual signals to the auditory cortex of a marmoset, it might indeed process these. However, does that mean we should not study the development and fine structure of the visual cortex for processing vision? Surely not!
Plus, it's true that brain regions can be reorganized across people, but to me it amazing at how much is preserved! For instance, Wager et al (2013, https://www.nejm.org/doi/full/10.1056/nejmoa1204471 ) found a signature of pain that could predict a participant's pain even when trained on other subjects. This is not to downplay individual variability, I think all of neuroscience (across all methods and levels) needs to more fully grasp with this. That said, there are common patterns and they are already quite interesting on their own!
@neurolili @albertcardona - Good points Lilli, but I do want to point out that the poor temporal resolution of old calcium indicators was one of the primary reasons why Janelia put so much effort into calcium indicators to get them to be fast, and also that even the slow ones at least have single-cell resolution. And speed is part of why GCaMP6 was so popular.
So I don't think that argument gets very far. (I didn't trust the slow calcium indicator signals all that much either.)
@ichoran @albertcardona Ah yes that's a good point! Yes, perhaps I was overzealous in comparing GCaMP to BOLD. It's more like doing widefield imaging with GCaMP6s.. Even in flies it could be interesting to do this, although there it would be more powerful complemented with genetics and anatomy. Sophie Aimon did some work along these lines, although this whole subfield is still early.