Work

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

Year: 2019

Type: article

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.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/tmi.2019.2915052

Authors: Israel Almodovar-Rivera, Ranjan Maitra

Institutions: University of Puerto Rico System, Iowa State University

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funders: Iowa State University, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering, Division of Graduate Education

Award: Statistical Methods for Improved Activation Detection in fMRI Studies