Reference number : GC-2017-Q1-02590, amendment number : , agreement type : G, recipient legal name : Newman, Aaron (Dalhousie University), recipient country : CA, recipient province : nan, recipient city : Nova Scotia|Nouvelle-Écosse, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : As neuroimaging research evolves, we are beginning to move from the study of average patterns of brain activity to understanding the sources of inter-individual variance. Over the past granting period my trainees and I have identified relationships between individual differences in neural signatures of lexical and grammatical processing (the N400 and P600, measured using EEG and MEG) and linguistic, cognitive, and demographic variables. Our findings indicate that scalp-recorded brain activity is much more sensitive to fine-grained individual differences than previously thought&mdash;including factors such as socioeconomic status and reading habits. In a second, related theme of our work, we have begun to record brain activity from people engaged in free conversation (rather than more typical tasks such as during receptive language processing, or simple, controlled production of single words). Having validated this approach, we are poised to enter a new era of neuroimaging research that holds great promise both in terms of studying language under more naturalistic conditions, and developing a database (corpus) containing both natural language data and associated brain activity. This work has necessitated the development and application of novel statistical approaches to analyzing complex, 4-dimensional neuroimaging data and relating it to a large number of predictive variables, including machine learning techniques. 
 
The overarching goal of our work in the next granting period will be to develop predictive models of neural responses during language processing , that allow us to accurately predict brain activity patterns based on a combination of objective linguistic, cognitive, and demographic measures, and preceding language context. Thus rather than assuming everyone will respond with the same pattern of brain activity, we attempt to model &mdash; and therefore understand &mdash; the factors that drive individual differences around the average pattern of activity. This holds the promise of a much richer understanding of how language is proceed in the brain, and ultimately insights that will guide individualized training (e.g., second language learning) and therapy (e.g., speech-language therapy) through our industrially-partnered R&amp;D efforts., program purpose french : As neuroimaging research evolves, we are beginning to move from the study of average patterns of brain activity to understanding the sources of inter-individual variance. Over the past granting period my trainees and I have identified relationships between individual differences in neural signatures of lexical and grammatical processing (the N400 and P600, measured using EEG and MEG) and linguistic, cognitive, and demographic variables. Our findings indicate that scalp-recorded brain activity is much more sensitive to fine-grained individual differences than previously thought&mdash;including factors such as socioeconomic status and reading habits. In a second, related theme of our work, we have begun to record brain activity from people engaged in free conversation (rather than more typical tasks such as during receptive language processing, or simple, controlled production of single words). Having validated this approach, we are poised to enter a new era of neuroimaging research that holds great promise both in terms of studying language under more naturalistic conditions, and developing a database (corpus) containing both natural language data and associated brain activity. This work has necessitated the development and application of novel statistical approaches to analyzing complex, 4-dimensional neuroimaging data and relating it to a large number of predictive variables, including machine learning techniques. 
 
The overarching goal of our work in the next granting period will be to develop predictive models of neural responses during language processing , that allow us to accurately predict brain activity patterns based on a combination of objective linguistic, cognitive, and demographic measures, and preceding language context. Thus rather than assuming everyone will respond with the same pattern of brain activity, we attempt to model &mdash; and therefore understand &mdash; the factors that drive individual differences around the average pattern of activity. This holds the promise of a much richer understanding of how language is proceed in the brain, and ultimately insights that will guide individualized training (e.g., second language learning) and therapy (e.g., speech-language therapy) through our industrially-partnered R&amp;D efforts., agreement title english : Predictive modelling of neuroimaging measures of language processing, agreement title french : Predictive modelling of neuroimaging measures of language processing, agreement number : RGPIN, agreement value : 140000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada