Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

被引:7
|
作者
Yin, Tao [1 ,2 ]
Ma, Peihong [1 ,2 ]
Tian, Zilei [1 ,2 ]
Xie, Kunnan [1 ,2 ]
He, Zhaoxuan [1 ,2 ]
Sun, Ruirui [1 ,2 ]
Zeng, Fang [1 ,2 ,3 ]
机构
[1] Chengdu Univ Tradit Chinese Med, Acupuncture & Tuina Sch, Third Teaching Hosp, Chengdu, Sichuan, Peoples R China
[2] Chengdu Univ Tradit Chinese Med, Acupuncture & Brain Sci Res Ctr, Chengdu, Sichuan, Peoples R China
[3] Chengdu Univ Tradit Chinese Med, Key Lab Sichuan Prov Acupuncture & Chronobiol, Chengdu, Sichuan, Peoples R China
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
SUPPORT VECTOR MACHINE; MULTIVARIATE CLASSIFICATION; PHYSICAL-EXERCISE; HUMAN BRAIN; PREDICTION; FMRI; DISORDER; SPECIFICITY; BIOMARKERS; NETWORK;
D O I
10.1155/2020/8871712
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research.
引用
收藏
页数:14
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