Paying attention? Imaging tests provide the answer

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Colored balls represent connections in neural networks involved in attention. Orange balls represent high number of connections in high attention networks, blue have more connections in low attention networks. Gray have roughly the same connections in both networks. (Illustration by Chun Laboratory / Yale University)

To “height,” “time,” “speed,” and “intelligence” add “attention” to the long list of things that can be measured objectively. Yale University researchers used brain-imaging data to “score” a person’s ability to perform tasks for extended periods of time, identifying a neuromarker that may help predict performance at school or tasks requiring long periods of focus.

“Attention is complicated and important in so many facets of our lives,” said Yale’s Monica D. Rosenberg, a graduate student in psychology and co-lead author of the study that appeared online Nov. 23 in the journal Nature Neuroscience. “We present a new way to compare differences among people and individuals over time. A comparison I use is: It is like how gross domestic product is used to measure economic health.”

Rosenberg and co-lead author, Emily Finn, working in the labs of Marvin Chun and Todd Constable, analyzed fMRI images of individuals who had undertaken laborious tasks of 30 minutes or longer and identified brain networks associated with performance. Imaging data of these networks predicted how subjects would perform on future tests of sustained attention even when they were not involved in any tasks at all. The model also predicted from a separate data set the symptoms of children and adolescents diagnosed with attention deficit disorder.

“We could use this measure in any number of ways, perhaps predicting which children might have future trouble in school and developing individualized training for them,” Rosenberg said.

She also noted, however, that the attention test does not predict intelligence.

“We do hope that in the future this data-driven, functional connectivity approach can help us predict a wide range of cognitive abilities and clinical symptoms,” she said.

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