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

Abstract

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.

26 pages, 2 tables, 19 figures. Accepted for publication in IEEE Transactions on Medical Imaging

References in corpus (3)

• The Statistical Analysis of fMRI Data
• A Re-defined and Generalized Percent-Overlap-of-Activation Measure for Studies of fMRI Reproducibility and its Use in Identifying Outlier Activation Maps
• Assessing Certainty of Activation or Inactivation in Test-Retest fMRI Studies

Cited by in corpus (3)

• Classification with the matrix-variate-$t$ distribution
• Reduced-Rank Tensor-on-Tensor Regression and Tensor-variate Analysis of Variance
• Adaptive Smoothing in fMRI Data Processing Neural Networks