Statistical Methods in Fast Functional MRI

PROPOSAL NUMBER.: 0504387

INSTITUTION: Rutgers University New Brunswick

NSF PROGRAM: STATISTICS

PRINCIPAL INVESTIGATOR and Co-PI: Shepp, Larry and Zhang, C Hui

PROPOSAL TITLE: Statistical Methods in Fast Functional MRI

Abstract

The proposed research will further advance and use statistical methods developed by the principle investigators and their collaborators to

sharply improve time-resolution for the functional magnetic resonance imaging. The objective remains to improve the time-resolution of

functional magnetic resonance imaging by sampling only a small fraction of the Fourier transform of the spin density, and using a prolate

wavelet filter to approximately obtain an integral representing the total activity of the difference in susceptibility between task and pre-task, over various regions of interest in the brain at successive time-points. The cost for this is a decrease in spatial resolution. A nearly optimal trajectory will be used for sampling a small cube in three-dimensional k-space about the origin. This sampling region is also nearly optimal. The use of a, again nearly optimal, prolate filter will provide a low spatial but high temporal resolution image of the deoxy-hemoglobin density. An aim of the project is to find one or more consistent locations in the brain where oxygen is consumed during

higher level processing by the brain of the image in the primary visual region. This region would then be scanned in a two-dimensional

experiment where a slice plane is chosen to go through the region which would then give convincing demonstration of feasibility of the

proposed methods.

The proposal focuses on developing statistical methods and related theory for fast functional magnetic resonance imaging, to sharply

improve the time-resolution of present techniques. Fast fMRI is expected to have profound and far-reaching consequences in the

understanding of brain function, a problem of central scientific interest at the present time.