Meeting Banner
Abstract #2225

Analyzing Big GABA: Comparison of Five Software Packages for GABA-Edited MRS

Mark Mikkelsen1,2, Pallab K. Bhattacharyya3,4, Pravat K. Mandal5,6, Deepika Shukla5, Anna M. Wang1,2, Martin Wilson7, Ulrike Dydak8,9, James B. Murdoch10, Jamie Near11, Georg Oeltzschner1,2, and Richard A.E. Edden1,2

1Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, 2F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, 3Imaging Institute, Cleveland Clinic Foundation, Cleveland, OH, United States, 4Radiology, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH, United States, 5Neuroimaging and Neurospectroscopy Laboratory, National Brain Research Centre, Gurgaon, India, 6The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, 7Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom, 8School of Health Sciences, Purdue University, West Lafayette, IN, United States, 9Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States, 10Canon Medical Research USA, Mayfield Village, OH, United States, 11Douglas Mental Health University Institute and Department of Psychiatry, McGill University, Montreal, QC, Canada

Given the number of software analysis packages available to the MRS community, surprisingly little attention has been paid to comparing the performance of each, particularly with regard to multi-site and multi-vendor datasets. Standardization of MRS methods will necessarily require that processing and quantification tools also produce comparable outcomes. This abstract describes a comparison of five widely used software packages analyzing multi-site edited MRS data to quantify in vivo GABA+/Cr levels. The overall agreement between the packages was moderate, with packages showing systematic site-to-site biases. Further analysis on a larger cohort of data will aid in determining the cause of these discrepancies.

This abstract and the presentation materials are available to members only; a login is required.

Join Here