This is a prime example of what I described. The problem is not with fMRI as a tool, but with what statistical modeling assumptions are made when making inferences. In this case, it's about statistical thresholds and how to avoid false positives--something that is a concern for any experiment, not just one that using an fMRI. To make this clear, here's the discussion:
"Can we conclude from this data that the salmon is engaging in the perspective-taking task? Certainly not. What we can determine is that random noise in the EPI timeseries may yield spurious results if multiple comparisons are not controlled for. Adaptive methods for controlling the FDR and FWER are excellent options and are widely available in all major fMRI analysis packages. We argue that relying on standard statistical thresholds (p < 0.001) and low minimum cluster sizes (k > 8) is an ineffective control for multiple comparisons. We further argue that the vast majority of fMRI studies should be utilizing multiple comparisons correction as standard practice in the computation of their statistics."
The authors don't cast doubt on the entire field of fMRI, just in what conclusions we can draw from certain statistic tests of high dimensional data in the presence of noise...