"There are certain patterns of activity across the brain that are consistent with thinking about one category versus another," says McNorgan. "We might think of this as a neural fingerprint."

These MRI patterns were then digitized and used to train a series of computer models to recognize which activity patterns were associated with each category.


"After training, models are given previously unseen activity patterns," he explains. "Significantly above-chance classification accuracy indicates that the models have learned a generalizable relationship between specific brain activity patterns and thinking about a specific category."

To test whether the digital brain models produced by this new method were more realistic, McNorgan gave them "virtual lesions" by disrupting activations in regions known to be important for each of the categories.

He found that the mutually constrained models showed classification errors consistent with the lesion location. For example, lesions to areas thought to be important for representing tools disrupted accuracy for tool patterns, but not the other two categories. By comparison, other versions of models not trained using the new method did not show this behavior.

"The model now suggests how brain areas that might not appear to be important for encoding information when considered individually may be important when it's functioning as part of a larger configuration or network," he says. "Knowing these areas may help us understand why someone who suffered a stroke or other injury is having trouble making these distinctions."

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