Simultaneous MEG and ULF MRI for Functional Imaging
同时进行 MEG 和 ULF MRI 进行功能成像
基本信息
- 批准号:7457722
- 负责人:
- 金额:$ 76.14万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2006
- 资助国家:美国
- 起止时间:2006-09-05 至 2010-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsAnatomyAreaAuditoryBrainBrain imagingCognitiveComputer softwareDataDevelopmentDimensionsDoctor of PhilosophyElectroencephalographyEpilepsyError SourcesFrequenciesFunctional ImagingFunctional Magnetic Resonance ImagingFutureGoalsHeadHumanImageLaboratoriesLeadLocalizedMagicMagnetic ResonanceMagnetic Resonance ImagingMagnetoencephalographyMeasurementMeasuresMethodsModalityModelingMotorNamesNuclearNuclear Magnetic ResonanceOperative Surgical ProceduresOutcomePathologyPhasePhysiologic pulsePredispositionProcessProtocols documentationPulse takingRelative (related person)Research PersonnelResolutionShapesSignal TransductionSliceSourceSpeedStructureSurfaceTechniquesThree-Dimensional ImagingVariantVisualWorkbasebrain tissuedensitydesignimprovedinstrumentinterestmagnetic fieldmillisecondneuroimagingnovel strategiesprogramsrelating to nervous systemresponsesensorsuperconducting quantum interference devicetool
项目摘要
DESCRIPTION (provided by applicant): We propose a new noninvasive modality for simultaneous functional and anatomical imaging of the human brain. This will be the first instrument ever to noninvasively measure both anatomical information and direct consequences of neural activity with high temporal resolution. We propose to combine magnetoencephalography (MEG) with ultra-low field magnetic resonance imaging (ULF-MRI). MEG (and electroencephalography - EEG) is unique in the ability to noninvasively measure neural activity in the brain with millisecond temporal resolution. ULF-MRI is an exciting new technique for structural imaging at magnetic fields in the Microtel regime. Signals for MEG and ULF-MRI are both measured by superconducting quantum interference device (SQUID) sensors. Hence MEG and ULF-MRI can be simultaneously acquired by the same sensor array. While MR signals at low field are drastically reduced relative to high-field (HF), the exquisite sensitivity of SQUID sensors and ULF-MRI techniques outlined here will more than compensate. This new functional/structural imaging modality will enhance neuroimaging of the human brain by reducing or eliminating numerous sources of error associated with co-registration (typically 5-10 mm or more), as the function and structure will be acquired with a single instrument. HF-MRI can be accurately co-registered to the MEG neural activity using HF and ULF surface renderings. Moreover, as ULF-MRI is free from distortions caused by susceptibility variations, anatomical surfaces acquired at ULF can be used to correct HF image distortions using existing warping algorithms. Correcting these distortions will improve MEG source localization by more accurately describing the shape of the head volume and the cortical surface (an important source constraint). Finally, acquisition of ULF-MRI by an array of SQUID sensors will enable faster image acquisition using MR sensor array techniques, techniques also being explored at HF. Simultaneous MEG/ULF-MRI will prove to be a powerful and general tool in the quest to understand the dynamic functional/structural relationships in the human brain, basically how and where the brain works. It will be used to study cognitive processes, for noninvasive localization of pathology such as epilepsy, and for determining surgical outcome (by localizing function in relation to pathology), to name a few. While MEG/ULF-MRI will provide a powerful new neuroimaging tool, it will open the door for exciting future developments. A three-fold modality including simultaneous ULF-MRI, MEG and EEG could readily be envisioned with dense array EEG. Ultimately, it has been speculated that NMR signatures of neural currents may eventually lead to unambiguous localization of neural activity. While unambiguous tomographic localization of neural activity appears possible, the temporal resolution will likely be no better than tens of milliseconds. ULF-MRI that may lead to direct imaging of neural activity, even with modest temporal resolution, can be used to constrain the simultaneously acquired MEG signal, providing millisecond or better temporal resolution.
描述(由申请人提供):我们提出了一种新的无创模式,用于同时进行人脑功能和解剖成像。这将是有史以来第一个无创测量解剖信息和神经活动直接后果的仪器,具有高时间分辨率。我们建议将脑磁图(MEG)与超低场磁共振成像(ULF-MRI)相结合。MEG(和脑电图- EEG)在以毫秒时间分辨率无创测量大脑神经活动的能力方面是独一无二的。超高频磁共振成像是一项令人兴奋的新技术,在磁场结构成像在Microtel政权。MEG和ULF-MRI的信号都是通过超导量子干涉装置(SQUID)传感器测量的。因此,可以通过相同的传感器阵列同时获取MEG和ULF-MRI。虽然相对于高场(HF),低场的MR信号大大减少,但这里概述的SQUID传感器和ULF-MRI技术的灵敏度将远远超过补偿。这种新的功能/结构成像方式将通过减少或消除与共配准相关的众多误差来源(通常为5-10毫米或更大)来增强人脑的神经成像,因为功能和结构将通过单一仪器获得。HF- mri可以使用HF和ULF表面渲染准确地共同注册到MEG神经活动。此外,由于ULF- mri不存在由敏感性变化引起的畸变,因此在ULF获得的解剖表面可以使用现有的翘曲算法来纠正HF图像畸变。纠正这些扭曲将通过更准确地描述头部体积和皮质表面的形状(一个重要的源约束)来改善MEG源定位。最后,通过SQUID传感器阵列获取ULF-MRI将使使用MR传感器阵列技术更快地获取图像,该技术也正在高频探索中。同时进行MEG/ULF-MRI将被证明是一种强大而通用的工具,可以帮助我们了解人类大脑的动态功能/结构关系,以及大脑的工作方式和位置。它将用于研究认知过程,用于非侵入性的病理定位,如癫痫,以及用于确定手术结果(通过与病理相关的功能定位),仅举几例。虽然MEG/ULF-MRI将提供一种强大的新神经成像工具,但它将为令人兴奋的未来发展打开大门。密集阵列脑电图可以很容易地设想一种三重模式,包括同时进行ULF-MRI, MEG和EEG。最终,据推测,神经电流的核磁共振特征可能最终导致神经活动的明确定位。虽然明确的神经活动断层扫描定位似乎是可能的,但时间分辨率可能不会超过几十毫秒。ULF-MRI可以直接成像神经活动,即使具有适度的时间分辨率,也可以用于约束同时获取的MEG信号,提供毫秒或更好的时间分辨率。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
ROBERT H KRAUS其他文献
ROBERT H KRAUS的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('ROBERT H KRAUS', 18)}}的其他基金
Simultaneous MEG and ULF MRI for Functional Imaging
同时进行 MEG 和 ULF MRI 进行功能成像
- 批准号:
7284865 - 财政年份:2006
- 资助金额:
$ 76.14万 - 项目类别:
Simultaneous MEG and ULF MRI for Functional Imaging
同时进行 MEG 和 ULF MRI 进行功能成像
- 批准号:
7133816 - 财政年份:2006
- 资助金额:
$ 76.14万 - 项目类别:
IMAGE SURFACE SENSOR ARRAY FOR BIOMAGNETIC MEASUREMENTS
用于生物磁测量的图像表面传感器阵列
- 批准号:
6323558 - 财政年份:1992
- 资助金额:
$ 76.14万 - 项目类别:
IMAGE SURFACE SENSOR ARRAY FOR BIOMAGNETIC MEASUREMENTS
用于生物磁测量的图像表面传感器阵列
- 批准号:
6393518 - 财政年份:1992
- 资助金额:
$ 76.14万 - 项目类别:
IMAGE SURFACE SENSOR ARRAY FOR BIOMAGNETIC MEASUREMENTS
用于生物磁测量的图像表面传感器阵列
- 批准号:
6188131 - 财政年份:1992
- 资助金额:
$ 76.14万 - 项目类别:
IMAGE SURFACE SENSOR ARRAY FOR BIOMAGNETIC MEASUREMENTS
用于生物磁测量的图像表面传感器阵列
- 批准号:
2756868 - 财政年份:1992
- 资助金额:
$ 76.14万 - 项目类别:
相似海外基金
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 76.14万 - 项目类别:
Research Grant














{{item.name}}会员




