Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity.
Journal
Neural plasticity
ISSN: 1687-5443
Titre abrégé: Neural Plast
Pays: United States
ID NLM: 100883417
Informations de publication
Date de publication:
2020
2020
Historique:
received:
25
06
2020
revised:
02
08
2020
accepted:
08
08
2020
entrez:
10
9
2020
pubmed:
11
9
2020
medline:
4
8
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32908491
doi: 10.1155/2020/8871712
pmc: PMC7463415
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
8871712Informations de copyright
Copyright © 2020 Tao Yin et al.
Déclaration de conflit d'intérêts
The authors declare that they have no competing interests.
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