Abstract
Published studies using functional and structural MRI include many errors in the way data are analyzed and conclusions reported. This was observed when working on a comprehensive review of the neural bases of synesthesia, but these errors are probably endemic to neuroimaging studies. All studies reviewed had based their conclusions using Null Hypothesis Significance Tests (NHST). NHST have yet been criticized since their inception because they are more appropriate for taking decisions related to a Null hypothesis (like in manufacturing) than for making inferences about behavioral and neuronal processes. Here I focus on a few key problems of NHST related to brain imaging techniques, and explain why or when we should not rely on “significance” tests. I also observed that, often, the ill-posed logic of NHST was even not correctly applied, and describe what I identified as common mistakes or at least problematic practices in published papers, in light of what could be considered as the very basics of statistical inference. MRI statistics also involve much more complex issues than standard statistical inference. Analysis pipelines vary a lot between studies, even for those using the same software, and there is no consensus which pipeline is the best. I propose a synthetic view of the logic behind the possible methodological choices, and warn against the usage and interpretation of two statistical methods popular in brain imaging studies, the false discovery rate (FDR) procedure and permutation tests. I suggest that current models for the analysis of brain imaging data suffer from serious limitations and call for a revision taking into account the “new statistics” (confidence intervals) logic.
Highlights
STATISTICAL INFERENCE AND Null Hypothesis Significance Tests (NHST)STATISTICAL INFERENCE Empirical investigations are based on statistical inference, even before computing any kind of statistical test: one wants to draw general conclusions based on a limited set of www.frontiersin.org HupéCommon mistakes and pitfalls in neuroimaging studies observations
The first part of this paper describes what I consider as the very basics of statistical inference, and what I understood of Null Hypothesis Significance Tests (NHST)
The list may not be exhaustive: it contains the errors we found in our review of the literature on synesthesia
Summary
STATISTICAL INFERENCE AND NHSTSTATISTICAL INFERENCE Empirical investigations are based on statistical inference, even before computing any kind of statistical test: one wants to draw general conclusions (the population) based on a limited set of www.frontiersin.org HupéCommon mistakes and pitfalls in neuroimaging studies observations (a sample). Observations need to be considered as “independently and identically distributed (i.i.d.)” random variables. This hypothesis depends on the empirical design and is often difficult to prove or control entirely (the state of a subject in the scanner can never be the same at the different times when the BOLD signal is measured). There are cases when computing the average of observed values is not correct or not very informative about the population, for example when the distribution of measures is not symmetrical around its mean, like for a Lognormal distribution. In that case, observed for bounded measures like, often, response times, an appropriate summary measure is the average of the logarithm of the measures, because after data transformation the errors follow a Gaussian distribution
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