Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation. Cereb Cortex 2017 Sep 01;27(9):4379-4395
Date
08/16/2016Pubmed ID
27522069DOI
10.1093/cercor/bhw240Scopus ID
2-s2.0-85032015905 (requires institutional sign-in at Scopus site) 55 CitationsAbstract
We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.
Author List
Anderson AJ, Binder JR, Fernandino L, Humphries CJ, Conant LL, Aguilar M, Wang X, Doko D, Raizada RDSAuthors
Andrew J. Anderson PhD Assistant Professor in the Neurology department at Medical College of WisconsinJeffrey R. Binder MD Professor in the Neurology department at Medical College of Wisconsin
Leonardo Fernandino PhD Assistant Professor in the Neurology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AdultBrain
Brain Mapping
Female
Humans
Magnetic Resonance Imaging
Male
Middle Aged
Motivation
Multivariate Analysis
Photic Stimulation
Reading
Semantics
Young Adult
