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Activity Number:
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342
- Novel Statistical Testing and Activation-Detection Methods for Imaging Data
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #324792
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View Presentation
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Title:
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FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI
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Author(s):
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Ranjan Maitra* and Israel Almodovar-Rivera
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Companies:
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Iowa State University and University of Puerto Rico
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Keywords:
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AM-FAST ;
AR-FAST ;
Adaptive Segmentation ;
circulant matrix ;
Gumbel distribution ;
reverse Weibull distribution
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Abstract:
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Functional Magnetic Resonance Imaging is a noninvasive tool used to study brain function. Detecting activation is challenged by many factors, and even more so in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated and fast adaptive smoothing and thresholding (FAST) algorithm that uses smoothing and extreme value theory on correlated statistical parametric maps for thresholding. Performance on simulation experiments spanning a range of low-signal settings is very encouraging. The methodology also performs well in a study to identify the cerebral regions that perceive only-auditory-reliable and only-visual-reliable speech stimuli as well as those that perceive one but not the other.
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Authors who are presenting talks have a * after their name.
