some SfN reflections

I'm now back from SfN and sorting through my notes. It was great to see familiar people again, and to meet some people I only knew from email and articles.

I thought I'd share here a few of my MVPA-related impressions, in no particular order. These are of course personal and no claim to be representative; please share if you found something different or think I missed a trend.

• RSA and searchlight analysis are quite popular. I can't remember seeing an analysis using only ROI-based MVPA. I saw several analyses combining searchlight and RSA (e.g. searching the brain for spheres with a particular RSA pattern).
• Linear classifiers (mostly svms) and Pearson correlation are very widely used. I saw a few nonlinear svms, but not many. Some posters included diagrams illustrating a linear svm, while others simply mentioned using MVPA, with the use of a linear svm being implicit.
• Feature selection ("voxel picking") is a major concern. Multiple people mentioned having no real idea which methods should be considered, much less knowing a principled, a priori way to choose a method for any particular analysis. This concern probably feeds into the popularity of searchlight methods.
• I saw quite a few efforts to relate (e.g. correlate) classification results with behavioral results and/or subject characteristics.
• Multiple studies did feature selection by choosing the X most active voxels (as determined by a univariate test on the BOLD), within the whole brain or particular regions.

I'll share some detailed thoughts and explanations of a few of the methods I ran across; hopefully sooner rather than later ... and I never did hear what the conference logo was supposed to be. It certainly was ubiquitous, though.