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Watching a Single Thought Form in the Brain
Remember the film, Minority Report ? In it, police of the future can anticipate the workings of the criminal mind, and arrest a bad guy when a crime is just a gleam in his evil eye. Just science fiction? Read on, from MIT's prestigious Technology Review magazine:
One of the long-term goals of the field of neuro-imaging is to understand what a person is thinking just by looking at the pattern of his or her brain activity--in essence, reading the mind. While that feat is still a long way off, scientists at the University of New Mexico have taken an important step by refining neuro-imaging techniques to the point where they can reliably detect a single thought forming in an individual's brain.The technique could be used to improve clinical applications of neuro-imaging, such as patient diagnosis, or to study cognitive processes that are fleeting or irreproducible, such as learning a new skill. "This could open up a whole new dimension of how fMRI could be used," says Peter Bandettini, director of the fMRI core facility at the National Institutes of Health in Bethesda, MD.
Functional magnetic resonance imaging (fMRI) measures the amount of blood flow to different parts of the brain, thereby indicating which brain areas are most active. Imaging the brain while someone performs mental tasks, such as remembering words or doing math, gives insight into the parts of the brain crucial for these cognitive processes.
But brain activity is very "noisy," meaning scientists must distinguish the relevant neural signals from background activity, which might come from a subject's breathing, moving, or even daydreaming. To detect brain activity associated with a specific task, then, most fMRI studies average brain scans from repeated tests in dozens of people.
Stefan Posse and colleagues at the University of New Mexico are developing new ways to collect and analyze fMRI data that allow them to detect brain activity from a single thought. They've created their highly sensitive imaging methods by taking more pictures in a shorter amount of time and by developing new algorithms to integrate those images and to reduce background noise.
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