Combining artificial neural network models and fMRI to study brain and language

Project Details


DESCRIPTION (provided by applicant): The overall goal of this project is to supplement the applicant's existing training in functional magnetic resonance imaging (fMRI) with additional training in artificial neural network (ANN) modeling. In general, ANN models have been used to provide mechanistic accounts of behavior, while fMRI has been used to test for neural activation differences corresponding to differences in task conditions. To date there have been few efforts to combine the two approaches. Successfully doing so will enable an additional level of inference in which, on the one hand, a specific cognitive mechanism can be attributed to a particular brain activation pattern, and on the other hand, neural correlates can be ascribed to dynamic, quantitative models of behavior. Aim 1 is to develop methods for relating network activity and fMRI data. We hypothesize that successfully combining ANN models with fMRI will yield results similar to but not identical with results from more traditional task or stimulus based methods of fMRI analysis. The class of models considered here contains an input layer, an output layer, and at least one hidden layer, in addition to weighted connections between these layers. We will use an aggregate energy term to derive model-related activity associated with each stimulus presented to the model. When concatenated across stimuli, this model-derived signal will be treated as a prediction of the blood oxygen level dependent time course against which the signal in each brain image voxel will be tested. The expected advantage of the combined approach is that it should provide a clear functional interpretation of the fMRI results. Aim 2 is to apply these methods to the study of a few primary phenomena in reading aloud: word frequency effects, spelling-to-sound regularity, as well as sublexical orthographies and phonotactics. We hypothesize that network-derived regressors for these factors will yield more interpretive power than have standard regressors alone. The critical point is that interpretation of results from the combined approach is constrained by specific processing characteristics of the model from which the regressors were derived. Public Health Relevance: We aim to combine the complimentary strengths of fMRI and modeling to gain a deeper understanding of the brain, which will be invaluable in understanding and developing treatments for brain damage. This work could set the stage for a better understanding not only of language disorders, but, in principle, of any neurally-mediated disruption of behavior that can be modeled (e.g., amnesia, executive dysfunction, perceptual disorders, motor impairments).
Effective start/end date7/1/086/30/10


  • Eunice Kennedy Shriver National Institute of Child Health and Human Development: $50,054.00
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development: $46,826.00


  • Artificial Intelligence
  • Neuroscience(all)


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