NIMH MEG Core Facility

NIMH MEG 核心设施

• 批准号: 8557116
• 负责人: Richard Coppola
• 金额: $ 126.6万
• 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/8557116
• 关键词: Agreement; Amygdaloid structure; Anterior; Antidepressive Agents; Anxiety; Auditory; Behavioral; Bilateral; Biological Markers; Brain; Brain region; Cognition; Cognitive; Communities; Complex; Comprehension; Computer software; Core Facility; Data; Data Analyses; Data Collection; Data Security; Detection; Development; Disease; Electrodes; Electroencephalography; Entropy; Equipment; Event; Experimental Designs; Frequencies; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Head; Helmet; Hippocampus (Brain); Human; Information Systems; Infusion procedures; Instruction; Ketamine; Laboratories; Language; MRI Scans; Magnetoencephalography; Magnetometries; Major Depressive Disorder; Manuals; Measures; Mental disorders; Methods; Mission; Modality; Modeling; Motor; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; National Institute on Deafness and Other Communication Disorders; Parietal Lobe; Patients; Pattern; Pharmacology; Policies; Positioning Attribute; Prefrontal Cortex; Preparation; Procedures; Program Development; Property; Protocols documentation; Published Comment; Research Design; Research Personnel; Resolution; Rest; Running; Scalp structure; Sensory; Series; Short-Term Memory; Signal Transduction; Slice; Source; Speech; Statistical Computing; Stimulus; Stress Tests; Structure; Surface; System; Techniques; Technology; Testing; Time; Tissues; Training; Traumatic Brain Injury; Universities; Visual; Work; base; brain research; computerized data processing; cranium; design; experience; independent component analysis; interest; magnetic field; neural model; open source; relating to nervous system; research study; response; scientific computing; sensor; somatosensory; superconducting quantum interference device; syntax; tool; user-friendly; virtual; way finding

The MEG Core staff works interactively with an extensive group of PI's in NIMH, NINDS and NIDCD for 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. This work has been extended to utilize the instant correlation feature of AFNI to investigate connectivty pattern in MEG data. 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 is being successfully run on the Biowulf Cluster (utilizing the open source OCTAVE software installed by MEG Core staff)allowing for tremendous increase in computing power. Dr Robinson has introduced new time slice and entropy based source analysis methods to the MEG user community. Dr Robinson's extion of complexity measures to MEG time series data has received considerable interest not only among IRP investigators but from MEG laboratories around the world. Groups at the University of Tubingen, the University of Nottingham, Aston University and the Brain Research Center at Bar Ilan are specially investigating these new methods in their respective studies. A former MEG Core user, Qian Luo, now at the St Louis MEG center, is applying to patients with TBi, Traumatic Brain Injury, an especially very potentially inporant biomarker aea. 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. 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. previous results have shown that increased anterior cingulate activity may be a biomarker that predicts the rapid antidepressant response to ketamine. Salvadore and Cornwell have 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. This work continues in several treatment and pharmacology based studies by Dr Zarate and others. Dr Cormwell has extended his early work on spatial navigation and has found specific interactions of MEG recorded hippocampal theta activity in spatial cognition and anxiety. 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. We have also found differences in resting network patterns in patient groups. The continued interest in 'resting activity' has spurred several addtional MEG studies. A new NINDS investigator, Dr Biyu He, has begun studies of scale free properties in functional imaging distinquished at rest and during tasks. Reorganization of functional brain networks can also be investigated using these methods. Dr Horwitz and his NIDCD group have extended large scale neural models to examine connectiivty measures that can reflect cortical dynamics at milliseciond resolution. Dr Braun's group has used MEG to examine patterns of syntactic comprehension in language comprehension as an an application to speech and language disruptions.

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、任务编程开发、采集协议以及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。已经制定了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，并开发了用于群体统计分析的工具。这项工作已扩展到利用 AFNI 的即时关联功能来研究 MEG 数据中的连接模式。 技术和科学成果非常出色。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。特别令人感兴趣的是虚拟通道的一致性分析，作为研究相互作用的大脑区域的方法。 工作人员正在与其他 MEG 团队合作，集成多个信号处理包，包括 FieldTrip、NUTMEG 和 BrainStorm。目标是为用户社区提供一个具有用户友好界面的统一工具包。 SAM 软件正在 Biowulf 集群上成功运行（利用 MEG Core 员工安装的开源 OCTAVE 软件），从而大大提高了计算能力。 Robinson 博士向 MEG 用户社区介绍了新的基于时间片和熵的源分析方法。 Robinson 博士将复杂性度量扩展到 MEG 时间序列数据不仅引起了 IRP 研究人员的极大兴趣，而且引起了世界各地 MEG 实验室的极大兴趣。图宾根大学、诺丁汉大学、阿斯顿大学和巴伊兰大脑研究中心的研究小组正在各自的研究中专门研究这些新方法。前 MEG Core 用户，Qian Luo，现在在圣路易斯 MEG 中心，正在应用于 TBi（创伤性脑损伤）患者，这是一种特别非常有可能重要的生物标志物。 不仅可以定位皮质表面源，还可以定位更深层次的结构。 对于工作记忆任务，β 带的 MEG 激活模式与同一受试者组的 fMRI（功能性磁共振成像）结果表现出异常一致。 Beta 去同步模式与功能磁共振成像任务中工作记忆中看到的双侧 DLPFC（背外侧前额叶皮层）和后顶叶皮层网络高度一致。 Altamura 等人表明，在前额叶活动的调节中可以看到预期信号，这些信号似乎是为即将到来的任务需求做准备而产生的。 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。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，前扣带回活动增加可能是预测氯胺酮快速抗抑郁反应的生物标志物。萨尔瓦多和康威尔发现工作记忆任务期间的功能连接可以预测氯胺酮的抗抑郁反应。一篇评论建议“精神压力测试”可能成为转化精神药理学的一种策略。 Zarate 博士和其他人在多项基于治疗和药理学的研究中继续这项工作。 Cormwell 博士扩展了他在空间导航方面的早期工作，并发现 MEG 记录的海马 θ 活动在空间认知和焦虑方面的特定相互作用。 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。为此，巴塞特和同事利用脑磁图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相关。我们还发现患者群体的静息网络模式存在差异。对“静息活动”的持续兴趣激发了几项额外的脑磁图研究。 NINDS 的新研究员 Biyu He 博士已开始研究在休息和任务期间表现出的功能成像的无标度特性。功能性大脑网络的重组也可以使用这些方法进行研究。 Horwitz 博士和他的 NIDCD 小组扩展了大规模神经模型来检查可以以毫秒分辨率反映皮质动态的连接测量。 Braun 博士的小组使用 MEG 来检查语言理解中的句法理解模式，作为语音和语言中断的应用。