NIMH MEG Core Facility

NIMH MEG 核心设施

• 批准号: 8940166
• 负责人: Richard Coppola
• 金额: $ 150.68万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8940166
• 关键词: Amygdaloid structure; Anterior; Antidepressive Agents; Auditory; Awareness; Behavioral; Brain; Chemosensitization; Clinical; Cognition; Cognitive; Complex; Computer software; Conscious; Core Facility; Data; Data Analyses; Data Collection; Data Security; Detection; Development; Disease; Dissociation; Electrodes; Electroencephalography; Electronics; Entropy; Equipment; Event; Experimental Designs; Eye Movements; Frequencies; Functional Magnetic Resonance Imaging; Graph; Head; Helmet; Hippocampus (Brain); Human; Information Systems; Infusion procedures; Instruction; Investigation; Journals; Ketamine; Learning; MRI Scans; Magnetoencephalography; Magnetometries; Major Depressive Disorder; Measures; Mental disorders; Methods; Mission; Modality; Modeling; Monitor; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; National Institute on Deafness and Other Communication Disorders; Neurosciences; Organism; Patients; Pattern; Perception; Performance; Positioning Attribute; Procedures; Property; Protocols documentation; Research Design; Resistance; Resolution; Rest; Scalp structure; Sensory; Short-Term Memory; Signal Transduction; Source; Statistical Computing; Stimulus; Structure; Surface; Synapses; System; Techniques; Technology; Testing; Time; Tissues; Training; Visual; Work; base; cranium; design; experience; improved; independent component analysis; interest; magnetic field; relating to nervous system; research study; response; scientific computing; sensor; signal processing; somatosensory; spatiotemporal; superconducting quantum interference device; tool

The MEG Core staff works interactively with an extensive group of PI's in NIMH, NINDS and NIDCD for study design, task development, acquisition protocols, signal processing and data analysis. Procedures have been setup for data security, transfer and storage. We have worked with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis. This has recently been extended to include an extra-dimensional format to faciltate time based connectivity across subject groups. The instant correlation feature of AFNI is being used to investigate connectivity pattern in MEG data. Other features of AFNI that allow extraction of brain surface meshes are being used to allow more detailed modeling of the MEG source signal. Signal analysis development includes event-related SAM (synthetic aperture magnetometry) and 275 channel ICA (independent component analysis). Development of time-frequency analysis methods has included Stockwell and wavelet transforms as well as multi-taper techniques. The analysis packages have been extended to include symbolic entropy measures and a transfer entropy mutual information technique to explore brain networks. We are waiting delivery of an upgraded package that will replace the external electronics as well as an improved eye-movement monitoring system. This will substantially improve reliability and substantially increase the useful life of the system. In earlier work Brian Cornwell and colleagues have demonstrated that MEG can reliably discriminate amygdala and hippocampal signals using MEG beamforming techniques. Continuing studies have shown that hippocampal function is impaired in patients with major depression as well as other brain changes when treated with ketamine. Recent work by Cornwell et al has shown that fast gamma activity in the hippocampus correlates with spatial learning. These studies are of particular interest to possibly elucidate the mechanism of the anti-depressant action of ketamine infusion. Previous results have shown that both increased anterior cingulate activity and functional connectivity during a working memory task can predict the antidepressant response of ketamine. Zarate and colleagues have utilized MEG to show that synaptic potentiation is critical for the antidepressant action in treat resistant major depression. Studying how the brain organizes itself into functional networks is key to understanding normal human cognition as well as when it becomes disordered in mental illness. Previously, Bassett and co-workers using the spatial and temporal ability of MEG to see changes of configuration during a task, found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. They extended this work to demonstrate that dysfuctional networks can be detected and related to behavioral differences in clinical groups. We have also found differences in resting network patterns in patient groups, and have now used graph theoretical methods to examine functional networks. The interest in 'resting activity' has continued with Dr Biyu He investigating scale free properties in functional networks. Further investigations of perception and conscious awareness have been pursued by several PIs. Using MEG He and colleagues (Li, Hill, He: Spatiotemporal Dissociation of Brain Activity Underlying Subjective Awareness, Objective Performance and Confidence, The Journal of Neuroscience, 19 March 2014, 34(12)) have shown that different activities underlay these cognitive phenomena.

MEG 核心工作人员与 NIMH、NINDS 和 NIDCD 的众多 PI 小组互动合作，进行研究设计、任务开发、采集协议、信号处理和数据分析。已经制定了数据安全、传输和存储的程序。我们与科学和统计计算核心合作，将 CTF MEG 文件传输到 AFNI，并开发了用于组统计分析的工具。最近，它已扩展到包括超维度格式，以促进跨学科组之间基于时间的连接。 AFNI 的即时关联功能用于研究 MEG 数据中的连接模式。 AFNI 允许提取大脑表面网格的其他功能可用于对 MEG 源信号进行更详细的建模。 信号分析开发包括事件相关的 SAM（合成孔径磁力测量）和 275 通道 ICA（独立分量分析）。时频分析方法的发展包括斯托克韦尔和小波变换以及多锥度技术。分析包已扩展到包括符号熵测量和转移熵互信息技术，以探索大脑网络。 我们正在等待交付升级包，该包将取代外部电子设备以及改进的眼动监测系统。这将显着提高可靠性并显着延长系统的使用寿命。 在早期的工作中，Brian Cornwell 及其同事证明，MEG 可以使用 MEG 波束形成技术可靠地区分杏仁核和海马信号。持续的研究表明，在接受氯胺酮治疗时，重度抑郁症患者的海马功能会受损，并且还会出现其他大脑变化。 Cornwell 等人最近的研究表明，海马体中的快速伽马活动与空间学习相关。这些研究对于阐明氯胺酮输注的抗抑郁作用机制特别感兴趣。先前的结果表明，工作记忆任务期间前扣带回活动和功能连接的增加可以预测氯胺酮的抗抑郁反应。 Zarate 及其同事利用 MEG 证明，突触增强对于治疗难治性重度抑郁症的抗抑郁作用至关重要。 研究大脑如何将自身组织成功能网络是理解人类正常认知以及精神疾病时认知紊乱的关键。此前，Bassett 和同事利用 MEG 的空间和时间能力来查看任务期间配置的变化，发现功能网络的特点是小世界属性，表明本地连接和远程连接的混合。他们扩展了这项工作，以证明功能障碍的网络可以被检测到并与临床群体的行为差异相关。我们还发现了患者组中静息网络模式的差异，并且现在使用图论方法来检查功能网络。随着 Biyu He 博士对功能网络中无标度特性的研究，人们对“休息活动”的兴趣仍在继续。 一些 PI 对感知和意识意识进行了进一步的研究。 He 及其同事使用 MEG（Li, Hill, He：主观意识、客观表现和信心下的大脑活动的时空分离，神经科学杂志，2014 年 3 月 19 日，34(12)）表明，不同的活动是这些认知现象的基础。