NIMH MEG Core Facility

MEG核心工作人员与NIMH、NINDS和NIDCD中的一大群PI互动工作，进行研究设计、任务开发、采集协议、信号处理和数据分析。数据安全、转移和储存程序已得到改进，将主要数据档案移到了HIMH维护的NIMH数据储存库。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，以前扩展到包括额外的维度格式，以促进跨学科组的基于时间的连接。 目前正在进一步扩大这一范围，以便能够在不同学科和群体之间进行时间方面的比较。 与事件相关的SAM（合成孔径磁力测量）和275通道伊卡（独立分量分析）的信号分析开发工作继续进行。时频分析方法的发展包括Stockwell变换和小波变换以及多锥度技术。一些用户团体正在试用分析包，这些分析包已经扩展到包括符号熵测量和传递熵互信息技术，以探索大脑网络。 一个早期测试版本的升级包，将取代外部电子产品已经过测试，并在所有渠道正常运行。我们正在等待软件包的完成和完全安装。新的眼动监测系统已经安装、测试并投入使用。 这些改进将大大提高系统的可靠性和使用寿命。 在早期的工作中，Brian Cornwell及其同事已经证明，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，当用氯胺酮治疗时，重度抑郁症患者的海马功能受损，以及其他大脑变化。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。这些研究对于可能阐明氯胺酮输注的抗惊厥作用机制特别有意义。先前的研究结果表明，在工作记忆任务中，前扣带回活动和功能连接的增加都可以预测氯胺酮的抗抑郁反应。Zarate及其同事利用MEG表明，突触增强对于难治性抑郁症的抗抑郁作用至关重要。 Nugent等人的新工作表明，MEG静息数据的组间差异突出了重度抑郁症的功能障碍。额外的新工作包括探索跨任务和患者群体的高伽马波段活动。 研究大脑如何将自身组织成功能网络是理解正常人类认知以及精神疾病中认知障碍的关键。此前，Bassett及其同事利用MEG的空间和时间能力发现，功能网络的特征是小世界特性，表明本地连接和远程连接的混合。我们还发现了患者组中静息网络模式的差异，现在已经使用图论方法来检查功能网络。在视听任务中已经研究了功能连接性指标，表明跨大脑区域的通信具有多种形式，并且没有一种措施足以捕获信息流的丰富性。 正在调查的新领域包括一个项目提案，将MEG动态与新的扩散张量成像（DTI）措施联系起来，以确定大脑区域的潜伏期。

The MEG Core staff works interactively with a large group of PI's in NIMH, NINDS and NIDCD for study design, task development, acquisition protocols, signal processing and data analysis. Procedures for data security, transfer and storage have been improved by moving the main data archive to the Helix maintained NIMH data store. Work with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis was previously extended to include an extra-dimensional format to facilitate time-based connectivity across subject groups. This is now being further expanded to allow time-dimension comparison across subjects and groups. Signal analysis development has continued on 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 that had been extended to include symbolic entropy measures and transfer entropy mutual information techniques to explore brain networks are now being piloted by a number of user groups. An early test version of the upgraded package that will replace the external electronics has been tested and performed properly across all channels. We are awaiting the completion of the package and full installation. The new eye-movement monitoring system has been installed, tested and is now in use. These improvements 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 treatment resistant major depression. New work by Nugent et al. has shown group differences in MEG resting data that highlight dysfunction in major depressive disorder. Additional new work includes exploration of high gamma band activity across task and patient groups. 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 found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. We have also found differences in resting network patterns in patient groups, and have now used graph theoretical methods to examine functional networks. Functional connectivity metrics have been studied in an audio-visual task demonstrating that the communication across brain regions takes on several forms and that no one measure will suffice to capture the richness of information flow. New areas under investigation include a project proposal to relate MEG dynamics to new Diffusion Tensor Imaging (DTI) measures for determining latency across brain regions.