MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data
With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software...
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doaj-4ad7df504a1c4155b5153b9798b5f3eb2020-11-25T03:24:22ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-07-011410.3389/fnins.2020.00545533010MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging DataYanmin Peng0Yanmin Peng1Xi Zhang2Xi Zhang3Yifan Li4Yifan Li5Qian Su6Qian Su7Sijia Wang8Sijia Wang9Feng Liu10Feng Liu11Chunshui Yu12Chunshui Yu13Chunshui Yu14Meng Liang15Meng Liang16School of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaSchool of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaSchool of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaSchool of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaSchool of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, ChinaSchool of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, ChinaSchool of Medical Imaging, Tianjin Medical University, Tianjin, ChinaTianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, ChinaWith the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUI-based toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion.https://www.frontiersin.org/article/10.3389/fnins.2020.00545/fullmachine learningmultivariate pattern analysismultivoxel pattern analysisneuroimaginggraphical user interfacedata fusion |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yanmin Peng Yanmin Peng Xi Zhang Xi Zhang Yifan Li Yifan Li Qian Su Qian Su Sijia Wang Sijia Wang Feng Liu Feng Liu Chunshui Yu Chunshui Yu Chunshui Yu Meng Liang Meng Liang |
spellingShingle |
Yanmin Peng Yanmin Peng Xi Zhang Xi Zhang Yifan Li Yifan Li Qian Su Qian Su Sijia Wang Sijia Wang Feng Liu Feng Liu Chunshui Yu Chunshui Yu Chunshui Yu Meng Liang Meng Liang MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data Frontiers in Neuroscience machine learning multivariate pattern analysis multivoxel pattern analysis neuroimaging graphical user interface data fusion |
author_facet |
Yanmin Peng Yanmin Peng Xi Zhang Xi Zhang Yifan Li Yifan Li Qian Su Qian Su Sijia Wang Sijia Wang Feng Liu Feng Liu Chunshui Yu Chunshui Yu Chunshui Yu Meng Liang Meng Liang |
author_sort |
Yanmin Peng |
title |
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data |
title_short |
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data |
title_full |
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data |
title_fullStr |
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data |
title_full_unstemmed |
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data |
title_sort |
mvpani: a toolkit with friendly graphical user interface for multivariate pattern analysis of neuroimaging data |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2020-07-01 |
description |
With the rapid development of machine learning techniques, multivariate pattern analysis (MVPA) is becoming increasingly popular in the field of neuroimaging data analysis. Several software packages have been developed to facilitate its application in neuroimaging studies. As most of these software packages are based on command lines, researchers are required to learn how to program, which has greatly limited the use of MVPA for researchers without programming skills. Moreover, lacking a graphical user interface (GUI) also hinders the standardization of the application of MVPA in neuroimaging studies and, consequently, the replication of previous studies or comparisons of results between different studies. Therefore, we developed a GUI-based toolkit for MVPA of neuroimaging data: MVPANI (MVPA for Neuroimaging). Compared with other existing software packages, MVPANI has several advantages. First, MVPANI has a GUI and is, thus, more friendly for non-programmers. Second, MVPANI offers a variety of machine learning algorithms with the flexibility of parameter modification so that researchers can test different algorithms and tune parameters to identify the most suitable algorithms and parameters for their own data. Third, MVPANI also offers the function of data fusion at two levels (feature level or decision level) to utilize complementary information contained in different measures obtained from multimodal neuroimaging techniques. In this paper, we introduce this toolkit and provide four examples to demonstrate its usage, including (1) classification between patients and controls, (2) identification of brain areas containing discriminating information, (3) prediction of clinical scores, and (4) multimodal data fusion. |
topic |
machine learning multivariate pattern analysis multivoxel pattern analysis neuroimaging graphical user interface data fusion |
url |
https://www.frontiersin.org/article/10.3389/fnins.2020.00545/full |
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