Activity Number:
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420
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 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 #318917
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View Presentation
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Title:
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Spatiotemporal Mixed Modeling of Multi-Subject fMRI via Method of Moments
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Author(s):
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Benjamin Risk * and David Matteson and R. Nathan Spreng and David Ruppert
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Companies:
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Statistical and Applied Mathematical Sciences Institute and Cornell University and Cornell University and Cornell University
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Keywords:
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Best linear unbiased prediction ;
Covariogram ;
Neuroimaging ;
Smoothing ;
task fMRI
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Abstract:
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Estimating spatiotemporal models for multi-subject fMRI is computationally challenging. We propose a mixed model for localization studies with locally stationary spatial random effects and time series errors. We develop method-of-moment estimators that leverage population and spatial information and are scalable to massive datasets. In simulations, subject-specific estimates of activation are considerably more accurate than the standard voxel-wise general linear model. Our mixed model also allows for valid population inference. We apply our model to cortical data from a motor task from the Human Connectome Project. Whereas the subject-specific maps based on the univariate approach appear noisy, our approach includes model-based smoothing that results in clearly delineated regions of activation. Subject-specific maps of activation from task fMRI are increasingly used in pre-surgical planning for tumor removal and in locating targets for transcranial magnetic stimulation. Our findings suggest that using spatial and population information is a promising avenue for improving clinical neuroimaging.
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Authors who are presenting talks have a * after their name.