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Scanning Your Thoughts
Machines learn to analyze brain activity />

CONTEXT: Can computers learn to read the human mind? Detecting thoughts may be beyond their abilities, but computers can be trained to recognize certain mental tasks from scans that monitor brain activity. One popular scanning technique, functional magnetic resonance imaging (fMRI), already aids the study of learning, memory, emotion, neural disorders, and psychiatric drugs. Using statistics and data analysis, researchers can identify patterns of activity as characteristic of certain mental activities and states. Now, Tom Mitchell and his colleagues at Carnegie Mellon University have shown that computers can automate this process, at least for some simple tasks.

METHODS AND RESULTS: Using fMRI data from subjects engaged in various tasks, the CMU team trained computers to recognize which fMRI patterns accompanied cognitive states for different tasks. During this process, the computer developed mathematical models to distinguish between different cognitive states. Then, given new fMRI data, the computers predicted the subjects’ mental states from the brain scans. Though imperfect, the automatically trained computers convincingly outperformed chance in discriminating whether a subject was looking at sentences or pictures, reading ambiguous or nonambiguous sentences, and reading words associated with different categories such as people, tools, or fruit.

WHY IT MATTERS: This work shows that a computer can use the results from one set of brain scans to predict what a brain was doing during other scans. This capability could eventually lead to more accurate use of MRI scans in medicine. It might also speed up data analysis, particularly when one individual is being studied over time. And, since the computers learned to recognize brain activity from a single short interval rather than a composite of several scans over a longer time period, it might reduce the time each patient spends in an MRI machine, making expensive equipment more readily available.

More broadly, this work is an important application in the field of machine learning. With relatively few training examples, the computers were able to detect meaningful patterns in data containing thousands of inputs, many of them irrelevant or inaccurate. As scientists collect ever more detailed data sets from the brain and other complex systems, these techniques proffer a way to use the information more effectively.

SOURCE: Mitchell, T. M., et al. 2004. Learning to decode cognitive states from brain images. Machine Learning 57:145-175.

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