NIMH MEG Core Facility

MEG核心员工与NIMH、NINDS和NIDCD的一大批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。通过将主数据档案移至Helix维护的NIMH数据存储，改进了数据安全、传输和存储程序。与科学和统计计算核心合作，以便能够将CTF MEG文件传输到非洲非政府组织，并开发了群体统计分析工具，以前已扩展到包括额外的维度格式，以促进主题群体之间基于时间的联系。现在，这一范围正在进一步扩大，以便能够跨受试者和群体进行时间维度的比较。 信号分析在与事件有关的SAM(合成孔径磁学)和275通道ICA(独立分量分析)方面继续发展。时频分析方法的发展包括斯托克韦尔变换和小波变换以及多锥度技术。已经扩展到包括符号熵测量和转移熵互信息技术的分析包，以探索大脑网络，现在正由一些用户群体进行试点。 将替换外部电子设备的升级包的早期测试版本已在所有渠道进行了测试并正常运行。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、测试并正在使用中。这些改进将大大提高可靠性，并大幅延长系统的使用寿命。 在早期的工作中，Brian Cornwell和他的同事已经证明了MEG可以使用MEG波束形成技术可靠地区分杏仁核和海马区的信号。持续的研究表明，在接受氯胺酮治疗时，患有严重抑郁症和其他脑部变化的患者的海马体功能会受到损害。康威尔等人最近的工作。研究表明，海马体中的快速伽马活动与空间学习相关。这些研究对可能阐明氯胺酮输注的抗抑郁作用的机制特别有意义。先前的研究结果表明，在工作记忆任务中，前扣带回活动的增加和功能连接的增加都可以预测氯胺酮的抗抑郁反应。Zarate和他的同事利用脑磁图表明，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。Nugent等人的新工作。显示了脑磁图休息数据的组间差异，这些数据突显了严重抑郁障碍的功能障碍。其他新工作包括探索跨任务组和患者组的高伽马频段活动。 研究大脑如何将自己组织成功能网络，是理解正常人类认知以及精神疾病中大脑何时变得混乱的关键。此前，巴西特和他的同事利用脑磁图的空间和时间能力发现，功能性网络的特征是小世界属性，表明既有本地连接，也有远程连接。我们还发现了患者群体中休息网络模式的差异，现在已经使用图论方法来检查功能网络。功能连通性度量在视听任务中进行了研究，表明跨大脑区域的交流有几种形式，没有一种衡量标准足以捕捉信息流的丰富性。 正在研究的新领域包括一个项目提案，将脑磁图动力学与新的扩散张量成像(DTI)测量相关联，以确定跨大脑区域的潜伏期。

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.