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

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

Department of Clinical Medicine

Research output: Contribution to conference › Abstract

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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 language	English
Number of pages	1
Publication status	Published - Aug 2020
Event	2020 ISMRM & SMRT Virtual Conference and Exhibition - Virtual event Duration: 8 Aug 2020 → 14 Aug 2020

Conference

Conference	2020 ISMRM & SMRT Virtual Conference and Exhibition
Period	8/08/20 → 14/08/20

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Maintext3_huxiaoAccepted author manuscript, 198 KBLicence: Unspecified

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title = "A Machine Learning Approach for investigation of white matter abnormalities in Parkinson’s disease",

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.",

author = "Xiao Hu and Chaoyong Xiao and Xiangrong Zhang and Sidong Liu and Zaixu Cui and Weiguo Liu and Long Qian",

year = "2020",

month = "8",

language = "English",

note = "2020 ISMRM & SMRT Virtual Conference and Exhibition ; Conference date: 08-08-2020 Through 14-08-2020",

}

TY - CONF

T1 - A Machine Learning Approach for investigation of white matter abnormalities in Parkinson’s disease

AU - Hu, Xiao

AU - Xiao, Chaoyong

AU - Zhang, Xiangrong

AU - Liu, Sidong

AU - Cui, Zaixu

AU - Liu, Weiguo

AU - Qian, Long

PY - 2020/8

Y1 - 2020/8

N2 - 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.

AB - 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.

M3 - Abstract

ER -