Abstract #2225

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

Mark Mikkelsen^1,2, Pallab K. Bhattacharyya^3,4, Pravat K. Mandal^5,6, Deepika Shukla⁵, Anna M. Wang^1,2, Martin Wilson⁷, Ulrike Dydak^8,9, James B. Murdoch¹⁰, Jamie Near¹¹, Georg Oeltzschner^1,2, and Richard A.E. Edden^1,2

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.

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