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.
Structural MRI is even more abused, where people find "differences" between 2 groups with ridiculously small sample sizes.
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.
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
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.
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.
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.
There are good ones but you have to be discerning.
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.
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 the device to get an independent measure of brain activation, 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)
fMRI's are being used in TBI/Concussion recovery that are study backed and seem to be delivering results.
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 likely is a simple comparison to baseline rather than bespoke statistical analyses relying on questionable assumptions about the BOLD response being related 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.
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: The reason being, with reliabilities as bad as these, it is obvious almost all fMRI studies are massively underpowered, and you really need to have hundreds or even up to a thousand participants to detect effects with any statistical reliability. Very few fMRI studies ever have even close to these numbers (https://www.nature.com/articles/s42003-018-0073-z).
EDIT: And kudos to you and your advisor here.
EDIT2: I will also say that a lot of the research on fMRI methods is very solid and often quite reproducible. I.e. papers that pioneer new analytic methods and/or investigate pipelines and such. There is definitely a lot of fMRI research telling us a lot of interesting and likely reliable things about fMRI, but there is very little fMRI research that is telling us anything reliably generalizable about people or cognition.
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.
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.
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?
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.
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?
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, I think you can 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!)
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 ;)
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.
https://source.washu.edu/2025/12/psychedelics-disrupt-normal...
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.
I think your expertise would be very welcome, but this comment is entirely unhelpful as-is. Saying there are bad comments in this thread and also that there is good literature out there without providing any specifics at all is just noise.
You don't have to respond to every comment you see to contribute to the discussion. At minimum, could you provide a hint for some literature you suggest reading?
Nah, it's not noise. It's a useful reminder not to take any comments too seriously and that this topic is far outside the average commenter's expertise.
I say this as a psychologist who is advising you to ignore all claims to the contrary, because they are misinformed. It is clear from the literature.
The literature is huge, and my bias is that I believe most of the only really good fMRI research is methodological research (i.e. about what fMRI actually means, and how to reliably analyze it). Many of the links I've provided here speak to this.
I don't think there is much reliable fMRI research that tells us anything about people, emotions, or cognition, beyond confirming some likely localization of function to the sensory and motor cortices, and some stuff about the Default Mode Network(s) that is of unclear importance.
A lot of the more reliable stuff involves the Human Connectome Project (HCP) fMRI data, since this was done very carefully with a lot of participants, if you want a place to start for actual human-relevant findings. But the field is still really young.
Instead of a nothingburger, you could have used your academic prowess to break down the top 1/2 misconceptions with expertise.
You might not have time to respond to all the comments but a couple of clarifications could have helped anyone else who doesn't comment without experience.
Just saying that next time you can be the change you want to see in HN instead of wasting text telling us how ignorant we are.
To me this is like shitting on cars in 1925 because they kill people every now and then. Cars didn't go away, and nor will fMRI, until someone finds a better way to measure living people's brains.
TUM's press is being sloppy, from conflating fMRI with MRI to presuming this is revolutionary, and ignoring earlier empirical work against this narrative (Windkessel's, Logothetis beta/gamma coupling, etc.)
fMRI has always had folks highlighting how shaky the science is. It's not the strongest of experimental techniques.
Risk of false positives in fMRI of post-mortem Atlantic salmon (2010) [pdf] - https://news.ycombinator.com/item?id=15598429 - Nov 2017 (41 comments)
Scanning dead salmon in fMRI machine (2009) - https://news.ycombinator.com/item?id=831454 - Sept 2009 (1 comment)
Direct link to the poster presentation: http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf
We sped up fMRI analysis using distributed computing (MapReduce) and GPUs back in 2014.
Funny how nothing has changes.
What's still amazing is fMRI can provide more visual context of what's happening in the brain, in what region, and activities that can help that improve.
There are other complementary technologies like QEEG and SPECT that can also shed a light as well.
It does seem the case that fMRI cann be more of a snapshot photo, and technologies like SPECT can provide more of a regional time lapse of activity.
[just kidding]
1. http://prefrontal.org/blog/2009/01/voodoo-correlations-in-so...
2. https://journals.sagepub.com/doi/10.1111/j.1745-6924.2009.01...
I know the actual diagnosis is several times more layered than this attempt at an explanation, but I always felt that trying to explain the brain by peering at it from outwards is like trying to debug code by looking at a motherboard through a bad microscope.
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