NIMH MEG Core Facility

NIMH MEG 核心设施

• 批准号: 8940166
• 负责人: Richard Coppola
• 金额: $ 150.68万
• 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/8940166
• 关键词: Amygdaloid structure; Anterior; Antidepressive Agents; Auditory; Awareness; Behavioral; Brain; Chemosensitization; Clinical; Cognition; Cognitive; Complex; Computer software; Conscious; Core Facility; Data; Data Analyses; Data Collection; Data Security; Detection; Development; Disease; Dissociation; Electrodes; Electroencephalography; Electronics; Entropy; Equipment; Event; Experimental Designs; Eye Movements; Frequencies; Functional Magnetic Resonance Imaging; Graph; Head; Helmet; Hippocampus (Brain); Human; Information Systems; Infusion procedures; Instruction; Investigation; Journals; Ketamine; Learning; MRI Scans; Magnetoencephalography; Magnetometries; Major Depressive Disorder; Measures; Mental disorders; Methods; Mission; Modality; Modeling; Monitor; National Institute of Mental Health; National Institute of Neurological Disorders and Stroke; National Institute on Deafness and Other Communication Disorders; Neurosciences; Organism; Patients; Pattern; Perception; Performance; Positioning Attribute; Procedures; Property; Protocols documentation; Research Design; Resistance; Resolution; Rest; Scalp structure; Sensory; Short-Term Memory; Signal Transduction; Source; Statistical Computing; Stimulus; Structure; Surface; Synapses; System; Techniques; Technology; Testing; Time; Tissues; Training; Visual; Work; base; cranium; design; experience; improved; independent component analysis; interest; magnetic field; relating to nervous system; research study; response; scientific computing; sensor; signal processing; somatosensory; spatiotemporal; superconducting quantum interference device; tool

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.

MEG核心员工与NIMH，NINDS和NIDCD的一组PI互动，用于研究设计，任务开发，获取协议，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。我们已经与科学和统计计算核心合作，以使CTF MEG文件转移到AFNI，并开发了用于小组统计分析的工具。最近已将其扩展到包括额外的格式，以跨主题组促进基于时间的连接。 AFNI的即时相关特征用于研究MEG数据中的连接模式。 AFNI的其他特征允许提取大脑表面网眼，以允许对MEG源信号进行更详细的建模。 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。分析软件包已扩展到包括符号熵测量和转移熵互助信息技术，以探索大脑网络。 我们正在等待升级的包装的交付，该软件包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，并大大提高系统的使用寿命。 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。 Cornwell等人最近的工作表明，海马中的快速伽马活性与空间学习相关。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，在工作记忆任务期间增加的前扣带回活性和功能连通性都可以预测氯胺酮的抗抑郁反应。 Zarate及其同事已利用MEG表明突触增强对于治疗抗性大抑郁症的抗抑郁作用至关重要。 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。以前，Bassett和同事使用MEG在任务过程中看到配置变化的空间和时间能力，发现功能网络的特征是小世界属性，表明局部连接和远距离连接的混合。他们扩展了这项工作，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。我们还发现了患者群体中静止网络模式的差异，现在使用图理论方法来检查功能网络。对“休息活动”的兴趣继续与Biyu博士一起研究功能网络中的无标度性能。 几个PI都对感知和有意识的意识进行了进一步的调查。使用MEG HE和同事（Li，Hill，He：Neuroscience杂志，2014年3月19日，34（12））表明，不同的活动构成了这些认知现象。