Improving the temporal accuracy of functional Magnetic Resonance Imaging

Abstract

AbstractA major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of temporal accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the temporal accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices. Specifically, we show how the current volume creation method leads to whole-brain volumes that contain within-volume temporal distortions and that are available at a low temporal resolution. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain no temporal distortions, and are available at a high temporal resolution. Statistical signal extraction occurs on the basis of a novel time point-by-time point approach. We evaluated the temporal characteristics of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved temporal accuracy of fMRI signals.