Cohen and his colleagues are helping to shape a newly evolving discipline called cognitive neuroscience. In recent work, they have exploited technology developed during the 1980s that many scientists believe will revolutionize study of the brain. Imaging technology, such as magnetic resonance imaging (MRI) and other techniques, combined with computing power makes it possible, in effect, to peel away the bone and membrane surrounding the brain. Without even touching their human subject, researchers can see what happens inside a living, thinking brain, and they can identify what parts of this intricate, complexly folded, interconnected mass of tissue "light up" during mental activities.
Cohen and colleagues use a technique known as functional MRI to record a view of the functioning brain that is among the most detailed yet reported. While other brain-mapping techniques give what resembles a satellite view of the world, in which cities can be seen and identified, the Pitt/CMU researchers can see streets. With functional MRI, they can map the sites of brain activity to a resolution as fine as one millimeter, comparable to mapping a football field in six-inch units.
Cohen and his colleagues used a conventional MRI machine, like those that became available in many hospitals during the 80s, a big advantage since the research can be accomplished without major new investment in technology. Functional MRI works on the principle that when brain cells (neurons) become active, blood flows to them, and the MRI scanner registers increased oxygen in the area. Because MRI machines used in this way detect changes resulting from biological function, the method got its name.
The technique generates large amounts of data quickly -- a great advantage, says Cohen, and a problem. "It gives a lot of information to work with, but likewise it's a tremendous amount of data to process -- as much as half a gigabyte per experiment." To deal with the data overload, Cohen and his colleagues turned to the Pittsburgh Supercomputing Center's Alpha Cluster, a linked network of 14 DEC Alpha workstations. They used the cluster to address a particular problem of their functional MRI experiments. A human subject stays in the machine for two to three hours as the MRI scanner records data. Though special pillows are used to reduce movement, it's impossible to keep the head perfectly still. Software called automatic image registration (AIR), developed by Roger Wood of UCLA, can correct for head movement, but the sheer number of images -- typically 1,200 per experiment -- creates an imposing demand on computing.
AIR is an ideal application for the Alpha Cluster, notes Cohen, because it is inherently parallel. A single experiment typically records 200 separate images for each of 6 separate scan sites, or slices. "For each slice," says Cohen, "you take as a reference point one of the 200 images and align all the others to it. There's no need for communication back and forth. Each sample can be aligned to the reference independently."
On a high-speed workstation, says Cohen, it took as much as 24 hours computing time to register the images from one experiment. "Often, we run two experiments in an evening, which means the computing can't keep up with the data, and this cripples the research." On the Alpha Cluster, the same computing takes an hour, a radical speedup that overcomes the research bottleneck.go back to the main screen