Wednesday, February 22, 2017

a methodological tour de force: "The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis"

"The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis" by Gardumi et al. (full citation below) is an impressive methodological tour de force: comprehensive analyses clearly described (even their group-level permutation scheme!). Some of its themes are similar to those by Coutanche, Solomon, & Thompson-Schill (2016) which I posted about yesterday: understanding the spatial scale of fMRI information.

The approach in Gardumi et al. (2016) is different than that of Coutanche et al. (2016): they started with 7T images acquired with 1.1 mm isotropic voxels, then reconstructed the images at 2.2 and 3.3 mm effective resolution as well, by zero-padding the k-space images, as illustrated in their Figure 1, below.
A neat part of this approach is that the resulting images have the same voxel size, but lower effective resolution, making it possible to directly compare analyses including the same number of voxels (which is good, since MVPA performance generally interacts with the number of voxels). Changing the effective resolution this way also avoids the issues related to differences between acquiring 1.1 and 3.3 mm voxels (e.g., movement sensitivity): only a single scanning session was used for each person.

Another interesting aspect is that they had two classification tasks: decoding the speaker or the spoken vowel (see paper for details; they used auditory stimuli and single-subject-defined auditory anatomical ROIs). One of their results summary figures is below (lots more detail in the paper!), showing group-level average accuracy for the two classifications at each effective resolution. As an aside, the x-axis is the number of voxels included, picked from univariate tests (n most active voxels from training set GLM): accuracy increased for both until around 1000 voxels were included, then leveled off (again, see the paper for details), which matches my general experience of plateauing performance (e.g.).

Anyway, Figure 6 (plus other tests that they describe) shows that smaller voxels generally did better for their vowel decoding classification, but not for speaker decoding. In the discussion Gardumi et al. (2016) ties this to previous literature findings "that informative voxels in the auditory cortex are widely distributed for vowel decoding, while more clustered for speaker decoding."

Yesterday I wrote that I'm not "convinced that it's safe to infer information spatial resolution from voxel resolution" ... am I convinced by Gardumi e al.? Yes, I think so. Below is my cartoon for how it could work. The blue squares are the brain region, the white circles informative parts, and the red squares voxels at two different sizes. Suppose that you need around a quarter of the voxel to be informative for its activity to be biased (and so contribute to a classification): this is much easier to obtain with small voxels than large ones if the informative parts are widely distributed (left), but about as easy to obtain with both small and large voxels if the informative parts are clustered (right).

So, I now am thinking that it can sometimes be valid to make inferences about the spatial distribution of information from comparisons across voxel resolutions. The way in which the different voxel resolutions are obtained strikes me as very important: I have a lot more reservations about inferences when the resolutions are generated by different acquisition sequences than by k-space zeroing. And perhaps some of my change of heart is due to the different mental models I have of "widely distributed" or "clustered" information as opposed to "coarse" or "fine-grained" spatial resolution. Both of my cartoons above have 10 informative bits (circles): would you describe the one on left as fine-grained and the one on the right as coarse-grained? Gardumi A, Ivanov D, Hausfeld L, Valente G, Formisano E, & Uluda─č K (2016). The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis. NeuroImage, 132, 32-42 PMID: 26899782

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