Abstract #4676
Advanced DWI Metrics Reveal Distinct Sex-Related Differences in Splenium of Corpus Callosum White Matter
Enamul H. Bhuiyan1, Qingfei Luo2,3, Muge Karaman2,4, Kezhou Wang2, Ping-Shou Zhong5, Laura Frey-Law6, James C. Ford7, Stephani P. Sutherland8, Tor D. Wager9, Patrick Sadil8, Micah Johnson8, Martin Lindquist8, Robert J. McCarthy10, Asokumar Buvanendran10, Joshua J. Jacobs11, John Burns12, Xiaohong Joe Zhou2,3,4, and the A2CPS Consortium13
1Center for Magnetic Resonance Research, College of Medicine, University of Illinois Chicago, Chicago, IL, United States, 2Center for Magnetic Resonance Research, University of Illinois Chicago, Chicago, IL, United States, 3Depratment of Radiology, University of Illinois Chicago, Chicago, IL, United States, 4Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, United States, 5Department of Math, Statistics and Computer Science, University of Illinois Chicago, Chicago, IL, United States, 6Department of Physical Therapy and Rehabilitation Science, Carver College of Medicine, University of Iowa, Iowa City, IA, United States, 7Geisel School of Medicine at Dartmouth, Hanover, NH, United States, 8Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States, 9Presidential Cluster in Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States, 10Department of Anesthesiology, Rush University Medical Center, Chicago, IL, United States, 11Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States, 12Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States, 13National Institutes of Health (NIH), https://a2cps.org/, Bethesda, MD, United States
Synopsis
Keywords: DWI/DTI/DKI, DWI/DTI/DKI, Diffusion-Weighted Imaging, Quantitative DWI, Machine Learning, XGBoost Classifier, Demographics.
Motivation: Advanced DWI metrics may reveal sex-related white matter variations, offering potential biomarkers for personalized chronic pain management.
Goal(s): This study reveals demographic influences on DWI metrics, focusing on sex differences in the white matter within the splenium of the corpus callosum.
Approach: Using data from 588 participants, DWI metrics were processed with QSIPrep, FSL, and PyDesigner and analyzed for sex differences through statistical tests and an XGBoost classifier and SHAP for machine learning results analysis.
Results: Significant sex-based differences emerged, with EAS AD as a potential metric. The classifier achieved 81% accuracy, highlighting potential DWI biomarkers for sex-based differentiation.
Impact: The A2CPS dataset reveals sex-specific influences on advanced DWI metrics, potentially paving the way for future biomarkers in personalized pain management and an enriched understanding of brain function.
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