fMRI brain study unpacks, predicts sentence-level verbal expression

Researchers have used functional MRI (fMRI) to come up with a way to predict neural patterns produced by words within sentences.

Building on previous studies that focused on single words lighting up parts of the brain, the team correctly anticipated brain-activity patterns at the sentence level to the tune of 70 percent accuracy, on average.

The study report describing the achievement is running in the August edition of Cerebral Cortex.

The team, mostly made up of neurological experts from the University of Rochester (New York) and the Medical College of Wisconsin, had 14 participants read 240 sentences describing everyday situations while undergoing fMRI.

They innovated methods to “decompose” the fMRI data into individual words in order to connect sentence-level fMRI activation patterns to the word-level semantic model. Then they used multiple regression to estimate activation patterns associated with each attribute in the model.

This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences, according to the study abstract.

A related article produced by the University of Rochester’s news office explains that the new semantic model employs 65 attributes. These were developed by surveying participants on the sensory, emotional, social and other aspects for a set of words.

Building on a model created by Jeffrey Binder, MD, of the Medical College of Wisconsin, a co-author in the present study, the researchers had the participants rate the degree to which a given root concept was associated with a particular experience.

In total, 242 unique words were rated with each of the 65 attributes.

“The strength of association of each word and its attributes allowed us to estimate how its meanings would be represented across the brain using fMRI,” says senior author Rajeev Raizada, PhD, MSc, in the article.

Lead author Andrew Anderson, a research fellow in Raizada’s lab, adds that the work may one day help people who struggle to put words together in sentences, including stroke and TBI patients.