"there are abundant opportunities for error, particularly when you
are relying on software to do much of the work. This was made glaringly
apparent back in 2009, when a graduate student conducted an fM.R.I.
scan of a dead salmon and found neural activity in its brain when it
was shown photographs of humans in social situations. Again, it was
a salmon. And it was dead."
I read a great (in the sense that it was laugh out loud preposterous) paper that used this experiment to assert that re-incarnation was a thing, as the salmon, post mortem, had been re-incarnated as a person but not yet born, their portal to the soul was now interpreting pictures as if the fish were both human and alive. It was a tour de force in circular reasoning. It may have been in the Journal of Irreproducible Results.
One way to read scientific papers is to deconstruct the apparatus in the experiment to see if it could actually produce the data in the paper, and if so, what would have to be true for it to do so. Good papers go through that in experimental design section, bad papers gloss over it or toss it out as a stipulation "We used the same instrument that everyone else uses to read brain activity the fMRI scanner." (bad) "We used a technique called fMRI scanning which measures the magnetic resonance of hemoglobin in the blood, specifically it measures the difference between oxygen rich and oxygen poor hemoglobin." (much better) Then as a reader you can see that any change in oxygen, whether it is brain activity or bacterial decomposition would show up in such a resonance scan.
This is actually a somewhat different issue. The salmon "result" is essentially a multiple comparisons problem. In a typical fMRI analysis, the researchers run a huge number of significance tests, one per voxel (i.e., 3d pixel). Even if false positives are relatively rare on a per-test basis, you're going to see a non-trivial amount of them when you run a large number of tests. This phenomenon was well-known in the statistical literature...and in the fMRI literature, but sometimes it takes a cute little parable, like the salmon, to drive something home.
In this case, however, there was one honest-to-God bug (in AFNI). The other software modeled something in a way that sounds fairly reasonable, but didn't quite line up with the way that nature (or, actually, the scanner manufacturers) behaved. Unlike the fish issue, I think you could legitimately try to do things correctly and still get bit by this sort of bug.
Without looking into this a lot, probably not much. This parcellation is described as multi-modal, so it's not using only structural or only diffusion, but I doubt it's using fMRI much if at all. Even if so, I'm sure they validated the parcellation against pre-existing parcellations. I'll double-check this tomorrow when I can access the full paper more easily.
The parcellations in the paper were defined in part on task-dependent activity. I'm asking a simpler question: did the HCP paper use the false-positive prone analysis software?
Much better article and discussion in the link that sctb posted. This article has a number of issues... it's conflating several different problems with fMRI without really explaining them, then arguing that it's worthless -- as opposed to just needing more groups to follow best practices.
This story reminds me of the Therac-25 (although no deaths were attributed to this bug). The book "Fatal Defect" should be required reading for any software engineer.
The authors' of the paper feel it is being misinterpreted and tried to submit errata to PNAS:[1]
They tried to change the following sentence:
“These results question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results.”
To
“These results question the validity of a number of fMRI studies and may have a large impact on the interpretation of weakly significant neuroimaging results.”
20 comments
[ 3.4 ms ] story [ 50.5 ms ] threadOne way to read scientific papers is to deconstruct the apparatus in the experiment to see if it could actually produce the data in the paper, and if so, what would have to be true for it to do so. Good papers go through that in experimental design section, bad papers gloss over it or toss it out as a stipulation "We used the same instrument that everyone else uses to read brain activity the fMRI scanner." (bad) "We used a technique called fMRI scanning which measures the magnetic resonance of hemoglobin in the blood, specifically it measures the difference between oxygen rich and oxygen poor hemoglobin." (much better) Then as a reader you can see that any change in oxygen, whether it is brain activity or bacterial decomposition would show up in such a resonance scan.
In this case, however, there was one honest-to-God bug (in AFNI). The other software modeled something in a way that sounds fairly reasonable, but didn't quite line up with the way that nature (or, actually, the scanner manufacturers) behaved. Unlike the fish issue, I think you could legitimately try to do things correctly and still get bit by this sort of bug.
http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf
Choice quotes:
"[the salmon] was not alive at the time of scanning"
"The salmon was asked to determine what emotion the individual in the photo must have been experiencing."
You fuck the formatting for mobile users.
http://www.nature.com/nature/journal/v536/n7615/full/nature1...
Why bad scientific code beats code following “best practices” (2014) https://news.ycombinator.com/item?id=12377385
https://www.amazon.com/Fatal-Defect-Chasing-Killer-Computer/...
They tried to change the following sentence:
“These results question the validity of some 40,000 fMRI studies and may have a large impact on the interpretation of neuroimaging results.”
To
“These results question the validity of a number of fMRI studies and may have a large impact on the interpretation of weakly significant neuroimaging results.”
Link to discussion in /r/NeuroScience: https://www.reddit.com/r/neuroscience/comments/4ri72b/the_so...
[1] http://blogs.warwick.ac.uk/nichols/entry/errata_for_cluster/