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Activity Number:
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37
- Object-Oriented Analysis of Imaging Data
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Type:
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Contributed
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Date/Time:
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Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #305357
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Title:
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Analyzing Spatial Variation Using Bayesian Functional Alignment
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Author(s):
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Guoqing Wang* and Abhi Datta and Martin Lindquist
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Companies:
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Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Johns Hopkins University
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Keywords:
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fMRI;
Spatial model;
Functional alignment;
Inter-subject variability
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Abstract:
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In the analysis of functional magnetic resonance imaging (fMRI) data the inter-subject variability in the location and distribution of functional regions dramatically decreases the spatial resolution and precision of population-level brain mapping, as features will not be properly aligned for subsequent statistical analysis. Here we construct spatial models of the relative misalignment of the individual-subject functional images with respect to a latent activation template, based on the use of Gaussian processes, which models activation with Gaussian functions whose parameters vary continuously over time or space. The fully Bayesian formulation of the model enables inference on the latent surface via posterior samples. The model allows us to test several types of hypotheses fundamental to brain mapping and cognitive neuroscience. These tests formalize many of the concepts researchers want to test in order to understand brain structure-to-function mapping. They also formalize inferences about the utility of multi-voxel pattern maps that are currently assumed (e.g., that local topological patterns are reliable and carry more information than voxel-wise activation magnitudes).
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Authors who are presenting talks have a * after their name.
