Over the past ten years a good deal has been learned from fMRI studies about the spatial patterns of neural activation used by the human brain to represent meanings of words and concepts. Much less is understood about the time evolution of this neural activity, including the temporally interrelated sub-processes the brain employs during the hundreds of milliseconds it takes to comprehend a single word, or the more complex processes it uses to construct and encode meaning of entire sentences as the words arrive one by one. We propose research to study, and to build computational models of, the detailed spatial and temporal neural activity observed during the comprehension of single words, phrases, sentences, and stories. This proposed research will specifically target the following questions: """"""""What information is encoded by neural activity where and when, and by which subprocesses in the brain, during the time it takes to comprehend a single word in isolation?"""""""" """"""""What is the flow of information encoded when a newly sensed word first activates sensory cortex, then later results in neural activation encoding the word meaning?"""""""" """"""""How does the brain integrate a newly encountered word in the context of earlier words in the sentence or phrase, to compose the meaning representation of the multi-word phrase or sentence?"""""""" and """"""""How do semantic expectations and demands, together with syntactic sentence structure alter the processing of words, compared to processing the same words in isolation, or as an unstructured set such as {kick, Joe, ball}?"""""""" To study these questions we will (1) devise novel experimental protocols to probe the flow of information encoded in neural signals during word and sentence processing, (2) collect new brain image data using both fMRI to achieve spatial resolution of a few millimeters, and MEG to achieve temporal resolution of a few milliseconds, (3) develop and apply novel machine learning approaches to build computational models that integrate and that predict this combined experimental data. Our goal is to develop an increasingly accurate computational model of how the brain comprehends words, phrases and sentences - a model that makes testable predictions about the neural activity observed in response to novel language stimuli. Intellectual Merit: This collaborative research brings together advanced machine learning algorithms with novel experimental protocols for MEG and fMRI brain imaging to advance our understanding of two fundamental open questions about the human brain: how does the brain represent meaning, and what neuro-cognitive processes construct that meaning piece-by-piece from perceived language stimuli? Broader Impacts: If successful, this research will impact a broad range of communities, including (1) cognitive neuroscience and computational linguistics, providing improved understanding of language processing in the brain, (2) machine learning, by driving the development of new methods for time series and latent variable analysis, integrating multiple data sets, and incorporating diverse background knowledge as priors, (3) clinical studies of brain pathologies, especially those related to language processing, and informing treatment strategies for developmental and acquired language disorders (4) education of graduates, undergraduates and the general public, through dissemination of technical articles, teaching materials, and news about our work in the public press.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-IFCN-B (55))
Program Officer
Miller, Brett
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Carnegie-Mellon University
Schools of Arts and Sciences
United States
Zip Code
Papalexakis, Evangelos E; Faloutsos, Christos; Mitchell, Tom M et al. (2016) Turbo-SMT: Parallel Coupled Sparse Matrix-Tensor Factorizations and Applications. Stat Anal Data Min 9:269-290
Fyshe, Alona; Talukdar, Partha P; Murphy, Brian et al. (2014) Interpretable Semantic Vectors from a Joint Model of Brain- and Text-Based Meaning. Proc Conf Assoc Comput Linguist Meet 2014:489-499
Wehbe, Leila; Murphy, Brian; Talukdar, Partha et al. (2014) Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses. PLoS One 9:e112575
Kujala, Jan; Sudre, Gustavo; Vartiainen, Johanna et al. (2014) Multivariate analysis of correlation between electrophysiological and hemodynamic responses during cognitive processing. Neuroimage 92:207-16
Papalexakis, Evangelos E; Faloutsos, Christos; Mitchell, Tom M et al. (2014) Turbo-SMT: Accelerating Coupled Sparse Matrix-Tensor Factorizations by 200×. Proc SIAM Int Conf Data Min 2014:118-126
Sudre, Gustavo; Pomerleau, Dean; Palatucci, Mark et al. (2012) Tracking neural coding of perceptual and semantic features of concrete nouns. Neuroimage 62:451-63