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...

Full description

Bibliographic Details
Main Authors: Yanmin Peng, Xi Zhang, Yifan Li, Qian Su, Sijia Wang, Feng Liu, Chunshui Yu, Meng Liang
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-07-01
Series:Frontiers in Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2020.00545/full
id doaj-4ad7df504a1c4155b5153b9798b5f3eb
record_format Article
spelling 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
work_keys_str_mv AT yanminpeng mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT yanminpeng mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT xizhang mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT xizhang mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT yifanli mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT yifanli mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT qiansu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT qiansu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT sijiawang mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT sijiawang mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT fengliu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT fengliu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT chunshuiyu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT chunshuiyu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT chunshuiyu mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT mengliang mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
AT mengliang mvpaniatoolkitwithfriendlygraphicaluserinterfaceformultivariatepatternanalysisofneuroimagingdata
_version_ 1724602003231866880