Title: FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI,
Abstract: 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
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.