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Activity Number:
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77
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #320534
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View Presentation
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Title:
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Exploiting Biology's Structure: Function Relationship to Improve Network Modeling in Neuroimaging
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Author(s):
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Barbara Wendelberger* and M. Elizabeth Meyerand
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Companies:
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University of Wisconsin - Madison and University of Wisconsin - Madison
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Keywords:
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Effective Connectivity ;
fMRI ;
DTI ;
Dynamic Causal Modeling
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Abstract:
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Dynamic causal modeling (DCM) incorporates statistical inference with biophysical modeling to estimate neural networks from functional magnetic resonance imaging (fMRI) data. DCM is a promising method for studying effective connectivity, which is the directed influence that neuronal structures exert on one another. Effective connectivity modeling has largely focused on the analysis of functional data alone. However, biology consistently demonstrates the correlation between anatomical structures and their function. Diffusion tensor imaging (DTI) and tractography can be used to infer the structural location of myelinated white matter tracts in the brain based on measured water diffusion. This study investigates the degree to which effective connectivity models might profit from the inclusion of detailed quantitative anatomical connectivity knowledge. DCM uses a fully Bayesian approach to estimate model parameters. The statistical methods presented here explore the best way to integrate information available from multiple imaging modalities and aim to improve DCM estimates through the use of DTI-informed priors.
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Authors who are presenting talks have a * after their name.
