A Flexible Architecture for Neuroimaging Data Analysis

用于神经影像数据分析的灵活架构

• 批准号: 6747735
• 负责人: DANIEL Y KIMBERG
• 金额: $ 35.66万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2006-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6747735
• 关键词: bioimaging /biomedical imaging; cognition; computer program /software; computer system design /evaluation; data management; functional magnetic resonance imaging; human subject; neural information processing; neuroimaging; performance; bioimaging /biomedical imaging; cognition; computer program /software; computer system design /evaluation; data management; functional magnetic resonance imaging; human subject; neural information processing; neuroimaging; performance

DESCRIPTION (provided by applicant): The emergence of functional neuroimaging as a tool for cognitive neuroscience remains promising, yet is hampered by a number of technological obstacles. These obstacles can preclude collaboration, slow research, dictate experimental designs, encourage errors, and lead to the widespread unnecessary duplication of effort. In the present proposal, we describe software solutions to several of these obstacles, embedded in a flexible architecture for neuroimaging data analysis and presentation. For users of neuroimaging processing and analysis software, these tools will facilitate the interoperation of disparate software tools and data formats (thereby reducing the commitment in selecting a particular software package), improve computational efficiency, and aid in the design and analysis of functional neuroimaging experiments. For developers of neuroimaging methods, they will provide a framework for development of future neuroimaging tools. In concert with this, we also propose a number of discrete improvements to the available tools for experimental design and analysis, intended to improve existing software in terms of both generality and overall functionality. We also propose a project aimed at understanding the neural basis of executive control. The task-switching paradigm may be said to operationalize the concept of executive control, in that it requires subjects to willfully switch between two metacognitive sets. We here propose two studies aimed at understanding the neural correlates of different facets of task switching performance, intended to address the question of a distinct central executive. At the same time, the parametric nature of task switching experiments makes it easy (conceptually, if not practically) to assess a form of generality for any observed neural effect. This type of project, which essentially considers the paradigm itself a random effect, is facilitated jointly by the work presented here.

描述（由申请人提供）：功能神经影像的出现 作为认知神经科学的工具，仍然有望 技术障碍的数量。这些障碍可以排除协作， 缓慢的研究，决定实验设计，鼓励错误并导致 广泛的不必要的努力重复。在本提案中，我们 描述其中几个障碍的软件解决方案，并嵌入 用于神经影像学数据分析和呈现的灵活体系结构。为了 神经成像处理和分析软件的用户，这些工具将 促进不同的软件工具和数据格式的互操作 （从而减少选择特定软件包的承诺）， 提高计算效率，并有助于设计和分析 功能性神经成像实验。对于神经成像方法的开发者， 他们将为开发未来神经影像学工具提供一个框架。在 与此合作，我们还提出了许多离散的改进 可用于实验设计和分析的可用工具，旨在改进 从一般性和整体功能方面的现有软件。 我们还提出了一个旨在理解执行神经基础的项目 控制。可以说，任务切换范式可以实现该概念 执行控制，因为它要求受试者在 两个元认知集。我们在这里提出了两项​​旨在了解的研究 任务切换性能的不同方面的神经相关性，意图 解决一个独特的中央主管的问题。同时， 任务切换实验的参数性质使其变得容易（从概念上讲，如果 不实际）评估任何观察到的神经效应的普遍性形式。 这种类型的项目，本质上考虑范式本身是随机的 效果由此处介绍的工作共同促进。