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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