title: Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.
abstract: 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.
authors: Brain and Cognitive Sciences, University of Rochester, NY14627, USA.
authors: Andrew James
authors: AJ
authors: Anderson
authors: Medical College of Wisconsin, Department of Neurology, Milwaukee, WI53226, USA.
authors: Jeffrey R
authors: JR
authors: Binder
authors: Medical College of Wisconsin, Department of Neurology, Milwaukee, WI53226, USA.
authors: Leonardo
authors: L
authors: Fernandino
authors: Medical College of Wisconsin, Department of Neurology, Milwaukee, WI53226, USA.
authors: Colin J
authors: CJ
authors: Humphries
authors: Medical College of Wisconsin, Department of Neurology, Milwaukee, WI53226, USA.
authors: Lisa L
authors: LL
authors: Conant
authors: Teledyne Scientific Company, Durham, NC27703, USA.
authors: Mario
authors: M
authors: Aguilar
authors: Brain and Cognitive Sciences, University of Rochester, NY14627, USA.
authors: Xixi
authors: X
authors: Wang
authors: Brain and Cognitive Sciences, University of Rochester, NY14627, USA.
authors: Donias
authors: D
authors: Doko
authors: Brain and Cognitive Sciences, University of Rochester, NY14627, USA.
authors: Rajeev D S
authors: RDS
authors: Raizada
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D009042:Motivation / Q000502:physiology*; D015999:Multivariate Analysis; D010775:Photic Stimulation / Q000379:methods; D011932:Reading; D012660:Semantics; D055815:Young Adult
keywords: concepts; embodiment; lexical semantics; multimodal model; semantic memory