Wavelet Decomposition Analysis for Ultra-high Temporal Resolution fMRI Time Series

Abstract

Functional magnetic resonance imaging (fMRI) is a powerful tool for human brain mapping. Previously, it has primarily been applied at low temporal resolution, i.e. repetition time >500 ms, and cannot resolve rapid neuronal and vascular function/dysfunction. Here we aim to achieve a ten-fold improvement in temporal resolution by localizing the brain coverage (i.e. single-slice) in combination with optimized MR acquisition schemes, e.g. using parallel imaging, reducing flip angle and reducing echo-time. A new challenge is that, at this resolution, physiologic noises become more pronounced and may mix with the true brain activation signals. We therefore applied wavelet decomposition to separate the MRI time-course into four components: fMRI signal, cardiac pulsation signal, respiratory fluctuation signal, and residual noise. In vivo experiments using flashing checkerboard visual stimulation revealed hemodynamic responses that are consistent with previous low-resolution data but with more detailed temporal features. Time-to-peak of the fMRI signal was determined in six healthy subjects and one patient with possible Alzheimer's disease. Measurement reproducibility of the proposed method was also evaluated in three of the subjects.