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

arXiv: https://arxiv.org/abs/1702.00111 · HTML: https://mlsanity.com/paper/1702.00111/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2017.

## Built on by (most cited first, 2 of 2)

- [CatSIM: A Categorical Image Similarity Metric](https://mlsanity.com/paper/2004.09073/) (2020)
- [A practical model-based segmentation approach for improved activation detection in single-subject functional Magnetic Resonance Imaging studies](https://mlsanity.com/paper/2102.03639/) (2021)

## Abstract (arXiv)

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.