Flórez Coronel, Juan Esteban

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    A Bayesian adaptive smoothing and thresholding approach for activation detection in single-subject fMRI
    (2024-07-10) Flórez Coronel, Juan Esteban; Almodóvar Rivera, Israel; College of Arts and Sciences - Sciences; Bustillo Zarate, Alcibiades; Rivera Santiago, Roberto; Department of Mathematics; Romañach, Rodolfo J.
    Functional Magnetic Resonance Imaging (fMRI) is a widely used non-invasive medical procedure for studying brain function. Identifying activated regions of the brain is a common challenge in fMRI analysis. Low-signal and small data cases pose significant difficulties for activation detection. These scenarios arise when studying high-level cognitive tasks or single-subject experiments, respectively. In this study, we propose an innovative algorithm, entitled Bayesian Fast Adaptive Smoothing and Thresholding (BFAST), which utilizes smoothing and extreme value theory on probabilistic maps to find threshold values. The algorithm’s performance was evaluated on artificial data that simulated a range of signal magnitudes. The results were promising, with an average similarity of 85% with respect to the expected output. Furthermore, the proposed procedure was applied to a study that aimed to identify the cerebral regions responsible for processing beliefs and questions as stimuli. Our findings suggest that the BFAST algorithm holds promise for detecting activated areas in the brain with high accuracy, particularly in cases involving low-signal and small data. Such advancements in fMRI analysis algorithms could lead to more accurate and precise studies of brain function, with significant implications for both clinical and research settings.