Grant: Discovery Grants Program - Individual
Dalhousie University
$56,000.00
Predictive modelling of neuroimaging measures of language processing
FY 2024-25
Summary
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 thoughtincluding 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 and therefore understand 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&D efforts.
Program
Discovery Grants Program - Individual
Recipient
Location
Nova Scotia, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2017-05340
Name nom
Newman, Aaron
Department département
Psychology & Neuroscience
Organization id
79
Institution établissement
Dalhousie University
Province en
Nova Scotia
Province fr
Nouvelle-Écosse
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2017
Award amount
56000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1502
Committee name en
Biological Systems and Functions
Committee name fr
Systèmes et fonctions biologiques
Area of application code
1200
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Advancement of knowledge
Area of application fr
Avancement des connaissances
Research subject code
6306
Research subject group en
Psychology
Research subject group fr
Psychologie
Research subject en
Cognitive science -- language
Research subject fr
Science cognitive -- langage
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Predictive modelling of neuroimaging measures of language processing
Keywords
language; cognitive neuroscience; eeg; fmri; meg; bilingualism; individual differences; statistical modelling
Application summary
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 thoughtincluding 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 and therefore understand 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&D efforts.