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

Xiao Hu, Chaoyong Xiao, Xiangrong Zhang, Sidong Liu, Zaixu Cui, Weiguo Liu, Long Qian

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Abstract

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.
Original languageEnglish
Publication statusAccepted/In press - Apr 2020

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    Hu, X., Xiao, C., Zhang, X., Liu, S., Cui, Z., Liu, W., & Qian, L. (Accepted/In press). A Machine Learning Approach for investigation of white matter abnormalities in Parkinson’s disease.