As an fMRI practitioner, I just want to point out that this problem has nothing to do with fMRI. It has to do with the weaknesses inherent in the most common methods for designing fMRI experiments, and for analyzing and modeling the data. The SNR in fMRI depends heavily on the level of blood pressure, arousal, attention and other factors that vary day-to-day. Any analysis that doesn't account for that variability will be unreliable.
Methods for designing experiments, analyzing and modeling fMRI data that are robust to this variability are available. The problem is that most people in the field don't use them.
To quantify the value of a method it is useful to consider the amount of information that the method recovers from the data stream, the prediction accuracy of the resulting models, generalization ability outside of the conditions used to fit the model and decoding/reconstruction accuracy. For all four of those criteria, the best approach is to use linearized encoding models that estimate an FIR filter for each voxel separately.
Methods for designing experiments, analyzing and modeling fMRI data that are robust to this variability are available. The problem is that most people in the field don't use them.