NIMH MEG Core Facility

MEG核心员工与NIMH、NINDS和NIDCD中广泛的PI小组互动工作，以进行研究设计、任务开发、采集协议、信号处理和数据分析。已经为数据安全、传输和存储建立了程序。我们与科学和统计计算核心合作，使CTF MEG文件能够传输到非洲非政府组织，并开发了用于群体统计分析的工具。这最近已扩展到包括一个额外维度的格式，以便于跨受试者群体的基于时间的连接。AFNI的即时关联特性正被用于研究脑磁图数据中的连接模式。AFNI的其他功能允许提取大脑表面网格，以允许对脑磁源信号进行更详细的建模。 信号分析的发展包括与事件相关的SAM(合成孔径磁学)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变换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。 我们正在等待一个升级的包裹的交付，它将取代外部电子设备以及改进的眼动监测系统。这将大大提高可靠性，并大大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。康威尔等人最近的研究表明，海马区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抑郁症的抗抑郁作用至关重要。 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，Bassett和他的同事利用脑磁图的时空能力来观察任务过程中结构的变化，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。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，并与临床群体中的行为差异有关。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。随着何碧玉博士对功能网络中的无标度性质的研究，人们对“静息活动”的兴趣一直在继续。 几个私人投资机构对知觉和意识意识进行了进一步的研究。利用Meg He和他的同事(Li，Hill，He：大脑活动在主观意识、客观表现和信心下的时空分离，《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。

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.