NIMH MEG Core Facility

我们有超过50个用户帐户代表众多NIMH，NINDS和NIDCD协议。正在进行议定书和规划研究的其他团体不断表示兴趣。 MEG核心工作人员一直与这些用户在研究设计、任务编程开发、采集协议以及信号处理和数据分析方面进行互动。已建立数据安全、传输和存储程序。已经制定了一份实质性的政策和程序手册。我们还与科学和统计计算核心合作，将CTF MEG文件传输到AFNI，并开发了工具， 分组统计分析。 技术和科学成果非常出色。信号分析的发展包括事件相关的SAM（合成孔径磁强计）和275通道伊卡（独立分量分析）。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。特别感兴趣的是虚拟通道的相干性分析作为一种方法来研究相互作用的大脑区域。 工作人员正在与其他MEG小组合作，整合几个信号处理软件包，包括FieldTrip，NUTMEG和BrainStorm。目标是为用户社区提供一个统一的工具包，并提供一个用户友好的界面。SAM软件已成功地在Biowulf集群上运行，从而大大提高了计算能力。Octave已跨群集安装，以便允许基于MATLAB脚本的数据处理。 定位不仅皮质表面源，但更深的结构的能力已被证明。 对于工作记忆任务，MEG β带的激活模式与功能性磁共振成像（fMRI）结果在同一受试者组中显示出异常一致。在功能磁共振成像任务中，工作记忆中的双侧DLPFC（背外侧前额叶皮层）和后顶叶皮层的网络与β去激活模式高度一致。阿尔塔穆拉等人的研究表明，在前额叶活动的调节中可以看到预期信号，这些信号似乎是在为即将到来的任务需求做准备时产生的。 Qian Lou和James Blair在早期的一项研究中考察了面部威胁处理的神经动力学，他们能够利用MEG的精细时间分辨率来了解杏仁核中的大脑相关反应甚至比视觉皮层更早。这支持了大脑对恐惧表情进行快速处理的建议。了解这些脑机制对于进一步研究心境和情感障碍具有重要意义。他们随后调查了视觉意识和伽马波段信号的关系。最近，他们发现杏仁核早期的反应是自动的，而后期的活动则受制于注意力负荷。其他人的新研究发现，当韵律和情感表达交织在一起时，联想区域会发生变化。 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。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。最近的研究结果表明，增加前扣带活动可能是一个生物标志物，预测快速抗抑郁反应氯胺酮。Salvadore和Cornwell现在进一步发现，工作记忆任务中的功能连接可以预测ketemine的抗抑郁反应。有评论认为，“精神压力测试”可能成为转化心理药理学的一种策略。 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。为此，Bassett及其同事使用MEG的空间和时间能力来研究大脑在运动任务期间与休息时相比如何改变配置。他们发现，功能性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。他们继续这项工作，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有关。Rutter等人还发现了患者组中静息网络模式的差异。对“静息活动”的持续关注促使了几项额外的MEG研究。Duyn及其同事（Liu，Fukunga，de Zwart，Duyn，NeuroImage 2010）已经表明，在fMRI中看到的大尺度波动与MEG在脑电活动中看到的波动相关。

We have over 50 user accounts representing numerous NIMH, NINDS and NIDCD protocols. There is continual interest from additional groups that are working on protocols and planning studies. The MEG Core staff has been working interactively with these users in terms of study design, task programming development, acquisition protocols, and signal processing and data analysis. Procedures have been setup for data security, transfer and storage. A substantial Policy and Procedures Manual has been established. We have also worked with the Scientific and Statistical Computing Core to enable transfer of CTF MEG files to AFNI and developed tools for group statistical analysis. Technical and scientific results have been excellent. 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. Of particular interest is coherence analysis of virtual channels as a method to investigate interacting brain regions. Staff are working with other MEG groups to integrate several signal processing packages including FieldTrip, NUTMEG and BrainStorm. The goal is to have a unified tool package with a user-friendly interface available to the user community. The SAM software has been successfully run on the Biowulf Cluster allowing for tremendous increase in computing power. Octave has ben installed across the cluster in order to allow for MATLAB script based processing of data. The ability to localize not only cortial surface sources but deeper structures has been demonstrated. For a working memory task MEG activation patterns for beta band have shown exceptional agreement with fMRI (functional magnetic resonance imaging) results in the same subject group. Beta desynchronization patterns agree highly with the network of bilateral DLPFC (dorsolateral prefrontal cortex) and posterior parietal cortex seen during working memory in fMRI tasks. Altamura et al have shown that there are anticipatory signals seen in the modulation of prefrontal activity that appear to arise from preparation for upcoming task demands. Qian Lou and James Blair in an earlier study examining the neural dynamics of facial threat processing have been able to utilize the fine temporal resolution of MEG to learn that there are brian related responses in the amygdala even earlier than in the visual cortex. This supports the suggestion of a quick processing route in the brain specific to fear expressions. Understanding these brain mechanisms will be important to further study in mood and affective disorders. They have followed with an investigation of the relation of visual awareness and gamma band signals. More recently they have shown that early amygdala response was automatic whereas later activity was subject to attentional load. Newer work by others has found changes in association areas when prosody and emotional expressiond are interwoven. 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. These studies are of particular interest to possibly elucidate the mechanism of the anti-depressant action of ketamine infusion. Recent results have shown that increased anterior cingulate activity may be a biomarker that predicts the rapid antidepressant response to ketamine. Salvadore and Cornwell have now further found that functional connectivity during a working memory task can predict the antidepressant response of ketemine. A commentary has suggested that 'psychiatric stress testing' may become a strategy for translational psychphamacology. 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. To this end Bassett and co-workers used the spatial and temporal ability of MEG to study how the brain changes configuration during a motor task compared to when at rest. They found that functional networks were characterized by small-world properties indicating a mix of both local connections and long range connections. They have continued this work to demonstrate that dysfuctional networks can be detected and related to behavioral differences in clinial groups. Rutter et al a have also found differences in resting network patterns in patient groups. The continued interest in 'resting activity' has spurred several addtional MEG studies. Duyn and colleagues (Liu,Fukunga,de Zwart, Duyn, NeuroImage 2010) have shown that the large scale fluctuations seen in fMRI have correlates with that seen in brain electrical activity by MEG.