Fast Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI

Functional magnetic resonance imaging is a noninvasive tool for studying cerebral function. Many factors challenge activation detection, especially in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated fast adaptive smoothing and thresholdi...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2019-12, Vol.38 (12), p.2821-2828
Main Authors: Almodovar-Rivera, Israel, Maitra, Ranjan
Format: Article
Language:English
Subjects:
Activation
Adaptive algorithms
adaptive segmentation
AFNI
ALL-FAST
AM-FAST
AR-FAST
BIC
cluster thresholding
CNR
Cognitive ability
Cognitive tasks
Correlation
Extreme value theory
Extreme values
Functional magnetic resonance imaging
Limiting
Magnetic resonance imaging
Random variables
Sensory integration
Smoothing
Smoothing methods
SPM
Statistical analysis
Task analysis
TFCE
Thresholding (Imaging)
Visual perception
Visual stimuli
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Summary:Functional magnetic resonance imaging is a noninvasive tool for studying cerebral function. Many factors challenge activation detection, especially in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated fast adaptive smoothing and thresholding (FAST) algorithm that uses smoothing and extreme value theory on correlated statistical parametric maps for thresholding. Performance on 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 or only-visual-reliable speech stimuli.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2019.2915052