ArticleFrontiers in neuroscience2020
MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Investigating the causal role of sleep disorders in anorexia nervosa and obsessive-compulsive disorder: a bidirectional Mendelian randomization study.Eating and weight disorders : EWD · 2026Article
- Increased Glymphatic System Activity and Hypothalamic Connectivity in Patients With Premenstrual Dysphoric Disorder.Depression and anxiety · 2026Article
- Linking Connectivity Dynamics to Symptom Severity and Cognitive Abilities in Children with Autism Spectrum Disorder: An FNIRS Study.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Neuroimaging and biological markers of different paretic hand outcomes after stroke.Journal of neuroengineering and rehabilitation · 2025Article
- The relationship between regional homogeneity in resting-state functional magnetic resonance imaging and cognitive function in depressive disorders with migraine.Scientific reports · 2025Article
- Abnormal subthalamic nucleus functional connectivity and machine learning classification in Parkinson's disease: a multisite functional magnetic resonance imaging study.Frontiers in aging neuroscience · 2025Article
- Exploring the Morphological Study of the Brain in Patients with Bipolar Disorder Based on Structural Magnetic Resonance Imaging.Neuropsychiatric disease and treatment · 2025Article
- Functional Connectivity-Based Searchlight Multivariate Pattern Analysis for Discriminating Schizophrenia Patients and Predicting Clinical Variables.Schizophrenia bulletin · 2024Article
- Pain-Discriminating Information Decoded From Spatiotemporal Patterns of Hemodynamic Responses Measured by fMRI in the Human Brain.Human brain mapping · 2024Article
- Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.Translational psychiatry · 2024Article
- Unraveling multi-scale neuroimaging biomarkers and molecular foundations for schizophrenia: A combined multivariate pattern analysis and transcriptome-neuroimaging association study.CNS neuroscience & therapeutics · 2024Article
- Voxel- and tensor-based morphometry with machine learning techniques identifying characteristic brain impairment in patients with cervical spondylotic myelopathy.Frontiers in neurology · 2024Article
- Brain structural correlates of postoperative axial pain in degenerative cervical myelopathy patients following posterior cervical decompression surgery: a voxel-based morphometry study.BMC medical imaging · 2023Article
- The role of ascending arousal network in patients with chronic insomnia disorder.Human brain mapping · 2023Article
- Changes in dynamic and static brain fluctuation distinguish minimal hepatic encephalopathy and cirrhosis patients and predict the severity of liver damage.Frontiers in neuroscience · 2023Article
- NeuroDecodeR: a package for neural decoding in R.Frontiers in neuroinformatics · 2023Article
- Differential grey matter structure in women with premenstrual dysphoric disorder: evidence from brain morphometry and data-driven classification.Translational psychiatry · 2022Article
- Exploring neurometabolic alterations in bipolar disorder with suicidal ideation based on proton magnetic resonance spectroscopy and machine learning technology.Frontiers in neuroscience · 2022Article
- SF-MVPA: A from raw data to statistical results and surface space-based MVPA toolbox.Frontiers in neuroscience · 2022Article
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8 authors.
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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 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.
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