ArticleNeuroinformatics2014
MANIA-a pattern classification toolbox for neuroimaging data.
Article in Neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Theranostic markers for personalized therapy of spider phobia: Methods of a bicentric external cross-validation machine learning approach.International journal of methods in psychiatric research · 2020Trial
- ACTION: Augmentation and computation toolbox for brain network analysis with functional MRI.NeuroImage · 2025Article
- aXonica: A support package for MRI based Neuroimaging.Biotechnology notes (Amsterdam, Netherlands) · 2024Article
- Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.Frontiers in aging neuroscience · 2022Article
- Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.Personality neuroscience · 2021Article
- Investigating the effect of changing parameters when building prediction models for post-stroke aphasia.Nature human behaviour · 2020Article
- MVPANI: A Toolkit With Friendly Graphical User Interface for Multivariate Pattern Analysis of Neuroimaging Data.Frontiers in neuroscience · 2020Article
- QModeling: a Multiplatform, Easy-to-Use and Open-Source Toolbox for PET Kinetic Analysis.Neuroinformatics · 2019Article
- Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.Neuroinformatics · 2018Article
- Differential Abnormal Pattern of Anterior Cingulate Gyrus Activation in Unipolar and Bipolar Depression: an fMRI and Pattern Classification Approach.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2017Article
- Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia.Frontiers in psychiatry · 2017Article
- CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.Frontiers in neuroinformatics · 2016Article
- Brain morphometric biomarkers distinguishing unipolar and bipolar depression. A voxel-based morphometry-pattern classification approach.JAMA psychiatry · 2014Article
- The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data.Frontiers in neuroinformatics · 2014Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional univariate statistics are common and widespread in neuroimaging research. However, functional and structural MRI data reveal a multivariate nature, since neighboring voxels are highly correlated and different localized brain regions activate interdependently. Multivariate pattern classification techniques are capable of overcoming shortcomings of univariate statistics. A rising interest in such approaches on neuroimaging data leads to an increasing demand of appropriate software and tools in this field. Here, we introduce and release MANIA-Machine learning Application for NeuroImaging Analyses. MANIA is a Matlab based software toolbox enabling easy pattern classification of neuroimaging data and offering a broad assortment of machine learning algorithms and feature selection methods. Between groups classifications are the main scope of this software, for instance the differentiation between patients and controls. A special emphasis was placed on an intuitive and easy to use graphical user interface allowing quick implementation and guidance also for clinically oriented researchers. MANIA is free and open source, published under GPL3 license. This work will give an overview regarding the functionality and the modular software architecture as well as a comparison between other software packages.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.