40 Percent of Fmri Signals Do Not Correspond to Actual Brain Activity
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A provocative study claiming that 40% of fMRI signals don't correspond to actual brain activity has sparked a lively debate, with some commenters jumping to the conclusion that the results are bogus. However, others clarified that the real takeaway is the importance of proper statistical corrections when analyzing fMRI scans to prevent spurious "activations." The discussion was punctuated by references to the infamous "dead salmon" paper, which demonstrated the dangers of flawed statistical analysis. As it turns out, the original salmon paper authors were on hand to confirm that their point was to highlight the pitfalls of poor statistical methods, not to dismiss fMRI entirely.
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Arguing with a dead fish may be a sign you're working too hard :)
There is no statistical analysis that can save you if your interpretation of a signal is wrong (for example, you can't get information about personality from phrenology, regardless of what statistical analysis you try to apply to the data). That's not to say that we need to just trust this study implicitly - I'm just trying to describe how serious of a problem to the field their claim is.
So if I show you a picture of a cat, and you like cats, then a bit of your brain might start using more oxygen because you're thinking about cute furry things, and if I show you a picture of a car, and you like cars, a different bit of your brain lights up showing more oxygen use because you're thinking about fast shiny things.
But really we've only got the barest idea of what bits of the brain do what, and maybe it's a bit of brain that goes "hey I'm happy" that lights up in both cases because you like both cats and cars.
We can kind of see bits we think are associated with muscle movement coming to life if I show you a picture of a bike, and you like cycling, and if I show you a really cool mountain track you imagine belting down it flat out. That lights up differently if I show you something else.
However, we do not really know except in very broad terms what bits of the brain actually do what. We can't "see thoughts", we just know that some bits of brain seem to use more oxygen than others, and from that we guess "this bit of brain is for thinking about sitting in a nice cafe with a cup of coffee and a newspaper" versus "this bit of brain is for being frightened of lions".
At least when phrenology was a thing, the ceramic heads with lines painted on were inexpensive and didn't require three-phase power and huge barrels of liquid helium.
(I mention this so more people can know the list exists, and hopefully email us more nominations when they see an unusually great and interesting comment.)
Reviewing the HN docs, https://news.ycombinator.com/bestcomments?h=168 might also be a good summary link.
Structural MRI is even more abused, where people find "differences" between 2 groups with ridiculously small sample sizes.
Herting, M. M., Gautam, P., Chen, Z., Mezher, A., & Vetter, N. C. (2018). Test-retest reliability of longitudinal task-based fMRI: Implications for developmental studies. Developmental Cognitive Neuroscience, 33, 17–26. https://doi.org/10.1016/j.dcn.2017.07.001
It's not at all clear to me that teenagers' brains OR behaviours should be stable across years, especially when it involves decision-making or emotions. Their Figure 3 shows that sensory experiments are a lot more consistent, which seems reasonable.
The technical challenges (registration, motion, etc) like things that will improve and there are some practical suggestions as well (counterbalancing items, etc).
I agree it makes a lot of sense though the sensory experiments are more consistent, somatosensory and sensorimotor localization results generally seem to the be most consistent fMRI findings. I am not sure registration or motion correction is really going to help much here, I suspect the reality is just that the BOLD response is a lot less longitudinally stable than we thought (brain is changing more often and more quickly than we expected).
Or if we do get better at this, it will be more sophisticated "correction" methods (e.g. deep-learners that can predict typical longitudinal BOLD changes, and those better allow such changes to be "subtracted out", or something like that). But I am skeptical about progress here given the amount of data needed to develop any kind of corrective improvements in cases where there are such low longitudinal reliabilities.
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> Using ICCs, recent efforts have examined test-retest reliability of task-based fMRI BOLD signal in adults. Bennett and Miller performed a meta-analysis of 13 fMRI studies between 2001 and 2009 that reported ICCs. ICC values ranged from 0.16 to 0.88, with the average reliability being 0.50 across all studies. Others have also suggested a minimal acceptable threshold of task-based fMRI ICC values of 0.4–0.5 to be considered reliable [...] Moreover, Bennett and Miller, as well as a more recent review, highlight that reliability can change on a study-by-study basis depending on several methodical considerations.
These can be measured themselves separately (that's exactly what they did here!) and if there's a spatial component, which the figures sort of suggest, you can also look at what a particular spot tends to do. It may also be interesting/important to understand why different parts of the brain seem to use different strategies to meet that demand.
They are indeed coupled, but the coupling is complicated and may be situationally dependent.
Honestly, it's hard to imagine many aggregate measurements that aren't. For example, suppose you learn that the average worker's pay increased. Is it because a) the economy is booming or b) the economy crashed and lower-paid workers have all been laid off (and are no longer counted).
Hasty post. I apologize.
Ekstrom, A. (2010). How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation. Brain Research Reviews, 62(2), 233–244. https://doi.org/10.1016/j.brainresrev.2009.12.004, https://scholar.google.ca/scholar?cluster=642045057386053841...
There have been some high profile influencer doctors pushing brain imaging scans as diagnostic tools for years. Dr. Amen is one of the worst offenders with his clinics that charge thousands of dollars for SPECT scans (not the same as the fMRI in this paper but with similar interpretation issues) on patients. Insurance won’t cover them because there’s no scientific basis for using them in diagnosing or treating ADHD or chronic pain, but his clinics will push them on patients. Seeing an image of their brain with some colors overlayed and having someone confidently read it like tea leaves is highly convincing to people who want answers. Dr. Amen has made the rounds on Dr. Phil and other outlets, as well as amassing millions of followers on social media.
[0] https://www.youtube.com/watch?v=J-SHgZ1XPXs
So many will watch this video and come away siding with Dr. Amen, feeling like they're doing the right thing to disregard the mean man on the other side who is questioning everything.
The alternative medicine and pseudoscience communities thrive on "but what if it works" or "they're just trying to help" attitudes, which snake oil sellers capitalize on.
I actually thought the interviewer was a little disingenuous. He said things like "We're on the same team" and "I'm not trying to trap you", then proceeded to lob his guest with criticisms from the other team and questions aimed to maneuver him into a contradiction. There's nothing inherently wrong with that, but if you're going to do it, be forthright you're engaging in a debate.
Earlier in the interview he could have put his cards on the table and plainly stated "Myself and others in the medical community are skeptical of the efficacy of imaging on outcomes, and a rigorous, double-blind study would lend dramatic support for us to adopt what you're touting."
Then they could have had the conversation he was clearly after, focused on that issue.
Instead it felt like I was watching for ages as he took a winding route to get there, then the interview cut off abruptly when they finally really did.
The overlays applied in editing while helpful and fair in some cases, at other times came across as one-sided. It's a shame we can't see a follow-up where the interviewee has an opportunity to respond (or squirm) in light of them.
For the record I would very much love to see additional research and gold-standard, double-blind studies. In the meantime I'll treat this as "Hey, we've got this interesting thing we can measure, we're seeing some good results in our practice" without over-emphasizing the confidence in this one diagnostic.
I did find the bit interesting about how having a gauge you can viscerally see impacted patients' engagement in care. Both agreed on the potential usefulness of that aspect, and conceded the difference in profiles between patients coming to Dr. Amen vs. ordinary front-line family physicians.
From a Medium article[2]:
"The issue was that he attended a party on a boat and someone took a picture of him and over a dozen other people in close proximity without masks. After getting caught, he made an apology video on his second channel, which only has 58k subscribers compared to his main channel’s 6 million. Not only was this apology riddled with flawed logic and self-justification, but now the opposition to COVID guidelines now has more ammunition."
[1] Image from November 2020: https://miro.medium.com/v2/resize:fit:1400/format:webp/0*Lr_...
[2] https://therewiredsoul.medium.com/the-real-problem-with-the-...
And also... https://en.wikipedia.org/wiki/Ad_hominem
I never said "Doctor Mike" is a bad doctor. I have no idea if he is a good or bad doctor.
Further, an ad hominem is when a person attacks someone's character without any base.
I wrote specifically about him not being at the forefront and questioning his values, as displayed by his actions during the pandemic. His actions were literally not in line with Covid guidelines. Those are guidelines that were formulated by hundreds (thousands?) of doctors, all of whom sought to be at the forefront of medical science during a pandemic.
As another user said, MRI scans not corresponding to brain activity is not really news, and in at least the part of the US I live in, MRI scans are not so easily recommended, especially since they're not covered by health insurance.
Dr. Amen should be called out, of course, but it doesn't mean a doctor is at the forefront for doing so.
Presumably because it is very analogous. You are essentially saying Dr. Mike shouldn’t be trusted because he made a bad decision. That is extremely similar to saying you shouldn’t trust a doctor’s advice because they happen to smoke.
> Further, an ad hominem is when a person attacks someone's character without any base.
No. An ad hominem is when the person is attacked rather than the argument. A terrible person can still make a perfectly sound argument. Calling them terrible doesn’t change the argument, even if it is emotionally satisfying.
> I wrote specifically about him not being at the forefront and questioning his values, as displayed by his actions during the pandemic.
You’re attacking his actions and not his recommendations. Ad hominem.
it is not ad-hominem to try to understand a person's motivations for expressing a particular opinion, which is why the above poster referred to 'character' which is not specific to the definition of ad-hominem, but is in the spirit thereof, that is, distracting from the argument. but if the person has shown themselves to be working contradictorily to public health policy, especially in consideration of the hippocratic oath, you may ask reasonably what they are about.
Missing the forest for the trees.
The point isn’t that neglecting to mask is exactly the same as smoking. Obviously these are different. The point is that in both cases the person in question is advising one thing and doing another. The fact that a doctor smokes or doesn’t mask up in a pandemic does not mean that their advice to not smoke or to wear a mask is not good advice.
If a person regularly snacks on lead paint but tells you not to eat paint, the advice is still sounds even if it’s coming from an idiot.
> it is not ad-hominem to try to understand a person's motivations
Sure, but claims of hypocrisy are still not a rebuttal.
No doubt it was hypocritical for Dr Mike to tell others to social distance and then hop on a boat with a dozen people unmasked, just as it was hypocritical for Gavin Newsom to attend a dinner at The French Laundry while telling others to stay home.
This isn’t actually relevant to whether the advice to socially distance was sound, though.
Yet here you are trying to convince folks why this doesn't lead to poor morals, low self-awareness, and a lack of trust in doctors. We are talking about a doctor, of course, not just an average nobody. And we are talking about a doctor with 6 million subscribers. His influence is wide.
Last I checked, a doctor is not the same as a politician.
> lack of trust in doctors
I don’t think demanding perfection from doctors helps with trust either.
An Ad-Hominem is specifically an attack on someone's arguments using some un-related attack on their character.
EG: "Dr. John's Opinions about vaccines are invalid because he smokes cigarettes." or "James assertion that the earth is round is invalid because he thinks that dogs are better than cats."
Ad-Hom is short for argumentum ad hominem. If you aren't making an argument with your attack, you are just insulting someone.
That is not what an ad hominem is.
Meanwhile habitual frauds and incompetents get a pass because at least their stupidity is consistent.
https://www.wired.com/2009/09/fmrisalmon/
i.e. the well regarded studies, i.e. Kanwisher and the visual processing areas, have follow up studies on primates and surgical volunteers w/ actual electrical activity correlating w/ visual stimuli etc
Influencers in general are always suspect. The things that get you an audience fast are trolling or tabloid-ish tactics like conspiracism.
Indeed, there's been quite a few studies [1] that find just including any old image of a brain with stuff highlighted will cause a paper to be perceived as more scientifically credible.
[1] https://pubmed.ncbi.nlm.nih.gov/17803985/
I'm a software engineer in this field, and this is my layman-learns-a-bit-of-shop-talk understanding of it. Both of these techniques involve multiple layers of statistical assumptions, and multiple steps of "analysing" data, which in itself involves implicit assumptions, rules of thumb and other steps that have never sat well with me. A very basic example of this kind of multi-step data massaging is "does this signal look a bit rough? No worries, let's Gaussian-filter it".
A lot of my skepticism is due to ignorance, no doubt, and I'd probably be braver in making general claims from the image I get in the end if I was more educated in the actual biophysics of it. But my main point is that it is not at all obvious that you can simply claim "signal B shows that signal A doesn't correspond to actual brain activity", when it is quite arguable whether signal B really does measure the ground truth, or whether it is simply prone to different modelling errors.
In the paper itself, the authors say that it is limited by methodology, but because they don't have one they use quantitative MRI. They also say it's because of radiation exposure and blah blah, but the real reason is their uni can't afford a PET scanner for them to use.
"The gold standard for CBF and CMRO2 measurements is 15O PET; but this technique requires an on-site cyclotron, a sophisticated imaging setup and substantial experience in handling three different radiotracers (CBF, 15O-water; CBV, 15O-CO; OEF, 15O-gas) of short half-lives8,35. Furthermore, this invasive method poses certain risks to participants owing to the exposure to radioactivity and arterial sampling."
[0] https://www.siemens-healthineers.com/en-us/magnetic-resonanc...
Two points I'm hoping you can help clarify:
> Researchers ... found that an increased fMRI signal is associated with reduced brain activity in around 40 percent of cases.
So it's not just that they found it was uncorrelated, they found it was anticorrelated in 40% of cases?
And you are suggesting that conclusion suffers from the same potential issues as these fMRI studies in general?
Like you mention, it seems to me if we wanted to really validate the model, we'd have to run the same experiment with two, three, or maybe even more different modalities (fMRI, PET with different tracers, etc).
And how it was almost impossible to reproduce many published and well cited result. It was both exciting and jarring to talk with the neuroscientist, because they ofc knew about this and knew how to read the papers but the one doing more funding/business side ofc didn't really spend much time putting emphasis on that.
One of the team presented a accepted paper that basically used Deep Learning (Attention) to predict images that a person was thinking of, from the fMRI signals. When I asked "but DL is proven to be able to find pattern even in random noise, so how can you be sure this is not just overfitting to artefact?" and there wasn't really any answer to that (or rather the publication didn't take that in to account, although that can be experimentally determined). Still, a month later I saw tech explore or some tech news writing an article about it, something like "AI can now read your brain" and the 1984 implications yada yada.
So this is indeed something probably most practitioners, masters and PhD, realize relatively early.
So now that someone says "you know mindfulness is proven to change your brainwaves?" I always add my story "yes, but the study was done with EEG, so I don't trust the scientific backing of it" (but anecdotally, it helps me)
This all makes sense because fMRI tracks metabolic activity via oxygenation changes, which is much more clearly and plausibly related to tissue health and recovery. In these cases, it is also most likely being used within-subject (i.e. longitudinally) to make comparisons to baselines, rather than in an attempt to make speculative inferences about the mind using groups of people, and based on bespoke statistical procedures and questionable assumptions linking the BOLD response to overly-specific kinds of neural activity.
All to say, this application might not fall in the 40%.
I just find articles like these can't help but feel like they have an agenda to undermine something instead of simply acknowledge the kinds of things it is and isn't working for.
There's no doubt these researchers have found something, but the need for sensationalistic headlines is well known in academia as well.
Sometimes it's noticeable where the research is specific in scope, but the findings are more general and broad.
Interesting. Do you happen to have any more information on this topic? I ask because I was under the impression that concussions are a functional/metabolic injury and not a structural injury, therefore, concussions are not visible on any type of fMRI, CT Scan, etc.. Though, I haven't looked into this topic for almost half a decade, so I imagine things have likely progressed.
THat might be what you're referring to as functional.
Metabolically, or otherwise, if the brain can't oeprate, what can the brain oversee and run?
Concussions are not visible, but blood circulation in a brain is, and where it's not regular, those areas suffer in a brain.
Things luckily have progressed and quite exciting.
Out of convenience, I'll share one I know about (no affiliation) that lay out their therapies and the science behind it as well.
Effectively (I hope I'm getting this accurately) it seems the blood vessels in the brain also have signalling from the blood and oxygen that gets affected which affects things downstream from there.
http://cognitivefxusa.com/
To put it succinctly, I think you have overfit your conclusions on the amount of data you have seen
https://news.ycombinator.com/item?id=46289133
EDIT: And kudos to you and your advisor here.
cog neuro labs need to start organizing their research programs more like giant physics projects. Lots of PIs pooling funding and resources together into one big experiment rather than lots of little underpowered independent labs. But it’s difficult to set up a more institutional structure like this unless there’s a big shift in how we measure career advancement/success.
I'm not sure what you mean by "experimental psychology" though. There are areas like psychophysics that are arguably experimental and have robust findings, and there are some decent-ish studies in clinical psychology too. Here the group sizes are probably actually mostly not too bad.
Areas like social psychology have serious sample size problems, so might benefit, but this field also has serious measurement and reproducibility problems, weak experimental designs, and particularly strong ideological bias among the researchers. I'm not sure larger sample sizes would fix much of the research here.
I can believe it; but a change doesn't have to be sufficient to be ncessary.
Then there are also papers w/ recordings done in primates...
But overall yes - you integrate many modalities of research for a more robust theory of cognition
Within-subject effects (this happens when one does A, but not when doing B) can be fine with small sample sizes, especially if you can repeat variations on A and B many times. This is pretty common in task-based fMRI. Indeed, I'm not sure why you need >2 participants expect to show that the principle is relatively generalizable.
Between-subject comparisons (type A people have this feature, type B people don't) are the problem because people differ in lots of ways and each contributes one measurement, so you have no real way to control for all that extra variation.
You would still in general need many subjects to show the same basic within-subject patterns if you want to claim the pattern is "generalizable", in the sense of "may generalize to most people", but, precisely depending on what you are looking at here, and the strength of the effect, of course you may not need nearly as much participants as in strictly between-subject designs.
With the low test-retest reliability of task fMRI, in general, even in adults, this also means that strictly one-off within-subject designs are also not enough, for certain claims. One sort of has to demonstrate that even the within-subject effect is stable too. This may or may not be plausible for certain things, but it really needs to be considered more regularly and explicitly.
Task-based fMRI has similar individual variability, but with an added complication: adaptive cognition. Once you've performed a task, your brain responds differently the second time. This happens when studies reuse test questions—which is why psychological research develops parallel forms. But adaptation occurs even with parallel forms (commonly used in fMRI for counterbalancing and repeated assessment) because people learn the task type itself. Adaptation even happens within a single scanning session, where BOLD signal amplitude for the same condition typically decreases over time.
These adaptation effects contaminate ICC test-retest reliability estimates when applied naively, as if the brain weren't an organ designed to dynamically respond to its environment. Therefore, some apparent "unreliability" may not reflect the measurement instrument (fMRI) at all, but rather highlights the failures in how we analyze and conceptualize task responses over time.
And yeah, part of why we need more within-subject and longitudinal designs is to get at precisely the things you mention. There is no way to know if the low ICCs we see now are in fact adaptation to the task or task generalities, if they reflect learning that isn't necessarily task-relevant adaptation (e.g. the subject is in a different mood on a later test, and this just leads to a different strategy), if the brain just changes far more than we might expect, or all sorts of other possibilities. I suspect if we ever want fMRI to yield practical or even just really useful theoretical insights, we definitely need to suss out within-subject effects that have high test-retest reliability, regardless of all these possible confounds. Likely finding such effects will involve more than just changes to analysis, but also far more rigorous experimental designs (both in terms of multi-modal data and tighter protocols, etc).
FWIW, we've also noticed a lot of magic can happen too when you suddenly have proper longitudinal data that lets you control things at the individual level.
Sure. Reliable in a small scope, saying very little reliably. It was pretty clear to me observing the reviewing process that "reliable" is doing the heavy lifting here.
>The important thing is to treat motion and physiological sources of noise as a first-order problem that must be taken very seriously and requires strict data quality inclusion criterion
And they don't have that. Ideals are nice ofc. And the nature of the noise, is ofc multi dimensional. For example, one internal study another team did was also neural decoding. Everything you said was explicitly accounted for, so they bought the most reliable measurement tool available, and collected data for more than a year, 8h/day, a person hired just for data.
They got publishable result. But nothing really useful. They noticed that, the brain pattern of the person drastically changed over the time, doing the identical task. So they were trying to measure a moving target. That's a result on itself imo, but not the content of the publication.
>To put it succinctly, I think you have overfit your conclusions on the amount of data you have seen
And to put it succinctly, since you're a practitioner:
"It is difficult to get a man to understand something, when his salary depends on his not understanding it." - Upton Sinclair
I think you have overfitted your conclusion on the data + inevitable bias due to it being your breadwinning profession. I quickly moved my tasks away from the neural part, so I'd like to believe at least I don't have the self interest bias.
So here you say quite a mouthful. If you train it on a pattern it'll see that pattern everywhere - think about the early "Deep Dream" trippy-dogs-pictures nonsense that was pervasive about eight or nine years ago.
I repaired a couple of cameras for someone who was working with a large university hospital about 15 years ago, where they were using admittedly 2010s-era "Deep Learning" to analyse biopsy scans for signs of cancer. It worked brilliantly, at least with the training materials, incredible hit rate, not too terrible false positive rate (no biggie, you're just trying to decide if you want to investigate further), really low false negative rate (if there was cancer it would spot it, for sure, and you don't want to miss that).
But in real-world patient data it went completely mental. The sample data was real-world patient data, too, but on "uncontrolled" patients, it was detecting cancer all over the place. It also detected cancer in pictures of the Oncology department lino floor, it detected cancer in a picture of a guy's ID badge, it detected cancer in a closeup of my car tyre, and it detected cancer in a photo of a grey overcast sky.
Aw no. Now what?
Well, that's why I looked at the camera for them. They'd photographed the biopsies with one camera on site, from "real patients", but a lot of the "clear" biopsies were from other sites.
You're ahead of me now, aren't you?
The "Deep Learning" system had in fact trained itself on a speck of shit on the sensor of one of the cameras, the one used for most of the "has cancer" biopsies and most of the "real patient under test" biopsies. If that little blob of about a dozen slightly darker pixels was present, then it must be cancer because that's what the grown-ups told it. The actual picture content was largely irrelevant because the blob was consistent across all of them.
I'm not too keen on AI in healthcare, not as a definitive "go/no-go" test thing.
You test your DL decoder on held-out data. This is the common practice.
Also saw this irl with a particular NGS diagnostic. This model was initially 99% accurate, P.I. smelled BS, had the grad student crunch the numbers again, 96% accurate, published it, built a company around this product —-> boom, 2 years later it was retracted because the data was a lot of amplified noise, spurious hits, overfitting.
I don’t know jack compared to the average HN contributor, but even I can smell the BS from a mile away in some of these biomedical AI models. Peer review is broken for highly-interdisciplinary research like this.
I also work in the field, specifically with sleep slow-wave enhancement.
Blood flow as a proxy for brain activity I always felt was a weak measure, as the brain activity involved across all manner of operating our biological systems, so is the increased blood flow measured in fMRI a response to cognition, or autonomic activity? What does that oxydation mean.
EEG is similarly flawed when we try to equate "brainwaves" to emotions and consciousness. I think we're almost better off measuring HRV, a much simpler measure, and more reliable.
I'm fascinated that so many people who discuss brainwaves think of them as actual "waves", when it is just how we plot electrical activity which creates a visual wave like pattern.
However, and this is specifically related to our work in sleep, we can detect slow-waves (I dislike that term, it's the synchronous firing of neurons) and we are able to stimulate this restorative brain function through sensory perception during sleep, and even create slow-waves in a lab using TMS.
Research linked on our website [1]
I agree the industry needs to stop projecting what we hope we're seeing with what is actually being measured, and we don't understand enough about how the brain works, but I think completely throwing away any brain related measures we have is going too far.
1 - https://affectablesleep.com/how-it-works#research
For task fMRI, the test-retest reliability is so poor it should probably be considered useless or bordering on pseudoscience, except for in some very limited cases like activation of the visual and/or auditory and/or motor cortex with certain kinds of clear stimuli. For resting-state fMRI (rs-fMRI), the reliabilities are a bit better, but also still generally extremely poor [1-3].
There are also two IMO major and devastating theoretical concerns re fMRI that IMO make the whole thing border on nonsense. One is the assumed relation between the BOLD signal and "activation", and two is the extremely horrible temporal resolution of fMRI.
It is typically assumed that the BOLD response (increased oxygen uptake) (1) corresponds to greater metabolic activity, and (2) increased metabolic activity corresponds to "activation" of those tissues. This trades dubiously on the meaning of "activation", often assuming "activation = excitatory", when we know in fact much metabolic activity is inhibitory. fMRI cannot distinguish between these things.
There are other deeper issues, in that it is not even clear to what extent the BOLD signal is from neurons at all (could be glia), and it is possible the BOLD signal must be interpreted differently in different brain regions, and that the usual analyses looking for a "spike" in BOLD activity are basically nonsense, since BOLD activity isn't even related to this at all, but rather the local field potential, instead. All this is reviewed in [4].
Re: temporal resolution, essentially, if you pay attention to what is going on in your mind, you know that a LOT of thought can happen in just 0.5 seconds (think of when you have a flash of insight that unifies a bunch of ideas). Or think of how quickly processing must be happening in order for us to process a movie or animation sequence where there are up to e.g. 10 cuts / shots within a single second. There is also just biological evidence that neurons take only milliseconds to spike, and that a sequence of spikes (well under 100ms) can convey meaningful information.
However, the lowest temporal resolutions (repetition times) in fMRI are only around 0.7 seconds. IMO this means that the ONLY way to analyze fMRI that makes sense is to see it as an emergent phenomenon that may be correlated with certain kinds of long-term activity reflecting cyclical BOLD patterns / low-frequency patterns of the BOLD response. I.e. rs-fMRI is the only fMRI that has ever made much sense a priori. The solution to this is maybe to combine EEG (extremely high temporal resolution, clear use in monitoring realtime brain changes like meditative states and in biofeedback training) with fMRI, as in e.g. [5]. But, it may still well be just the case fMRI remains mostly useless.
[1] Elliott, M. L., Knodt, A. R., Ireland, D., Morris, M. L., Poulton, R., Ramrakha, S., Sison, M. L., Moffitt, T. E., Caspi, A., & Hariri, A. R. (2020). What Is the Test-Retest Reliability of Common Task-Functional MRI Measures? New Empirical Evidence and a Meta-Analysis. Psychological Science, 31(7), 792–806. https://doi.org/10.1177/0956797620916786
[2] Herting, M. M., Gautam, P., Chen, Z., Mezher, A., & Vetter, N. C. (2018). Test-retest reliability of longitudinal task-based fMRI: Implications for developmental studies. Developmental Cognitive Neuroscience, 33, 17–26. https://doi.org/10.1016/j.dcn.2017.07.001
[3] Termenon, M., Jaillard, A., Delon-Martin, C., & Achard, S. (2016). Reliability of graph analysis of resting state fMRI using test-retest dataset from the Human Connectome Project. NeuroImage, 142, 172–187. https://doi.org/10.1016/j.neuroimage.2016.05.062
[4] Ekstrom, A. (2010). How and when the fMRI BOLD signal relates to underlying neural activity: The danger in dissociation. Brain Research Reviews, 62(2), 233–244. https://doi.org/10.1016/j.brainresrev.2009.12.004, https://scholar.google.ca/scholar?cluster=642045057386053841...
[5] Ahmad, R. F., Malik, A. S., Kamel, N., Reza, F., & Abdullah, J. M. (2016). Simultaneous EEG-fMRI for working memory of the human brain. Australasian Physical & Engineering Sciences in Medicine, 39(2), 363–378. https://doi.org/10.1007/s13246-016-0438-x
Even if neuronal activity is (obviously) faster, the (assumed) neuro-vascular coupling is slower. Typically there are several seconds till you get a BOLD response after a stimulus or task, and this has nothing to do with fMRI sampling rate (fNIRS can have much faster sampling rate, but the BOLD response it measures is equally slow, too). Think of it as that neuronal spiking happens in a range of up to some hundred milliseconds and the body changing the blood flow happens much slower than that.
The issue is that measuring the BOLD response, even in best case scenario, is a very very indirect measure of neuronal activity. This is typically lost when people referring to fMRI studies as discovering "mental representations" in the brain and other non-sense, but here we are. Criticising the validity of the BOLD response itself, though, is certainly interesting.
But I don't think we are really disagreeing on anything major here. I do think there is likely some useful potential locked away in carefully designed resting-state fMRI studies, probably especially for certain chronic and/or persistent systemic cognitive things like e.g. ADHD, autism, or, perhaps more fruitfully, it might just help with more basic understanding of things like sleep. But, I also won't be holding my breath for anything major coming out of fMRI anytime soon.
Funny, because this exact measure [0] was brought up in response to a claim I made over a year ago [1] that both the spatial and temporal resolution of our instruments for measuring the brain are at present inadequate.
There is a worrying trend of faith in scientism, or the belief that we already have all the answers squirreled away in a journal somewhere.
[0] https://news.ycombinator.com/item?id=41834346
[1] https://news.ycombinator.com/item?id=41807867
Obviously we hope what we learn from e.g. psychology and fMRI will help us explain more things about the mind, and surely most researchers in psychology hope their research will help us get some answers on things related to qualia as well. And almost certainly most good / consistent reductionist researchers must believe that qualia arise from the brain, at least in significant part.
However, precisely by this reductionist logic, and since it is immediately and phenomenally clear that the rate of change of qualia in the mind (or the "amount" of different qualia, i.e. images or sounds that one can process or generate in the mind in under a second) is incredibly fast, it follows immediately and logically without any need for an experiment that fMRI cannot have the temporal resolution needed for a rich understanding of the mind, simply based on knowing the TR (temporal sampling resolution) is so poor. And yet, I also find a lot of people in scientific brain research go oddly silent or seem to refuse to accept this argument unless some strange sort of published, quantificationist operationalization can be pointed to (hence my pre-emptive mentioning information transmission in neurons in under 100ms).
I'm not sure I'd call this scientism, exactly, I tend to see it as "selective quantificationism", i.e. that certain truths can only be proven as true if you introduce some kind of numerical measurement procedure and metrical abstraction. Like, no one demands a study with Scoville units to prove that e.g. a ghost pepper is at least an order of magnitude hotter than candied ginger, even though this is as blazingly obvious as the fact that the mind moves too fast for something that can barely capture images of the brain at a rate of two per second.
I think I broadly agree with you though that credulousness to (statistically and methodologically weak) scientific / technological claims mostly comes down to vibes and desires / needs, and not statistical significance, logical rigor, evidence, or etc.
Where needs / desires are high, vibes will win over rationality, and vice-versa. It is easier for people to be objective about science that doesn't really clearly matter in any obvious direction, or at all. fMRI is "the mind", and thus consciousness, and so unfortunately reduces rational evaluation in much the same way speculation about AI and "consciousness" and etc does. *Shrug*
It will also require fMRI researchers to think more carefully about their theories as well (e.g. noting the speed of the mind / amount and kinds of thinking involved in certain tasks, and being realistic about whether or not fMRI could actually capture something meaningful there). Too often there is no theory, and too many studies are just correlating patterns with some task without actually carefully thinking about the task and deconstructing the components, testing activations in those (e.g. ablation studies in AI research) and etc.
For readers who don't know, we can measure at a higher temporal resolution better if we use some tricks, and also massively sacrifice spatial resolution ("reduce volumes") and/or how much of the brain is scanned (look at single slices), but the spatial resolution in most fMRI given e.g. a 0.5 TR (2 images per second) is usually already quite poor (generally already getting difficult to clearly even make out gyri and basic brain anatomy: see for example Figures 7 and on here, noting the TRs in the captions: https://www.frontiersin.org/journals/neuroscience/articles/1...).
Still, it's a good point, and you're right of course newer and better scanners and techniques might improve things here on both fronts, but my understanding is that the magnetic field strengths needed to actually get the right combo of spatial and temporal resolution are, unfortunately, fatal, so we are really up against a physical/biological limit here.
And as you said, it isn't that useful anyway, because the BOLD response is already so slow, and obviously something just emerging from the sum of a massive amount of far more rapid electrochemical signaling that the fMRI just can't measure anyway.
If they were to measure a person who performs mental arithmetic on a daily basis, I'd expect his brain activity and oxygen consumption to be lower than those of a person who never does it. How much difference would that make?
The question then is, do you expect a person who is really good at mental arithmetic to have less neural firing on arithmetic tasks (e.g., what is 147 x 38) than the average joe. I would hypothesize yes overall to solve each question; however, I'd also hypothesize the momentary max intensity of the expert to peak higher. Think of a bodybuilder vs. a SWE bench-pressing 100 lbs for 50 reps. The bodybuilder has way more muscle to devote to a single rep, and will likely finish the set in 20 seconds, while the SWE is going to take like 30 minutes ;)
It involved going to the lab and practicing the thing (a puzzle / maze) I would be shown during the actual MRI. I think I went in to “practice” a couple times before showing up and doing it in the machine.
IIRC the purpose of practicing was exactly that, to avoid me trying ti learn something during the scan (since that wasn’t the intention of the study).
In other words, hopefully people control for that variable.
(Side note: I absolutely fell asleep during half the scan. Oops! I felt bad, but I guess that’s a risk when you recruit sleep deprived college kids!)
https://source.washu.edu/2025/12/psychedelics-disrupt-normal...
https://www.quantamagazine.org/a-math-theory-for-why-people-...
Condolences.
It's why I generally only ask questions, or ask for clarification instead of directly challenging something I think might be wrong now in threads that aren't related to something I have deeeeep personal knowledge of. I know when I'm out of my area, and don't want to add to the ignorance.
Being all "PC" and "nice" about stuff that is what it is, or isn't -- THAT adds to ignorance.
fMRI is a cool, expensive tech, like so many others in genetics and other diagnostics. These technologies create good jobs ("doing well by doing good").
But as other comments point out, and practitioners know, their usefulness for patients is more dubious.
Wondering how they created that baseline. Was it with fMRI data (which has deviance from actual data, as pointed out)? Or was it through other means?
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