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> They say the null is known to be false. Can you quote where they say that, because the article says: "Because two groups of subjects are randomly drawn from

> They say the null is known to be false. Can you quote where they say that, because the article says: "Because two groups of subjects are randomly drawn from a large group of healthy controls, the null hypothesis of no group difference in brain activation should be true. Moreover, because the resting-state fMRI data should contain no consistent shifts in blood oxygen level-dependent (BOLD) activity, for a single group of subjects the null hypothesis of mean zero activation should also be true." There is also this quote: "Resting-state data should not contain systematic changes in brain activity, but our previous work (14) showed that the assumed activity paradigm can have a large impact on the degree of false positives. " This does not mean the null is false. That papers shows that temporal autocorrelations can also introduce spurious false positives. These factors affect block, but not event-related designs. They can be avoided by filtering--and maybe by averaging over many subjects (since these oscillations aren't locked to anything). In any case, they don't appear, as you can verify on the right side of Figure 1A. Finally, the discussion has a paragraph describing reasons why the experiment might be flawed, but most of those possible objections seem answered.

nonbel 10y ago
The key point is that I am saying the second quote does say the null is false. This is where we differ. The actual null hypothesis they are testing is different than the one they actually care and talk about. It includes additional auxilliary assumptions that they say are wrong. What gets plugged in as the null hypothesis is not a problem in the realm of statistics. That is a choice of the researcher. The stats algorithm worked fine.
- mattkrause 10y ago
  That sentence is closer to a link than actual content. If you look at paper (14), it has the following graph: http://imgur.com/a/oxSqp http://imgur.com/a/oxSqp It shows that the null model is perfectly appropriate for individual voxels under the event-related paradigms. The FWER there is indistinguishable from 5%, as it should be if the null were true. It also shows that FWER for the null model is inflated by ~6x for blocked paradigms. As they say in the new paper, it is unclear if that would re-occur in a multi-subject experiment (that was apparently a criticism of the first paper). Based on this, the absolute worst you can say is "the data on blocked paradigms is not very interesting", but that leaves their event-related data completely intact. As for the stats thing, the actual formal itself test is obvious fine--it's a t-test! They're saying that the pipeline that runs between the raw DICOM output of the scanner and the t-test, which is an equally important part of the testing procedure, is flawed. I'm happy to keep discussing this with you, but we seem to be going in circles. If "additional auxiliary assumptions" are the problem here, please describe them. If you think the 2012 NeuroImage paper proves that their null model is wrong in all cases, I'd love to read a few sentences describing how that follows.
  - nonbel 10y ago
    >"They're saying that the pipeline that runs between the raw DICOM output of the scanner and the t-test, which is an equally important part of the testing procedure, is flawed." Yes, in other words the null hypothesis is being rendered false, by some aspect of the analysis pipeline. I am saying that is the correct description of the problem. Describing the problem as excess false positives is confused, because these are true positives. They appear to have successfully identified a problem, but then described and analyzed it incorrectly. >"The FWER there is indistinguishable from 5%, as it should be if the null were true." If the null is true, the pvalues should be samples from a uniform distribution. Another thing is they should have shown histograms of these. The 5% below 0.05 is not the whole story. There could be other ways the deviation manifests and/or they just chose sample sizes to be powered to get that result. "I'd love to read a few sentences describing how that follows." We have already read the exact sentences that mean the null model is false, multiple times. You have agreed that something was wrong, but want to call it something other than the null model.
    - deleted 10y ago
      [deleted]
    - ihnorton 10y ago
      > Describing the problem as excess false positives is confused, because these are true positives. This is deeply wrong. A "true positive" in this context would mean that the resting brain activity of multiple subjects is actually correlated with arbitrary length, randomized, moving test windows. Again, the data is untreated; they are imposing arbitrary test windows and looking for increases in brain activity (increased image intensity) correlated with the stimulus-ON sections of the pseudo-treatment. From reading some of your prior comments on the subject, it seems prudent to point out that the causal link between stimulus and BOLD effect is quite literally observable: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130346/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130346/
      - nonbel 10y ago
        >"This is deeply wrong. A "true positive" in this context would mean that the resting brain activity of multiple subjects is actually correlated with arbitrary length, randomized, moving test windows." This is the same misconception I have been trying to dispel. The null hypothesis is not the inverse of that "layman's" statement, it is a specific set of predicted results calculated in a very specific way. It is a mathematical statement, not an English prose statement. In this case, apparently one part of this calculation (this assumption about autocorrelation, whatever it is doesn't matter to my point) has lead to such a large deviation from the observations that the null model has been rejected. The null model has been rejected correctly. This is not a false positive. The problem here is not false-positives due to faulty stats. It is the poor mapping between the hypothesis the researchers want to test, and the null hypothesis they are actually testing. The tools provided by statisticians look like they worked just fine in this case. If the researchers have decided to use them for GIGO, that is not a statistical problem.

nonbel 10y ago

The key point is that I am saying the second quote does say the null is false. This is where we differ. The actual null hypothesis they are testing is different than the one they actually care and talk about. It includes additional auxilliary assumptions that they say are wrong. What gets plugged in as the null hypothesis is not a problem in the realm of statistics. That is a choice of the researcher. The stats algorithm worked fine.

mattkrause 10y ago
That sentence is closer to a link than actual content. If you look at paper (14), it has the following graph: http://imgur.com/a/oxSqp http://imgur.com/a/oxSqp It shows that the null model is perfectly appropriate for individual voxels under the event-related paradigms. The FWER there is indistinguishable from 5%, as it should be if the null were true. It also shows that FWER for the null model is inflated by ~6x for blocked paradigms. As they say in the new paper, it is unclear if that would re-occur in a multi-subject experiment (that was apparently a criticism of the first paper). Based on this, the absolute worst you can say is "the data on blocked paradigms is not very interesting", but that leaves their event-related data completely intact. As for the stats thing, the actual formal itself test is obvious fine--it's a t-test! They're saying that the pipeline that runs between the raw DICOM output of the scanner and the t-test, which is an equally important part of the testing procedure, is flawed. I'm happy to keep discussing this with you, but we seem to be going in circles. If "additional auxiliary assumptions" are the problem here, please describe them. If you think the 2012 NeuroImage paper proves that their null model is wrong in all cases, I'd love to read a few sentences describing how that follows.
- nonbel 10y ago
  >"They're saying that the pipeline that runs between the raw DICOM output of the scanner and the t-test, which is an equally important part of the testing procedure, is flawed." Yes, in other words the null hypothesis is being rendered false, by some aspect of the analysis pipeline. I am saying that is the correct description of the problem. Describing the problem as excess false positives is confused, because these are true positives. They appear to have successfully identified a problem, but then described and analyzed it incorrectly. >"The FWER there is indistinguishable from 5%, as it should be if the null were true." If the null is true, the pvalues should be samples from a uniform distribution. Another thing is they should have shown histograms of these. The 5% below 0.05 is not the whole story. There could be other ways the deviation manifests and/or they just chose sample sizes to be powered to get that result. "I'd love to read a few sentences describing how that follows." We have already read the exact sentences that mean the null model is false, multiple times. You have agreed that something was wrong, but want to call it something other than the null model.
  - deleted 10y ago
    [deleted]
  - ihnorton 10y ago
    > Describing the problem as excess false positives is confused, because these are true positives. This is deeply wrong. A "true positive" in this context would mean that the resting brain activity of multiple subjects is actually correlated with arbitrary length, randomized, moving test windows. Again, the data is untreated; they are imposing arbitrary test windows and looking for increases in brain activity (increased image intensity) correlated with the stimulus-ON sections of the pseudo-treatment. From reading some of your prior comments on the subject, it seems prudent to point out that the causal link between stimulus and BOLD effect is quite literally observable: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130346/ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130346/
    - nonbel 10y ago
      >"This is deeply wrong. A "true positive" in this context would mean that the resting brain activity of multiple subjects is actually correlated with arbitrary length, randomized, moving test windows." This is the same misconception I have been trying to dispel. The null hypothesis is not the inverse of that "layman's" statement, it is a specific set of predicted results calculated in a very specific way. It is a mathematical statement, not an English prose statement. In this case, apparently one part of this calculation (this assumption about autocorrelation, whatever it is doesn't matter to my point) has lead to such a large deviation from the observations that the null model has been rejected. The null model has been rejected correctly. This is not a false positive. The problem here is not false-positives due to faulty stats. It is the poor mapping between the hypothesis the researchers want to test, and the null hypothesis they are actually testing. The tools provided by statisticians look like they worked just fine in this case. If the researchers have decided to use them for GIGO, that is not a statistical problem.