We’re talking networking.
Yes, we normally cringe at the thought of another schmooze fest at the office, but when it comes to the brain, networking is not only important, but crucial. For any behavior or any cognitive process to occur, multiple areas of our brain must work in concert. These areas may be distant from each other in the brain, but become active at the same time or deactivate together. This pairing of activity may imply that the areas involved are necessary for whatever behavior is taking place. This is called functional connectivity. The areas are connected, not physically, but in terms of what they are used for.
Within the brain, there are countless functionally connected networks designed to carry out the processes of everyday life. What’s surprising though is how we have networks that are active even when we are doing nothing at all, like when we sleep. These networks, called resting state networks, are the least understood. It’s not quite clear what these networks are for. Furthermore, in the case of a mental disorder, such as schizophrenia, these resting state networks seem to be disrupted. They are less connected, or have weaker connections. Clearly, there some importance to these networks.
Here’s where my project comes in. It has been shown that individuals with autism have less functional connectivity at resting state than those without autism. There might be disruptions in the resting state networks. But which networks and which areas are involved in this disruption? In my project, I am analyzing data from fMRI scans of individuals with autism and control participants without autism. During the scans, the participants were instructed to lie still in the scanner, thinking about nothing in particular for 5 minutes. With the data analysis technique I’m using, I’ll be able to determine which of 90 areas in the brain are functionally connected when a participant is at rest. I’ll also be able to visualize the resting state networks of the participants by constructing graphs of the functionally connected areas. With this, I hope to find differences between the autism group and the control group in the graphed networks. The differences may include changes in the strength of certain connections or even discrepancies in which areas are connected.
What’s to be gained here? We already know that functional connectivity is decreased in autism. However, studies like this one will allow us to gain knowledge of the overall properties of the resting state networks of people with autism. Are their networks under-connected, over-connected, or completely different? To know these things will bring us closer to understanding the nature of this disorder, and more importantly, how we can better help.
[This post was originally published at my previous blog, Neurolore.]