Improving the characterization of activity in white matter systems: links within distributed neural networks
改善白质系统活动的表征:分布式神经网络内的链接
基本信息
- 批准号:RGPIN-2015-04961
- 负责人:
- 金额:$ 2.91万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Understanding how the brain works is a rapidly advancing area of science and technology. The growing scientific and public interest in brain imaging advances makes perfect sense when considering that our most prized higher-level mental functions, like language and memory, rely on finely tuned, complex processing of brain circuits. These brain circuits exist as distributed networks that are dynamically activated as information is processed. In recent years, there have been major technological advances in our ability to non-invasively peer inside the brain and watch aspects of these networks process information - to visualize our brain at work as "activation." But, what does this activation mean?***My prior work was focused on seeking a better understanding of the concept of "activation" in distributed brain networks - a fundamental step towards learning more about higher mental functions. In the process, a missing link was identified. The brain is divided into two, approximately equal, types of tissues (gray matter and white matter); when considering distributed networks, gray matter can be thought of as the dots, with white matter as the lines connecting the dots. The missing link: brain imaging of "activation" sees only the dots, which effectively represents only 50% of the picture. Without being able to isolate the active lines that connect the dots, it is difficult, if not impossible, to identify these vital distributed networks.***The key objective of this proposal is to identify active connections (the lines) in white matter in order to better understand these dynamic networks. My research will capitalize on advancements in brain imaging, using functional magnetic resonance imaging (fMRI) and related MRI-based techniques. To date, our group has led in the use of fMRI to detect white matter activation in the brain. Through a series of experiments, we have demonstrated white matter activation in specific brain connections (primarily the corpus callosum). The next step will be to discover and characterize the optimal measurement of, and explore the underlying biological basis for, white matter activation. Ultimately, the goal is to help better identify brain networks, at work and at rest, that support vital higher-level mental functions. This fundamental research is then hoped to bring real-world applications in the neurological evaluation of brain dysfunction (e.g., multiple sclerosis and Alzheimer's Disease).***The anticipated outcome of this work is to improve our understanding of brain activation during high-level function and dysfunction. By improving our ability to visualize distributed brain networks, this research will lay the foundation for a host of future advances to help the one-third of all Canadians who are affected by brain-related conditions during their lives. ********
了解大脑是如何工作的是一个快速发展的科学和技术领域。考虑到我们最珍贵的高级心理功能,如语言和记忆,依赖于大脑回路的精细调整和复杂处理,科学和公众对大脑成像进展的兴趣日益增长是完全有道理的。这些大脑回路以分布式网络的形式存在,随着信息的处理而动态激活。近年来,我们在非侵入性地窥视大脑内部并观察这些网络处理信息的各个方面的能力方面取得了重大的技术进步-将我们的大脑工作可视化为“激活”。“不过,这个激活是什么意思?我之前的工作集中在寻求更好地理解分布式大脑网络中的“激活”概念-这是了解更高精神功能的基础一步。在这个过程中,发现了一个缺失的环节。大脑分为两种大致相等的组织(灰质和白色物质);当考虑分布式网络时,灰质可以被认为是点,白色物质是连接点的线。缺失的一环:大脑对“激活”的成像只看到了点,而点实际上只代表了画面的50%。如果不能隔离连接这些点的活动线路,就很难(如果不是不可能的话)识别这些重要的分布式网络。该提案的主要目标是识别白色物质中的活动连接(线),以便更好地理解这些动态网络。我的研究将利用脑成像的进步,使用功能性磁共振成像(fMRI)和相关的基于MRI的技术。到目前为止,我们小组已经率先使用功能磁共振成像来检测大脑中白色物质的激活。通过一系列的实验,我们已经证明了白色物质激活特定的大脑连接(主要是胼胝体)。下一步将是发现和表征白色物质激活的最佳测量方法,并探索其潜在的生物学基础。最终,我们的目标是帮助更好地识别工作和休息时支持重要的高级心理功能的大脑网络。这项基础研究有望在脑功能障碍的神经学评估中带来现实世界的应用(例如,多发性硬化症和阿尔茨海默病)。这项工作的预期成果是提高我们对高水平功能和功能障碍期间大脑激活的理解。通过提高我们可视化分布式大脑网络的能力,这项研究将为未来的一系列进步奠定基础,以帮助三分之一的加拿大人在生活中受到大脑相关疾病的影响。********
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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DArcy, Ryan其他文献
DArcy, Ryan的其他文献
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{{ truncateString('DArcy, Ryan', 18)}}的其他基金
Characterizing brain function changes during neuroplasticity within distributed neural systems
表征分布式神经系统中神经可塑性过程中大脑功能的变化
- 批准号:
RGPIN-2020-05419 - 财政年份:2022
- 资助金额:
$ 2.91万 - 项目类别:
Discovery Grants Program - Individual
Characterizing brain function changes during neuroplasticity within distributed neural systems
表征分布式神经系统中神经可塑性过程中大脑功能的变化
- 批准号:
RGPIN-2020-05419 - 财政年份:2021
- 资助金额:
$ 2.91万 - 项目类别:
Discovery Grants Program - Individual
Characterizing brain function changes during neuroplasticity within distributed neural systems
表征分布式神经系统中神经可塑性过程中大脑功能的变化
- 批准号:
RGPIN-2020-05419 - 财政年份:2020
- 资助金额:
$ 2.91万 - 项目类别:
Discovery Grants Program - Individual
Improving the characterization of activity in white matter systems: links within distributed neural networks
改善白质系统活动的表征:分布式神经网络内的链接
- 批准号:
RGPIN-2015-04961 - 财政年份:2019
- 资助金额:
$ 2.91万 - 项目类别:
Discovery Grants Program - Individual
Development of virtual reality cognitive training tasks using EEG
使用脑电图开发虚拟现实认知训练任务
- 批准号:
533151-2018 - 财政年份:2018
- 资助金额:
$ 2.91万 - 项目类别:
Engage Grants Program
Further development of support for handling neuroimaging data within Safe Software's FME
进一步开发对 Safe Software 的 FME 中处理神经影像数据的支持
- 批准号:
537195-2018 - 财政年份:2018
- 资助金额:
$ 2.91万 - 项目类别:
Engage Plus Grants Program
Improving the characterization of activity in white matter systems: links within distributed neural networks
改善白质系统活动的表征:分布式神经网络内的链接
- 批准号:
RGPIN-2015-04961 - 财政年份:2017
- 资助金额:
$ 2.91万 - 项目类别:
Discovery Grants Program - Individual
Development of support for handling neuroimaging data within Safe Software's fme
开发对 Safe Software 的 fme 内处理神经影像数据的支持
- 批准号:
519759-2017 - 财政年份:2017
- 资助金额:
$ 2.91万 - 项目类别:
Engage Grants Program
Improving the characterization of activity in white matter systems: links within distributed neural networks
改善白质系统活动的表征:分布式神经网络内的链接
- 批准号:
RGPIN-2015-04961 - 财政年份:2016
- 资助金额:
$ 2.91万 - 项目类别:
Discovery Grants Program - Individual
Integrating Target Tape technology in multimodal neuroimaging registration
将目标磁带技术集成到多模式神经影像配准中
- 批准号:
484657-2015 - 财政年份:2015
- 资助金额:
$ 2.91万 - 项目类别:
Engage Grants Program
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