Friday, 4 April 2014

Does it matter that we do not know the link between neuronal signals and the BOLD response?

In recent weeks there have been a couple of articles that have revisited the question of the importance (or not) of the results of fMRI studies ( and For me the importance of fMRI to address any research question is dependent on the degree to which the hypothesis requires specific predictions about the underlying neuronal signals. In general, the more any hypothesis is dependent upon specific neuronal parameters the less convincing the results and conclusions of that study will be. Currently, we still do not know with any confidence how the BOLD signal in humans is modulated by neuronal firing rate and/or by modulations in the amplitude of the local field potentials at different frequencies. In addition, we have virtually no data that addresses how any relationships between the neuronal measures and the BOLD signal might differ in different brain regions. Indeed Harris et al. (2011) wrote “In particular, BOLD signals need not directly report spiking activity in the imaged area, but instead reﬂect the many factors associated with neural activity that lead to an increase in blood ﬂow. Most importantly, neurotransmitters released during synaptic activation are now known to directly inﬂuence local blood ﬂow and it is thought that the BOLD signal may most closely reﬂect the excitatory synaptic component, rather than the action potential component, of neural activity”. Therefore, it would seem that it would be prudent to reduce the weight given to fMRI results that purport to reflect changes to specific parameters of the underlying neuronal signal. A good case study that demonstrates the difficulty in relating neuronal signal modulations to BOLD signal modulations is the history of using fMRI to investigate the presence (or not) of mirror neurons in humans. Which is still unresolved.

However, not all fMRI experiments have hypotheses that are based on specific predictions about the underlying neuronal signals. Indeed, it is interesting to note that the examples given in support of fMRI research by Matt Wall ( are examples of such research. Here, the fMRI signal is employed as a biomarker, without any attempt to explain or link any modulations in the BOLD signal to specific neuronal parameters. So, does it matter that we do not know the link between neuronal signals and the BOLD response? I would say – it depends. It depends on whether your hypothesis makes specific predictions about the underlying neuronal signal or not. If it does then it clearly does matter that the link between neuronal signals and the BOLD response is not known. If not, then it does not matter.

What ever your thoughts this paper is well worth a read.

Harris JJ, Reynell C, Attwell D. (2011) The physiology of developmental changes in BOLD functional imaging signals. Dev Cogn Neurosci. 1(3):199-216.

Tuesday, 18 February 2014

A note of caution when selecting ROIs from orthogonal contrasts

In neuroimaging, fMRI, EEG and MEG, it is quite common to circumvent the requirement to correct for multiple comparisons by reducing the multidimensional data to a single value per subject per condition by selecting a region/electrodes/time window of interest. Previously, I have shown that the results of statistical tests are biased if this is based on where any effect is maximal ( However, it is common practise to select these region/electrodes/time window of interest from an orthogonal contrast to the one of interest in a factorial design. In other words, if we have a 2X2 design with factors A and B we might select our region/electrodes/time window of interest where the main effect has the highest statistical difference and then test whether the interaction is significant, averaging across this region/electrodes/time window of interest. However, is this biased? The answer is maybe.  What follows is covered in "Circular analysis in systems neuroscience: the dangers of double dipping" by Kriegeskorte et al. (2009)

To test this I simulated data from the 4 cells of a 2X2 factorial design, A1, A2 B1 and B2, such that the data for each trial and each subject and each time point was drawn from the same normally distributed random population. I then selected either the time point where either (i) the main effect of A vs B was maximal or (ii) randomly selected a time point. Then for this time point I tested whether the interaction between A and B was significant. This was repeated 500 times and I calculated the percentage of false positives. At a significance level of 0.05 we should expect false positives 5% of the time. This is precisely what was produced. Irrespective of how the time point was selected the percentage of false positives was 5% (see left hand panel of the figure below). Therefore, choosing a region/electrodes/time window of interest from an orthogonal contrast is not biased? Well the answer is yes but only if all the cells have the same variance. If I rerun the same simulation but now I reduce the variance of A1 by a factor of 10, keeping the means of all the cells the same, equal to zero, I get a very different result. Now the statistical test for the interaction is biased with over twice as many false positives as predicted (see right hand panel of figure below)

This simulation assumed that cell A1 had a different variance to cells A2, B1 and B2. If I now rerun the simulation but now assume that all cells have unequal variances by dividing the variance of A1 by 1, A2 by 2, B1 by 3 and B2 by 4 then the proportion of false positives rises above 50%.


Thursday, 26 September 2013

A word of caution about testing for U-shaped functions


Thursday, 25 July 2013

Further thoughts on pre-registration




Friday, 5 July 2013

Some Thoughts on Pre-registration
