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

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 neuroplasticit...

Full description

Saved in:
Bibliographic Details
Published in:Neural plasticity 2020, Vol.2020 (2020), p.1-14
Main Authors: Zeng, Fang, He, Zhaoxuan, Xie, Kunnan, Tian, Zilei, Ma, Peihong, Yin, Tao, Sun, Ruirui
Format: Article
Language:English
Subjects:
Acupuncture
Algorithms
Artificial intelligence
Brain research
Classification
Decision trees
Design
Electroconvulsive therapy
Intervention
Machine learning
Magnetic resonance imaging
Neural networks
Neuroimaging
Pain
Review
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2090-5904
1687-5443
DOI:10.1155/2020/8871712