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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Neural Plasticity |
| Online Access: | http://dx.doi.org/10.1155/2020/8871712 |
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| _version_ | 1850236404062224384 |
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| author | Tao Yin Peihong Ma Zilei Tian Kunnan Xie Zhaoxuan He Ruirui Sun Fang Zeng |
| author_facet | Tao Yin Peihong Ma Zilei Tian Kunnan Xie Zhaoxuan He Ruirui Sun Fang Zeng |
| author_sort | Tao Yin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1aa99ea243f14d0dad370f3b37f87bc5 |
| institution | OA Journals |
| issn | 2090-5904 1687-5443 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Neural Plasticity |
| spelling | doaj-art-1aa99ea243f14d0dad370f3b37f87bc52025-08-20T02:01:58ZengWileyNeural Plasticity2090-59041687-54432020-01-01202010.1155/2020/88717128871712Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for NeuroplasticityTao Yin0Peihong Ma1Zilei Tian2Kunnan Xie3Zhaoxuan He4Ruirui Sun5Fang Zeng6Acupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaAcupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaAcupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaAcupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaAcupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaAcupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaAcupuncture and Tuina School/The Third Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaThe 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.http://dx.doi.org/10.1155/2020/8871712 |
| spellingShingle | Tao Yin Peihong Ma Zilei Tian Kunnan Xie Zhaoxuan He Ruirui Sun Fang Zeng Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity Neural Plasticity |
| title | Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity |
| title_full | Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity |
| title_fullStr | Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity |
| title_full_unstemmed | Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity |
| title_short | Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity |
| title_sort | machine learning in neuroimaging a new approach to understand acupuncture for neuroplasticity |
| url | http://dx.doi.org/10.1155/2020/8871712 |
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