Abstract Details
Activity Number:
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415
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 11, 2015 : 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 #316110
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View Presentation
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Title:
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Improved Activation Detection via Complex-Valued AR(p) Modeling of fMRI Voxel Time Series
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Author(s):
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Daniel Adrian* and Ranjan Maitra and Daniel Rowe
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Companies:
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Grand Valley State University and Iowa State University and Marquette University
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Keywords:
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Cochran-Orcutt estimation procedure ;
partial autocorrelation function ;
Rice distribution ;
signal-to-noise ratio ;
bilateral finger-tapping motor experiment ;
phase information
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Abstract:
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A complex-valued model with AR(p) errors is proposed as an alternative to the more common Gaussian-assumed magnitude-only AR(p) model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and are compared in terms of activation detection and false discovery rates for simulated and experimental data. For simulated data, the complex-valued AR(p) model likelihood-ratio activation statistic shows superior power of activation detection at low signal-to-noise ratios and lower false discovery rates. Also, when applied to an experimental data set, the activation map produced by the complex-valued AR(p) model more clearly identifies the primary activation regions. Our results advocate the use of the complex-valued data and the Gaussian AR(p) model as a more efficient and reliable tool in fMRI experiments over the current practice of using only the magnitude dataset.
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Authors who are presenting talks have a * after their name.
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