Brain network discovery in medical research
Scientists discovered some time ago that they could use magnetic resonance imaging (MRI) to map brain activity when a person was repetitively performing a task, like looking at flashing lights or doing serial maths calculations. More recently, they found that these MRI signals could also provide useful information when the scanned subject was not doing anything. They called these signals “resting state” signals.
“Resting state” signals come from consistent areas of the brain that are co-activated by tasks, suggesting a functional organization. Much information about brain function could be quickly extracted with little input from the patient. The potential for use as a diagnostic and therapeutic monitoring technique, as well as a research tool, is huge.
However, the data consist of time series obtained from multiple “voxels” in the brain, meaning that there are millions of data points with both spatial and temporal components. Extracting coherent information from this mass of data is a big statistical and computational challenge.
Our new analysis approach for resting state data uniquely provides repeatable, reliable results from single scanning sessions. We are developing methods for network comparison and are applying our new approaches across a targeted range of patient data.