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Abstract #1899

A Machine Learning Approach for investigation of white matter abnormalities in Parkinson’s disease

xiao hu1, chaoyong xiao2, xiangrong zhang2, sidong liu3, zaixu cui4, weiguo liu5, and long qian6
1radiology, nanjing brain hospital, nanjing, China, 2nanjing brain hospital, nanjing, China, 3Clinical Medicine, Macquarie University, Sydney, Australia, 4Psychiatry, University of Pennsylvania, Philadelphia, PA, United States, 5neurology, nanjing brain hospital, nanjing, China, 6Biomedical Engineering, Peking University, beijing, China

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms.1 The abnormalities of white-matter (WM) tracts/regions have been demonstrated in PD. However, previous studies have largely dependent on univariate analysis, such as t-test, which may result in Type-1 error. Further, it remains unclear whether the disruption of WM tracts/regions provided worthwhile information to identify PD from HC. Hence, in current study, a machine learning approach was applied to investigate the white matter profiles of PD.

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