Enhancing 3dsvm to improve its interoperability and dissemination

增强 3dsvm 以提高其互操作性和传播

基本信息

项目摘要

DESCRIPTION (provided by applicant): This research plan outlines crucial software enhancements to a program called 3dsvm, which is a command line program and graphical user interface (gui) plugin for AFNI (Cox, 1996). 3dsvm performs support vector machine (SVM) analysis on fMRI data, which constitutes one important approach to performing multivariate supervised learning of neuroimaging data. 3dsvm originally provided the ability to analyze fMRI data as described in (LaConte et al., 2005). Since its first distribution as a part of AFNI, it has been steadily extended to provide new functionality including regression and non-linear kernels, as well as multiclass classification capabilities. In addition to its integration into AFNI, features that make 3dsvm particularly well suited for fMRI analysis are that it is easy to spatially mask voxels (to include/exclude them in the SVM analysis) as well as to flexibly select subsets of a dataset to use as training or testing samples. It has been used to generate results for our own work and for collaborative efforts and has been cited as a resource by others (Mur et al. 2009; Hanke et al. 2009). Despite many positive aspects of 3dsvm, the priorities of PAR-07-417 address a genuine need that this software project has - the ability to focus on improvements that will increase its dissemination and interoperability. A major motivation for PAR-07-417 is to facilitate the improved interface, characterization, and documentation to enhance the extent of sharing and to provide the groundwork for future extensions. Our aims are well aligned with this program announcement. Further, there is a growing need in the neuroimaging community for tools such as 3dsvm. Since 3dsvm is not a new project, is tightly integrated into the software environment of AFNI, and can be further integrated to enable better functionality to support needs as diverse as NIfTI format capabilities to rtFMRI, this proposed project will help to further the NIH Blueprint for Neuroscience Research by supporting its need for wide-spread adoption of high-quality neuroimaging tools. PUBLIC HEALTH RELEVANCE: This proposal focuses on improving, characterizing, and documenting an existing neuroinformatics software tool. The project described will help to further the NIH Blueprint for Neuroscience Research by supporting its need for wide-spread adoption of high-quality neuroimaging tools.
描述(由申请人提供):该研究计划概述了对称为3dsvm的程序的关键软件增强,该程序是用于Afni的命令行程序和图形用户界面(Gui)插件(cox,1996)。3DSVM对fMRI数据进行支持向量机分析,构成了对神经成像数据进行多变量监督学习的一种重要方法。3DSVM最初提供了分析fMRI数据的能力,如(LaConte等人,2005年)所述。自从它作为AFNI的一部分首次发行以来,它一直在稳步扩展,以提供包括回归和非线性内核在内的新功能,以及多类分类能力。除了集成到AFNI之外,使3DSVM特别适合于fMRI分析的特征是,很容易在空间上掩蔽体素(在支持向量机分析中包括/排除它们),以及灵活地选择数据集的子集作为训练或测试样本。它被用来为我们自己的工作和合作努力产生成果,并被其他人引用为一种资源(Mur等人。2009年;Hanke等人。2009年)。尽管3dsvm有许多积极的方面,但PAR-07-417的优先事项解决了该软件项目的一个真正需要--能够专注于将增加其传播性和互操作性的改进。PAR-07-417的一个主要动机是促进改进的接口、特征和文档,以增强共享的程度,并为未来的扩展提供基础。我们的目标与这项计划的宣布非常一致。此外,神经成像领域对3dsvm等工具的需求越来越大。由于3dsvm不是一个新项目,已紧密集成到Afni的软件环境中,并且可以进一步集成以实现更好的功能,以支持从Nifti格式到rtFMRI的各种需求,此拟议项目将支持NIH神经科学研究蓝图,支持其广泛采用高质量神经成像工具的需求。 公共卫生相关性:该提案侧重于改进、描述和记录现有的神经信息学软件工具。所描述的项目将通过支持NIH广泛采用高质量神经成像工具的需求,帮助进一步推动NIH神经科学研究蓝图。

项目成果

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STEPHEN M LACONTE其他文献

STEPHEN M LACONTE的其他文献

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{{ truncateString('STEPHEN M LACONTE', 18)}}的其他基金

Using real-time fMRI neurofeedback and motor imagery to enhance motor timing and precision in cerebellar ataxia
使用实时功能磁共振成像神经反馈和运动想象来增强小脑共济失调的运动计时和精度
  • 批准号:
    10354246
  • 财政年份:
    2021
  • 资助金额:
    $ 15.65万
  • 项目类别:
Using real-time fMRI neurofeedback and motor imagery to enhance motor timing and precision in cerebellar ataxia
使用实时功能磁共振成像神经反馈和运动想象来增强小脑共济失调的运动计时和精度
  • 批准号:
    10609494
  • 财政年份:
    2021
  • 资助金额:
    $ 15.65万
  • 项目类别:
Next generation Magnetoencephalography for human social neuroscience
用于人类社会神经科学的下一代脑磁图
  • 批准号:
    10224930
  • 财政年份:
    2020
  • 资助金额:
    $ 15.65万
  • 项目类别:
Next generation Magnetoencephalography for human social neuroscience
用于人类社会神经科学的下一代脑磁图
  • 批准号:
    10430081
  • 财政年份:
    2020
  • 资助金额:
    $ 15.65万
  • 项目类别:
Next generation Magnetoencephalography for human social neuroscience
用于人类社会神经科学的下一代脑磁图
  • 批准号:
    10632037
  • 财政年份:
    2020
  • 资助金额:
    $ 15.65万
  • 项目类别:
Temporally Adaptive fMRI
时间自适应功能磁共振成像
  • 批准号:
    7001247
  • 财政年份:
    2005
  • 资助金额:
    $ 15.65万
  • 项目类别:
Temporally Adaptive fMRI
时间自适应功能磁共振成像
  • 批准号:
    6854105
  • 财政年份:
    2005
  • 资助金额:
    $ 15.65万
  • 项目类别:
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