MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data

Abstract

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 GUIbased 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.

Differential grey matter structure in women with premenstrual dysphoric disorder: evidence from brain morphometry and data-driven classification

“…In addition, we performed pattern classification to distinguish women with PMDD from controls based on each structural measure. To that end, we used the MVPANI toolbox ( http://funi.tmu.edu.cn ) [ 32 ] implemented in Matlab. All participants were divided into 10 folds, resulting in 13 participants in the first 9 folds and 14 participants in the last 4 folds.…”

Dynamic and Static Amplitude of Low-Frequency Fluctuation Is a Potential Biomarker for Predicting Prognosis of Degenerative Cervical Myelopathy Patients: A Preliminary Resting-State fMRI Study

“…In this study, MVPA analysis was performed using the MVPANI toolbox ( 24 ) ( http://funi.tmu.edu.cn ) and LibSVM's implementation of linear Support Vector Machine (SVM) using default parameters. The linear kernel was used and the penalty coefficient (c) was set to 1.…”

Identification and Therapeutic Outcome Prediction of Cervical Spondylotic Myelopathy Based on the Functional Connectivity From Resting-State Functional MRI Data: A Preliminary Machine Learning Study

“…The pattern classification was performed to classify patients with CSM and HCs based on FC using the MVPANI toolbox (http:// funi.tmu.edu.cn) and LibSVM's implementation of linear SVM using the default parameters (35). A large vector with 6,670 features was extracted from each subject.…”