Friday, September 11, 2015

a nice "Primer on Pattern-Based Approaches to fMRI"

John-Dylan Haynes has a new MVPA review article out in Neuron, "A Primer on Pattern-Based Approaches to fMRI" (full citation below). I say "new" because he (and Rees) was the author of one of the first MVPA overview articles ("Decoding mental states from brain activity in humans"), back in 2006. As with the earlier article, this is a good introduction to some of the main methods and issues; I'll highlight a few things here, in no particular order.

At left is part of Figure 4, which nicely illustrates four different "temporal selection" options for event-related designs. The red and green indicate the two different classes of stimuli (cats and dogs), with the black line the fMRI signal in a single voxel across time.

I often refer to these as "temporal compression" options, but like "temporal selection" even better: we are "selecting" how to represent the temporal aspect of the data, and "compression" isn't necessarily involved.

John-Dylan Haynes (quite properly, in my opinion) recommends permutation tests (over binomial and t-tests) for estimating significance, noting that one of their (many) benefits is in detecting biases or errors in the analysis procedure.

Overall, I agree with his discussion of spatial selection methods (Figure 3 is a nice summary), and was intrigued by the mention of wavelet pyramids as a form of spatial filtering. I like the idea of spatial filtering to quantify the spatial scale at which information is present, but haven't run across a straightforward implementation (other than searchlight analysis); wavelet pyramids might need more investigation.

I also appreciate his caution against trying too many different classifiers and analysis procedures on the same dataset, pointing out that this can cause "circularity and overfitting". This problem is also sometimes referred to as having too many experimenter degrees of freedom: if you try enough versions of an analysis you can probably find one that's "significant." His advice to use nested cross-validation strategies (e.g., tuning the analysis parameters on a subset of the subjects) is solid, if sometimes difficult in practice because of the limited number of available subjects. The advice to use an analysis that "substantially decreases the number of free parameters" is also solid, if somewhat unsatisfying: I often advise people to use linear SVM, c=1 as the default analysis. While tuning parameters and testing other classifiers could conceivably lead to a higher accuracy, the risk of false positives from exploiting experimenter degrees of freedom is very real.

I also like his inclusion of RSA and encoding methods in this "primer". Too often we think of classifier-based MVPA, RSA, and encoding methods as unrelated techniques, but it's probably more accurate to think of them as "cousins," or falling along a continuum.

It's clear by now that I generally agree with his issue framing and discussion, though I do have a few minor quibbles, such as his description of MVPA methods as aimed at directly studying "the link between mental representations and corresponding multivoxel fMRI activity patterns". I'd assert that MVPA are also useful as a proxy for regional information content, even without explicit investigation of cognitive encoding patterns. But these are minor differences; I encourage you to take a look at this solid review article. Haynes JD (2015). A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. Neuron, 87 (2), 257-70 PMID: 26182413


  1. I was just reading through a bunch of Machine Learning Intros for fMRI analysis, and I must Haynes' is - although maybe not that advanced - one of the best. Very clear, readable though packed with useful information and CLEAN. Unfortunately I have come accross a couple which were not without imprecise terminology or even confused presentation of concepts

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