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; 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核心员工与NIMH，NINDS和NIDCD的一组PI互动合作，用于研究设计，任务编程开发，获取协议以及信号处理和数据分析。已经为数据安全，传输和存储设置了过程。已经建立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，使CTF MEG文件可以转移到AFNI，并开发了用于组统计分析的工具。这项工作已扩展到利用AFNI的即时相关功能来研究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（合成孔径磁力测定法）和275通道ICA（独立的组件分析）。时频分析方法的开发包括Stockwell和小波变换以及多型技术。特别感兴趣的是对虚拟通道的连贯分析作为研究相互作用大脑区域的一种方法。 员工正在与其他MEG组合作，集成了几个信号处理包，包括现场渡轮，肉豆蔻和头脑风暴。目标是拥有一个统一的工具包，其用户社区可用的用户友好界面。 SAM软件正在成功运行在Biowulf群集上（使用MEG Core员工安装的开源八度软件），允许计算能力大大提高。罗宾逊博士已向MEG用户社区推出了新的时间切片和基于熵的源分析方法。 罗宾逊博士将复杂性措施扩展到MEG时间序列数据，不仅在IRP调查人员中，而且从世界各地的MEG实验室中都引起了人们的极大兴趣。图宾根大学，诺丁汉大学，阿斯顿大学和伊兰的大脑研究中心的小组在各自的研究中都专门研究了这些新方法。前MEG核心使用者Qian Luo现在位于圣路易斯MEG中心，正在TBI，创伤性脑损伤的患者申请，这是一种特别可能是内孢子的生物标志物AEA。 已经证明了不仅可以定位皮质表面来源而且更深的结构的能力。 对于工作记忆任务，Beta频段的MEG激活模式显示了与fMRI（功能性磁共振成像）的非凡一致性导致同一主题组。 beta脱离同步模式与双侧DLPFC（背外侧前额叶皮层）的网络高度一致，而在功能磁共振成像任务中工作记忆期间可见的后顶叶皮层。 Altamura等人表明，在对额叶活动的调节中看到的预期信号似乎是由于为即将到来的任务需求做准备而产生的。 在较早的工作中，布莱恩·康威尔（Brian Cornwell）及其同事证明，梅格（Meg）可以使用MEG束构成技术可靠地区分杏仁核和海马信号。持续研究表明，用氯胺酮治疗时，严重抑郁症以及其他大脑变化的患者海马功能受损。这些研究尤其令人感兴趣，可能阐明氯胺酮输注抗抑郁作用的机制。先前的结果表明，前扣带回活性的增加可能是一种生物标志物，可以预测对氯胺酮的快速抗抑郁反应。萨尔瓦多（Salvadore）和康威尔（Cornwell）发现，在工作记忆任务中的功能连通性可以预测酮胺的抗抑郁反应。评论表明，“精神病压力测试”可能成为转化心理学的策略。这项工作继续在Zarate博士等基于药理学的研究中继续进行。 Cormwell博士扩大了他在空间导航方面的早期工作，并发现MEG记录的海马theta活动在空间认知和焦虑中的特定相互作用。 研究大脑如何将自己组织到功能网络中是了解正常人类认知以及在精神疾病中变得无序的关键。为此，巴塞特（Bassett）和同事使用梅格（MEG）的空间和时间能力来研究与休息时间相比，在运动任务期间大脑在运动任务期间的构型如何变化。他们发现功能网络的特征是小世界属性，表明本地连接和远距离连接的混合。他们继续进行这项工作，以证明可以检测到功能障碍网络并与临床组的行为差异有关。我们还发现患者群体中的静止网络模式有所不同。对“休息活动”的持续兴趣刺激了几项额外的MEG研究。一位新的Ninds研究员Biyu He博士已经开始研究在休息和任务期间分布的功能成像中的无标度性能。还可以使用这些方法研究功能性脑网络的重组。 Horwitz博士及其NIDCD组扩展了大规模神经模型，以检查可以反映Milliseciond分辨率皮质动力学的连接度量。 Braun博士的小组已使用MEG来研究语言理解中句法理解的模式，以此作为语音和语言中断的应用。