JSM 2005 Online Program

Abstract #304479

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	473
Type:	Topic Contributed
Date/Time:	Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Biometrics Section
Abstract - #304479
Title:	Maximizing Power with Arterial Spin Labeling fMRI
Author(s):	Jeanette Mumford*+ and Luis Hernandez-Garcia and Thomas Nichols
Companies:	University of Michigan and University of Michigan and University of Michigan
Address:	, Ann Arbor, MI, 48109-2029, United States
Keywords:	arterial spin label ; autocorrelation function modeling
Abstract:	Functional Magnetic Resonance Imaging (fMRI) is a method for measuring human brain activity. Typically, fMRI uses the blood oxygen level-dependent (BOLD) effect, a nonquantitative method where signal changes are attributable to changes in blood flow, blood volume, and oxygen metabolism. We consider a relatively new quantitative fMRI method: blood flow imaging with arterial spin labeling (ASL). ASL measures blood flow using magnetically labeled arterial blood as a tracer, and the data consists of alternating control images (with no magnetic label) and labeled images. The subtraction of the control/label pairs creates length-N/2 time series. While the original length-N time series have appreciable autocorrelation, the differencing process tends to whiten the data, and the length-N/2 time series are assumed to have iid errors. We propose that, instead of differencing the data, the differencing should be built into the model and the length-N data should be modeled directly. In preliminary work, we found that modeling all of the data results in marked increase in precision (24% decrease in stdev). ACF modeling and whitening will improve the precision even further.

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