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>"The null hypothesis is that neural activity is the same across blocks." You may have written this before seeing my other response, but I will repeat the poin

by nonbel 10y ago

>"The null hypothesis is that neural activity is the same across blocks." You may have written this before seeing my other response, but I will repeat the point here. You have mentioned only one part of the null hypothesis, there are other assumptions being made that can be violated. From their description, it sounds like they discovered one that is causing the null model to make bad predictions. Btw, I am using "null hypothesis" and "null model "interchangeably here. Really, null model should be the preferred term but people are so used to "hypothesis".

mattkrause 10y ago
Can you explain what those assumptions are then, even roughly? I read the PNAS paper as being an extension of their previous work, not a contradiction of it. They've scaled the data set up (the previous paper had less data), added more packages (the previous paper used only SPM), and ran it as a group analysis (the previous paper was within subjects). They pretty much say that right in the PNAS text: "That work found a high degree of false positives, up to 70% compared with the expected 5%, likely due to a simplistic temporal autocorrelation model in SPM. It was, however, not clear whether these problems would propagate to group studies. Another unanswered question was the statistical validity of other fMRI software packages. We address these limitations in the current work with an evaluation of group inference with the three most common fMRI software packages [SPM (15, 16), FSL (17), and AFNI (18)]." Furthermore, the NeuroImage paper says that the AR(1) temporal autocorrelation model in SPM is actually not too problematic for event-related designs: "Overall, a simple AR(1) model for temporal correlations appears to be adequate for fast designs (E1 and E2) at all three TRs. However, there is a massive inflation of false positive rates at short TRs that is particularly pronounced for slower (block) designs. " If that's what is bugging you, just ignore the red and yellow bars in the PNAS figures.
- nonbel 10y ago
  >"Can you explain what those assumptions are then, even roughly?" I have not looked at the code myself. I only read their description of it. As I quoted earlier: "assumed activity paradigm can have a large impact on the degree of false positives" If the assumed activity paradigm affects (what they are calling) the false positive rate, it is obviously part of the model. If they are arbitrarily varying it because they don't know which one is "best", it is clearly wrong. The t-tests will pick up this wrong assumption if there is enough data to do so (even without knowing the details, remember they say it affects the "false positive" rate). The t-test is doing it's job correctly, however, they are plugging in a known to be incorrect null hypothesis, rendering this testing procedure pointless.
  - mattkrause 10y ago
    Maybe we're agreeing? The ENTIRE POINT of the paper is that there are inappropriate assumptions baked into the defaults of FSL and SPM. (AFNI has an actual code-does-the-wrong-thing bug). These errors result in cluster-wise tests being inappropriately sized: when thresholds are set to p<0.001, there are more than 0.1% false positives. They tested this by dividing a single stream of "off" data into segments of "ON" and "OFF" data. They do this in four different ways (two block paradigms and two event-related paradigms). Most fMRI experiments use a paradigm that is close to one of these, so this isn't a fishing attempt--it's meant to make the results broadly applicable . They argue that, by construction, no ON data is present in this pseudo-data. Therefore, any ON calls must be spurious/false alarms/type I errors/etc, which lets you calculate the actual FWER and compare it with its nominal size. You seem to be arguing that this step is not true--some other assumption, which they knew about(?), causes it. The NeuroImage paper says that different assumed activity paradigms have different false positive rates because the assumed model is dumb. The event-related paradigms alternate between on and off rapidly (and at random), which washes out the effects of unmodelled low frequencies. The much slower block designs, on the other hand, are especially susceptible to this problem because they spend a long time in each condition. The current work extends that to look at other ways in which the default assumptions might be dumb in SPM, while adding in more data, different tests, and other packages. I am open to the argument that the Block Design data (e.g., in Figure 2 in PNAS) does not add much because the NeuroImage paper already suggests those designs are screwed. However, it also says that the event-related designs are actually in pretty good shape: the voxel-based familywise error rates in the NeuroImage paper are about right (Figure 2), but the cluster FWER for the same design is through the roof in the PNAS paper. Otherwise though, I don't see many unwarranted/undiscussed assumptions in the paper. If you want to keep claiming that the authors (and I) are making wild assumptions, it would be nice to have a vague sense of what they are and why they/I am wrong.
    - nonbel 10y ago
      I think we are largely in agreement on the surface. The problem is that they are attributing the "positive" results to statistical error, when it isn't due to that. The stats are working just fine and correctly telling us the null hypothesis is false. The problem is with the crappy null hypothesis, not the stats.

by nonbel 10y ago

mattkrause 10y ago

Can you explain what those assumptions are then, even roughly? I read the PNAS paper as being an extension of their previous work, not a contradiction of it. They've scaled the data set up (the previous paper had less data), added more packages (the previous paper used only SPM), and ran it as a group analysis (the previous paper was within subjects). They pretty much say that right in the PNAS text: "That work found a high degree of false positives, up to 70% compared with the expected 5%, likely due to a simplistic temporal autocorrelation model in SPM. It was, however, not clear whether these problems would propagate to group studies. Another unanswered question was the statistical validity of other fMRI software packages. We address these limitations in the current work with an evaluation of group inference with the three most common fMRI software packages [SPM (15, 16), FSL (17), and AFNI (18)]." Furthermore, the NeuroImage paper says that the AR(1) temporal autocorrelation model in SPM is actually not too problematic for event-related designs: "Overall, a simple AR(1) model for temporal correlations appears to be adequate for fast designs (E1 and E2) at all three TRs. However, there is a massive inflation of false positive rates at short TRs that is particularly pronounced for slower (block) designs. " If that's what is bugging you, just ignore the red and yellow bars in the PNAS figures.

nonbel 10y ago
>"Can you explain what those assumptions are then, even roughly?" I have not looked at the code myself. I only read their description of it. As I quoted earlier: "assumed activity paradigm can have a large impact on the degree of false positives" If the assumed activity paradigm affects (what they are calling) the false positive rate, it is obviously part of the model. If they are arbitrarily varying it because they don't know which one is "best", it is clearly wrong. The t-tests will pick up this wrong assumption if there is enough data to do so (even without knowing the details, remember they say it affects the "false positive" rate). The t-test is doing it's job correctly, however, they are plugging in a known to be incorrect null hypothesis, rendering this testing procedure pointless.
- mattkrause 10y ago
  Maybe we're agreeing? The ENTIRE POINT of the paper is that there are inappropriate assumptions baked into the defaults of FSL and SPM. (AFNI has an actual code-does-the-wrong-thing bug). These errors result in cluster-wise tests being inappropriately sized: when thresholds are set to p<0.001, there are more than 0.1% false positives. They tested this by dividing a single stream of "off" data into segments of "ON" and "OFF" data. They do this in four different ways (two block paradigms and two event-related paradigms). Most fMRI experiments use a paradigm that is close to one of these, so this isn't a fishing attempt--it's meant to make the results broadly applicable . They argue that, by construction, no ON data is present in this pseudo-data. Therefore, any ON calls must be spurious/false alarms/type I errors/etc, which lets you calculate the actual FWER and compare it with its nominal size. You seem to be arguing that this step is not true--some other assumption, which they knew about(?), causes it. The NeuroImage paper says that different assumed activity paradigms have different false positive rates because the assumed model is dumb. The event-related paradigms alternate between on and off rapidly (and at random), which washes out the effects of unmodelled low frequencies. The much slower block designs, on the other hand, are especially susceptible to this problem because they spend a long time in each condition. The current work extends that to look at other ways in which the default assumptions might be dumb in SPM, while adding in more data, different tests, and other packages. I am open to the argument that the Block Design data (e.g., in Figure 2 in PNAS) does not add much because the NeuroImage paper already suggests those designs are screwed. However, it also says that the event-related designs are actually in pretty good shape: the voxel-based familywise error rates in the NeuroImage paper are about right (Figure 2), but the cluster FWER for the same design is through the roof in the PNAS paper. Otherwise though, I don't see many unwarranted/undiscussed assumptions in the paper. If you want to keep claiming that the authors (and I) are making wild assumptions, it would be nice to have a vague sense of what they are and why they/I am wrong.
  - nonbel 10y ago
    I think we are largely in agreement on the surface. The problem is that they are attributing the "positive" results to statistical error, when it isn't due to that. The stats are working just fine and correctly telling us the null hypothesis is false. The problem is with the crappy null hypothesis, not the stats.