Processing strategies for time‐course data sets in functional mri of the human brain

TLDR

Functional MRI data acquired with gradient‑recalled echo‑planar imaging are processed using various strategies, including analyses of finger‑motion paradigms. The study aims to develop and evaluate new FMRI image‑processing techniques—shape‑based thresholding and drift elimination—to better characterize brain responses to finger‑motion paradigms. Using vector‑space mathematics, the authors analyze time‑series data in both time and frequency domains, apply shape‑based thresholding and drift‑removal, and compare the resulting cross‑correlation images to conventional subtraction methods on motor‑cortex data. Shape‑based thresholding via correlation coefficients, followed by cross‑correlation image formation, was found to be the most effective FMRI processing method.

Abstract

Abstract Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient‐recalled echo‐planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time‐domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor—cortex and compared with more conventional image—subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross‐correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.