John Bodenschatz (https://mssc.mu.edu/~johnboden/) is a PhD candidate at Marquette University in the department of Computational Mathematical and Statistical Sciences. He obtained an MS in Applied Statistics from Marquette in 2023 and a BS in Mathematics and Physics from the University of Cincinnati in 2021.

Functional magnetic resonance imaging (fMRI) is used to study how the brain works by measuring changes in signals across different parts of the brain. Researchers want fMRI scans that are both fast and detailed. However, increasing temporal and spatial resolution also increases noise, which can make the data harder to analyze. Complex-valued fMRI time series can be deconstructed into two parts: magnitude and phase. While most studies focus on magnitude images, research shows that the phase component can also contain important biological information. In this work we focus on analyzing phase-only activation using maximum likelihood estimation with a mathematically accurate model. This approach allows phase information to be modeled more accurately, particularly in cases where the signal-to-noise ratio is low, resulting in the detection of task-related signal changes in the phase component of fMRI time series. 1-2 Sentence Bio: