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Continued Development And Maintenance Of The Neuroimaging In Python Project

Mark T Desposito, Professor University Of California Berkeley

Grant 5R01MH081909-02 from National Institute Of Mental Health, IRG: ZRG1

Abstract: This grant proposes the continued maintenance, testing, and evaluation of the Neuroimaging in Python (NiPy) project. In particular, we propose to apply best practices and proven methods for software design, construction, and implementation to extend the applicability of NiPy to the larger neuroimaging research community. By addressing the parallel achievements and increased interdependence of neuroimaging research and computing sciences, we will benefit the existing NiPy user community as well as increasing the potential for NiPy to attract significantly more developers and users. We will modernize, refactor, and further develop NiPy into an easy to modify and extend software environment with the ability to be repaired and evolved as the needs of the community of users change. Working closely with the existing NiPy developer community, we aim to improve and enhance NiPy in terms of infrastructure, architecture, interoperability, usability, and reproducibility

Keywords: computer program /software, computer system design /evaluation, neuroimaging computer assisted diagnosis, computer human interaction, handbook, image processing, interdisciplinary collaboration, statistics /biometry bioimaging /biomedical imaging, magnetic resonance imaging, positron emission tomography

Project start date: 2007-07-10

Project end date: 2010-03-31

Dissemination of cross-platform software for artifact detection and region of interest analysis of functional imaging data

Satrajit S Ghosh, Research Scientist Susan Whitfield-Gabrieli, Research Scientist Massachusetts Institute of Technology


Abstract: The general aim of this project is to disseminate software
that will enhance the quality and consistency of analysis of functional magnetic resonance imaging (fMRI) data. The goal is to enhance, document and make publicly available software for artifact detection, statistical region-of-interest analysis and visualization of fMRI data. Better quality control methods and statistical methods will generate more credible and repeatable results, which should therefore lead to faster biomedical discoveries and to potential reduction in the cost of running fMRI studies. From a software engineering standpoint, the goal is to offer a well-designed, cross- platform, extensible software that is intuitive and easy to use.
Custom modules will be created for use of this software within some functional
analysis streams (FMRIB Software Library, FSL, Smith et al., 2004, Statistical Parametric Mapping, SPM, Friston 2003, FreeSurfer Functional Analysis STream, FSFAST, Tsao et al., 2003 and Neuroimaging in Python, NiPy), and support will be provided to embed the software in other analysis streams.
PUBLIC HEALTH RELEVANCE: The proposed project aims to
disseminate software for sophisticated statistical analyses (Nieto-Castanon et al., 2003) and quality control of functional magnetic resonance imaging (fMRI) data. Providing these tools should enable users of fMRI technology to produce more detailed, consistent and reliable results. This will lead to better understanding of how the brain works and thereby directly impact approaches to diagnosing and treating neurological disorders.
Keywords: computer program /software, computer system design
/evaluation, region of interest analysis, cross-platform/cross-package software

Project start date: 2007-07-01

Project end date: 2010-04-30