News Release

Predicting a person's distinct brain connectivity

Peer-Reviewed Publication

American Association for the Advancement of Science (AAAS)

Based on functional magnetic resonance imaging of a person's brain when that individual is resting, a new model reported in this study is able to predict how that individual's brain will look during a range of active tasks. Such a tool, if applicable beyond the healthy population upon which this model is based, could be used to investigate functional brain regions in people who cannot perform tasks, such as paralyzed patients or infants, authors of this study say. Previous studies suggest that functional magnetic resonance imaging (fMRI) data on a person's brain activity in a task-free (or resting) state - in which they can think or reflect without being engaged in an external task - can reveal aspects of how the brain will behave during active tasks. However, studies have only explored this at the group level, and not for the individual. The ability to carry out a mapping in individual subjects has been a promising but unfulfilled aspect of resting-state fMRI. To determine if the resting state could be used to predict differences of brain connectivity between individuals, Ido Tavor and colleagues used data from 98 individuals in the Human Connectome Project (HCP) database. The data capture their brain activity at a resting state, as well as during a variety of different tasks categorized across seven different behavioral domains, such as decision-making (gambling) and language interpretation (reading). Using this data, the researchers trained a model that could accurately predict individual task variations across all behavioral domains based on the person's brain activity at a resting state. Overlapping predicted patterns with actual patterns confirmed the model's accuracy. The current study is novel in that it goes beyond previous group-level studies, suggesting that resting-state fMRI patterns provide considerable information for estimating individual differences in task activation.


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