IM‐03: Mr imaging at 3.0T: Advantages and drawbacks

not available. IM-02 MULTIVARIATE STATISTICAL METHODS IN THE ANALYSIS OF NEUROIMAGING DATA Gene E. Alexander, Arizona State University, Tempe, AZ, USA. Contact e-mail: gene.alexander@asu.edu Background: Neuroimaging studies have typically relied on univariate analyses to evaluate brain changes associated with age-related, neurodegenerative disease. Multivariate network analysis methods can test for spatial covariance patterns in neuroimaging data, providing the opportunity to evaluate brain changes as network interactions that can reflect the regionally distributed effects of aging and disease (Alexander and Moeller, 1994). It is well established that patients with Alzheimer’s disease (AD) show regional reductions and declines in brain function and structure assessed by positron emission tomography (PET) and magnetic resonance imaging (MRI). Objective: To review multivariate network analysis approaches for neuroimaging data and illustrate applications to studies of aging and AD. Methods: The application of multivariate network analyses to neuroimaging data will be presented using the Scaled Subprofile Model (SSM; Moeller et al., 1987). As a modified form of principal component analysis, the SSM produces regional patterns from neuroimaging data on a voxel-basis throughout the brain and corresponding subjects scores that reflect an individual’s degree of pattern expression. Bootstrap re-sampling is performed to determine robust regional contributions to the identified patterns. The use of this analytic approach with PET and MRI will be discussed, including potential applications for aiding early detection, for tracking progression, and for evaluating treatments in both human and non-human animal models of aging and neurodegenerative disease. Results: Studies using multivariate SSM analyses provide support for regionally distributed patterns of brain changes associated with AD that differ from the effects of healthy aging. Such SSM network patterns can be directly evaluated in relation to external measures of behavior, genetic risk, and other biological markers. Further, they can be used to track brain changes over time and to evaluate the effects of treatments or interventions for aging and AD. Conclusions: The findings illustrate the potential for using multivariate analyses, like SSM, as a complement to univariate analysis methods to characterize brain changes in the context of aging, facilitating translational neuroimaging studies of age-related, neurodegenerative disease. IM-03 MR IMAGING AT 3.0T: ADVANTAGES AND DRAWBACKS Matt Bernstein, Department of Radiology, Mayo Clinic College of Medicine, Rochester, MN, USA. Contact e-mail: mbernstein@mayo.edu The Scaling of Several Physical Parameters with the Strength of the Main Magnetic Field B0 Physical Parameter Dependence on Bo Signal-noise-ratio Linear Susceptibility variation (in Hz) Linear Chemical Shift (in Hz) Linear SAR (in W/Kg) Quadratic RF wavelength (in meters) Inverse Magnetic resonance imaging (MRI) at the main field strength of 3.0T (3T) is gaining wider clinical acceptance (1-4). Imaging of the brain has been a primary application of 3T MRI (5-7). The main advantage of 3T MRI is the approximate doubling of the signal-to-noise ratio (SNR) compared to the standard field strength of 1.5T. As shown in the figure, the increased SNR can be translated into increased spatial resolution. As summarized in the table, however, other physical parameters such as susceptibility variation (measured in hertz), and the RF heating or specific absorption rate (SAR, measured in W/kg) also increase at 3T. This can lead to restrictions on the imaging protocol, or image artifacts with increased conspicuity at 3T (8). In this abstract the scaling of the physical parameters with MRI field strength is reviewed. Advantages of 3T for neuroimaging are discussed, along with image artifacts that are commonly observed at 3T. When possible, countermeasures to reduce the artifacts are presented. 1. Lin W, An H, Chen Y, et al. Practical consideration for 3T imaging. Magn Reson Imaging Clin N Am 2003;11:615-639. 2. Takahashi M, Uematsu H, Hatabu H. MR imaging at high magnetic fields. Eur J Radiol 2003;46:45-52. 3. Schmitt F, Grosu D, Mohr C, et al. 3 Tesla MRI: successful results with higher field strengths. Radiologe 2004;44:31-47. 4. Hu X, Norris DG. Advances in high-field magnetic resonance imaging. Annu Rev Biomed Eng 2004;6:157-184. 5. Larsson EM, Stahlberg F. 3 Tesla magnetic resonance imaging of the brain. Better morphological and functional images with higher magnetic field strength. Lakartidningen 2005;102:460-463. 6. Briellmann RS, Pell GS, Wellard RM, Mitchell LA, Abbott DF, Jackson GD. MR imaging of epilepsy: state of the art at 1.5 T and potential of 3 T. Epileptic Disord 2003;5:3-20. 7. Schmitz BL, Aschoff AJ, Hoffmann MH, Gron G. Advantages and pitfalls in 3T MR brain imaging: a pictorial review. AJNR Am J Neuroradiol 2005;26:2229-2237. 8. Bernstein MA, Huston J III, and Ward HA, Imaging Artifacts at 3.0T. JMRI 2006; 24:735-746. IM-04 AMYLOID IMAGING IN NORMAL AGING Mark Mintum, Washington University School of Medicine, St. Louis, MO, USA. Contact e-mail: mintunm@mir.wustl.edu Abstract not available. S92 Preconference Imaging IMnot available. S92 Preconference Imaging IM