Hot, Hot, HOT: Blog Dedicated to MVPA

In my whole world-wide wandering, I have concluded that nobody really knows what multivoxel pattern analysis (MVPA) is. For example, a couple of days ago I asked the checkout cashier at Target what MVPA was; he looked at me like some loutish knight beriddled by a troll.

I had nearly given up trying to understand what it was, until I stumbled upon this blog dedicated to vivisecting MVPA so that you can peer into the inner workings of this gruesome monstrosity. After having read this blog for the better part of an afternoon, I have concluded that it is able to make your wildest dreams come true and can answer any questions you could possibly have about MVPA, including discussions on the effect of different experiment parameters on classification accuracy, what a searchlight algorithm is, and what MVPA detects, exactly. It also includes, as far as I can tell, the only convincing argument in favor of allowing people to keep midgets as pets.

The author is a far more dedicated instructor than I am, with large swaths of R code accompanying most of the tutorials so that you can get a better feel for what's going on. I give this baby a 10/10.

Repost: Why Blogs Fail

Pictured: One of the reasons why blogs fail

Blogger Neuroskeptic recently wrote a post about why blogs fail, observing what divides successful from unsuccessful blogs, and why the less successful ones ultimately stop generating new content or disappear altogether. Reading this made me reflect on why I started this in the first place; initially it was to write down, in blog-form, what I saw and heard at an AFNI workshop earlier this spring. Since then, I tried to give it a more coherent theme, by touching upon some fMRI methodology topics that don't get as much attention as they deserve, and creating a few walkthroughs to help new students in the field get off the ground, as there isn't much out there in the way of interactive videos showing you how to analyze stuff.

Analyzing stuff, in my experience, can be one of the most intimidating and paralyzing experiences of a graduate student's career; not because of laziness or incompetence, but because it is difficult to know where to start. Some of the obstacles that hinder the analysis of stuff (e.g., common mistakes involving experiment timing, artifacts that can propagate through an entire data set, not having a method for solving and debugging scripting errors) do not need to be repeated by each generation, and my main objective is to point out where these things can happen, and what to do about them. Of course, this blog contains other topics related to my hobbies, but overall, the theme of how to analyze stuff predominates.

In light of all of this, here is my pact with my readers: This blog will be updated at least once a week (barring any extreme circumstances, such as family emergencies, religious observations, or hospitalization from Nutella overdose); new materials, such as walkthroughs, programs, and instructional videos, will be generated on a regular basis; and I will respond to any (serious) questions posted in the comments section. After all, the reason I take any time out of my busy, Nutella-filled day to write any of this content is because I find it interesting and useful, and hope that somebody else will, too.