Analysis of Functional Magnetic Resonance Imaging in Python

By K. Jarrod Millman, Matthew Brett. 2007

79 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging, Neuroscience

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Citations in the corpus

Array programming with NumPy (2020, background): “2) builds on top of this foundation to provide several, widely used technique specific libraries [16, 17, 23], that in turn underlay numerous domain specific projects [24, 25, 26, 27, 28, 29].”
Machine learning for neuroimaging with scikit-learn (2014, background): “It provides efﬁcient implementations of state-of-the-art algorithms, accessible to non-machine learning experts, and reusable across scientiﬁc disciplines and application ﬁelds.”
A hitchhiker's guide to diffusion tensor imaging (2012, methodology): “On the other hand, Python based tools, especially the Nipy project (including tools such as Dipy , NiBabel , and Nipype ) are more ﬂexible and customizable, but less intuitive and user friendly (Millman and Brett, 2007).”
SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python (2019, unknown): “1.”
PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data (2009, methodology): “With the project Neuroimaging in Python (NIPY; Millman and Brett, 2007)) there is already an ongoing effort to provide a comprehensive software library for traditional fMRI data analysis.”
Developing Open-Source Scientific Practice * (2018, methodology): “For instance, we are both members of the Neuroimaging in Python [23] community in addition to participating in the more general parts of the scientific Python software stack.”
Teaching Computational Reproducibility for Neuroimaging (2018, methodology): “Rather than focusing on a specific neuroimaging analysis package, we taught scientific coding with Python and its associated scientific libraries [7, 12], and then showed the students how to run standard statistical procedures on imaging data using these tools [8].”