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Erschienen in:

01.03.2009

PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data

verfasst von: Michael Hanke, Yaroslav O. Halchenko, Per B. Sederberg, Stephen José Hanson, James V. Haxby, Stefan Pollmann

Erschienen in: Neuroinformatics | Ausgabe 1/2009

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Abstract

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python’s ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

Vorheriger Artikel A 3D Model of Human Cerebrovasculature Derived from 3T Magnetic Resonance Angiography

Nächster Artikel Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Resource Announcement

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In the literature, authors have referred to the application of machine learning techniques to neural data as decoding (Kamitani and Tong 2005; Haynes et al. 2007), information-based analysis (e.g. Kriegeskorte et al. 2006) or multi-voxel pattern analysis (e.g. Norman et al. 2006). Throughout this article we will use the term classifier-based analysis to refer to all these methods.

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Given that the results reported are from a single participant, we are simply illustrating the capabilities of PyMVPA, not trying to promote any analysis method as more-effective than another.

Note that PyMVPA internally makes use of a number of other aforementioned Python modules, such as NumPy and SciPy.

To a certain degree PyMVPA also supports importing ANALYZE files.

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Nothing prevents a software developer from adding a GUI to the toolbox using one of the many GUI toolkits that interface with Python code, such as PyQT (http://www.riverbankcomputing.co.uk/software/pyqt/) or wxPython (http://www.wxpython.org/).

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Titel: PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data
verfasst von: Michael Hanke
Yaroslav O. Halchenko
Per B. Sederberg
Stephen José Hanson
James V. Haxby
Stefan Pollmann
Publikationsdatum: 01.03.2009
Verlag: Humana Press Inc
Erschienen in: Neuroinformatics / Ausgabe 1/2009
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-008-9041-y

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