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In Vivo Brain Magnetic Resonance Spectroscopy: A Measurement of Biomarker Sensitivity to Post-Processing Algorithms

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MR brain spectroscopy biomarkers are diagnostic for a wide variety of neurological disorders.  Although rarely discussed in detail in the literature, reported biomarkers depend upon parameters for several algorithms that serially mitigate distortions and allow biomarkers to demonstrate statistical significance.  The specific parameter values are often not reported and, moreover, are not selected with rigorous analytical support.  For the first time, the sensitivity of prospective biomarkers to changes in specific parameter values is quantified with MRS biomarkers for spinal cord injury previously reported in the literature.  Our results demonstrate that the statistical significance of candidate biomarkers can change dramatically with slight changes in individual parameters.  Further, the ability to identify parameter values that maximize statistical significance and effect size can improve the diagnostic power of prospective biomarkers.  Our work motivates the need to develop and report measures for sensitivity that will add greater context to reported MRS biomarkers and aid their subsequent validation.
MR brain spectroscopy biomarkers are diagnostic for a wide variety of neurological disorders. Although rarely discussed in detail in the literature, reported biomarkers depend upon parameters for several algorithms that serially mitigate distortions and allow biomarkers to demonstrate statistical significance. The specific parameter values are often not reported and, moreover, are not selected with rigorous analytical support. For the first time, the sensitivity of prospective biomarkers to changes in specific parameter values is quantified with MRS biomarkers for spinal cord injury previously reported in the literature. Our results demonstrate that the statistical significance of candidate biomarkers can change dramatically with slight changes in individual parameters. Further, the ability to identify parameter values that maximize statistical significance and effect size can improve the diagnostic power of prospective biomarkers. Our work motivates the need to develop and report measures for sensitivity that will add greater context to reported MRS biomarkers and aid their subsequent validation.

Clinical translation of reported biomarkers requires reliable and consistent algorithms to derive biomarkers. However, the literature reports statistically significant differences between one dimensional (1D) MRS measurements from control groups and subjects with disease states but frequently provides little information on the algorithms and parameters used to process the data. The sensitivity of in vivo brain magnetic resonance spectroscopy (MRS) biomarkers is investigated with respect to parameter values for two key stages of post-acquisitional processing. Our effort is specifically motivated by the lack of consensus on approaches and parameter values for the two critical operations, water resonance removal and baseline correction. The different stages of data processing also introduce varying levels of uncertainty and arbitrary selection of parameter values can significantly underutilize the intrinsic differences between two classes of signals. The sensitivity of biomarkers points to the need for a better understanding of how all stages of post-acquisitional processing affect biomarker discovery and ultimately, clinical translation. Our results also highlight the possibility of optimizing biomarker discovery by the careful selection of parameters that best reveal class differences. Using previously reported data and biomarkers, our results demonstrate that small changes in parameter values affect the statistical significance and corresponding effect size of biomarkers. Consequently, it is possible to increase the strength of biomarkers by selecting optimal parameter values in different spectral intervals. Our analyses with a previously reported data set demonstrate an increase in effect sizes for wavelet-based biomarkers of up to 36%, with increases in classification performance of up to 12%.
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Authors

See complete bios of the authors in the full version of this article.
D CocuzzoD Cocuzzo
Mr. Cocuzzo is a graduate student at the Department of Computer Science at Stanford University, focusing on large scale distributed systems, security, and data mining. Previously, he was an engineer at Draper Laboratory in Cambridge, MA. Dan earned his B.S. in electrical engineering at Northeastern University in Boston, MA.

A LinA Lin
Dr. Lin is currently an Instructor with the Department of Radiology at Harvard Medical School and Research Associate at Brigham and Women’s Hospital, Boston, MA. He is also a visiting research associate at the Center of MR Research at University of Illinois Chicago. Dr. Lin’s primary research interests are the clinical applications of magnetic resonance spectroscopy.

P StanwellP Stanwell
Mr. Stanwell is currently an Associate Professor in the Faculty of Health and Medicine at The University of Newcastle, Australia. His research interest centers on developing medical imaging techniques to address clinically relevant needs including ischemic stroke imaging, traumatic brain injury, sport-related concussion, and radiation oncology.

C MountfordC Mountford
Dr. Mountford is currently a Professor in Radiology at The University of Newcastle in Australia and full Professor of Radiology at the Harvard Medical School in the USA. She is the Director of the Centre for MR in Health in Newcastle and Director of the Centre for Clinical Spectroscopy at the Brigham and Women’s Hospital in Boston. Professor Mountford is a Fellow of the International Society of Magnetic Resonance in Medicine.

N KeshavaN Keshava
Dr. Keshava is currently a Senior Principal Informatics Scientist in the Research and Development Information organization at AstraZeneca Pharmaceuticals in Waltham, MA. His responsibilities include developing analytics for big data challenges across all areas of drug development. He has also been the principal investigator on a variety of research programs focusing on neuroimaging, psychology, and neurophysiology.

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