Algorithms for BRAIN -- Human Connectome Project

The BRAIN Initiative was announced this week, and it has been really exciting for people like me already trying to map a nervous system. I've been trying to figure out what to do over the next year to finish my thesis, and the algorithmic side of this project is really the area that needs the most attention. The big challenge will be to create something that not only helps with my thesis project of mapping the neurons in the leech ganglion, but also provides a useful tool for other large-scale brain mapping projects.

The human connectome project comes to mind as a very similar problem from the algorithmic side. This project is at the macro-scale for connectomics -- attempting to map the connectivity of different brain regions using MRI, fMRI, DTI etc. There are several challenges to overcome from the vast amount of data that is being collected, and the algorithms that will help to solve my problems could also be useful in this context. Everyone understands that getting maps of neural activity will be extremely important in understanding how the brain works, but they don't realize that once you have that data, there is a long way to making any sense of it.

Part of the problem with the human connectome project is the problem of registration. Registration is exactly my thesis project -- identification of all of the pieces. In my case I'm trying to identify individual cells across different animals. In the human connectome project, the challenge is to identify individual brain regions. These regions must be consistent and somehow identifiable across individuals. In the fMRI world, typically a brain is mapped on to the "canonical" brain, which is essentially just some ladies brain. The prominent features are analyzed by humans and these features are used to morph individuals onto a single brain-space. The human connectome project is expanding upon this by adding in all the DTI connections, and some fMRI response data to help identify different brain regions.

The feature abstraction that we have started is going to be a useful way of putting these different data sets in the same space so that registration algorithms can work universally. In HCP they get function and anatomical data as well as connectivity data across different brain regions, but like in the leech the same brain region may not be in exactly the same spot etc. But, across animals the same brain regions should have similar functional properties and similar connectivity profiles. By taking all of these as abstract features and then doing registration analysis on this feature space, there may be a consistent tool that can tackle both of these problems and more.

A way of looking at connectomics is based on graphs, which has an extensive mathematical/theoretical background/tools. Graphs are made up of nodes and edges. The connectome is specifying all of the edges -- which nodes are connected. The challenge is to figure out how many nodes there are, and how to match nodes across different individuals/organisms.