In neuroscience research, we spend a lot of time trying to make sense of brain images. I spend a good part of most of my days answering the question, “what space am I in?” That’s the cool-kid lingo for talking about what kind of image you are working with. There’s “native space”, “corrected space”, “standard space”, “surface space”, “anatomical space”, “functional space”, …, the list goes on and on. For most students that work with brain imaging data, figuring out how to think clearly about all these spaces is difficult, headache-inducing work. But over time you begin to build an intuition about how to deal with spaces, and soon you are moving effortlessly between them.
An image space consists of dimensions broken up into units of size. Within that space, the image itself is composed by adding pixel intensity at the location of each unit. Therefore, an anatomical brain image might be a volume consisting of 3-dimensions.
We can make different types of tissue have different brightness by being clever with our measurement techniques. For example, the anatomical image is sensitive to differences in the fat and water content of the tissue. Other images may be more sensitive to certain kinds of damage, or blood flow, or oxygen content, or many other things.
One of the trickiest bits of analyzing these images is getting all these spaces to line up. For instance, you might be interested in seeing where some brain activity is occurring. Images that are sensitive to brain function are not very sensitive to brain anatomy, making finding the specific location of the activity difficult.
To know exactly where the activity is occurring in an individual, we need to acquire a good anatomical image along with the functional image, and then try our best to line up the two. Another common challenge is that researchers want to analyze multiple research subjects so that our statistics are valid and generalizable to the population. But people have different- looking brains! We need to find some good way of lining up the images of everyone in our sample. Finally, we want our results to have some correspondence to other results in our field of study, so we need some…