PRoNTo: Pattern Recognition for Neuroimaging Toolbox

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 11; no. 3; pp. 319 - 337
• Main Authors: Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., Mourão-Miranda, J.
• Format: Journal Article Web Resource
• Language: English
• Published: Boston Springer US 01.07.2013; Springer; Springer Nature B.V; Humana Press
• Subjects: Age Factors; Algorithms; Applied sciences; Artificial intelligence; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Biomedicine; Brain - physiology; Brain Mapping; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Computer science; control theory; systems; Computer Simulation; Engineering, computing & technology; Exact sciences and technology; Human health sciences; Humans; image analysis; Image Processing, Computer-Assisted; Ingénierie, informatique & technologie; Investigative techniques, diagnostic techniques (general aspects); Learning and adaptive systems; Likelihood Functions; machine learning; Medical sciences; Multivariate Analysis; multivariate pattern analysis; Nervous system; Neuroimaging; Neurology; Neurosciences; Original; Original Article; Pattern Recognition, Automated; Pattern recognition. Digital image processing. Computational geometry; Predictive Value of Tests; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Radionuclide investigations; Sciences de la santé humaine; Software; toolbox; Image analysis; Neuroimaging software; Pattern recognition; MVPA; Machine learning; Computer vision; Data analysis; Statistical analysis; Software development; Image processing; Cognition; Multivariate analysis; Modeling; Open source software; Encephalon; Statistical data; Cognitive theory; Neuroscience; Medical imagery; Pattern analysis; Artificial intelligence; Medical application

In the past years, mass univariate statistical analyses of neuroimaging data have been complemented by the use of multivariate pattern analyses, especially based on machine learning models. While these allow an increased sensitivity for the detection of spatially distributed effects compared to univ...