Abstract #0194

Age- and sex-related spatial patterns of variation in normal brain magnetic susceptibility (QSM) revealed by Blind Source Separation (BSS) and Supervised Machine Learning

Ferdinand Schweser^1,2, Balint Sule¹, Juliane Damm¹, Niels P Bergsland^1,3, Michael G Dwyer¹, Akshay V Dhamankar¹, Bianca Weinstock-Guttman⁴, and Robert Zivadinov^1,2

¹Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States, ²Center for Biomedical Imaging, Clinical and Translational Science Institute, University at Buffalo, The State University of New York, Buffalo, NY, United States, ³MR Research Laboratory, IRCCS, Don Gnocchi Foundation ONLUS, Milan, Italy, ⁴BairdMS Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States

Previous studies using QSM have demonstrated a relatively high inter-subject variation of brain susceptibility. In the present work, we combined a blind source separation technique with a machine learning strategy to disentangle spatial networks of independent variation of brain susceptibility. As a first step toward a better understanding of the underlying causes of variation, we studied their associations with age and sex. The analysis revealed several networks with distinct anatomical features, although the applied analysis technique did not involve any information about anatomy, age, or sex.

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