Integrating EEG/MEG and fMRI: 99-M-0172

整合 EEG/MEG 和 fMRI：99-M-0172

基本信息

批准号：
8939994
负责人：
Richard Coppola
金额：
$ 5.56万
依托单位：
NATIONAL INSTITUTE OF MENTAL HEALTH
依托单位国家：
美国
项目类别：
财政年份：
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/8939994
关键词：
Adaptive Behaviors Address Amygdaloid structure Anterior Area Auditory Awareness Behavioral Biological Markers Blood flow Brain Brain Part Brain region Cereals Clinical Cognitive Cognitive deficits Cognitive remediation Coupling Disease Electroencephalography Family member Frequencies Functional Magnetic Resonance Imaging Functional disorder Genetic Genotype Graph Imaging Techniques Individual Differences Investigation Measurement Measures Memory Methods Neurons Outcome Pathology Patients Pattern Performance Process Property Protocols documentation Relative (related person)Resolution Rest Schizophrenia Short-Term Memory Siblings Signal Transduction Source Structure System Task Performances Time Training Variant Visual Work cingulate cortex cohort gamma-Aminobutyric Acid improved indexing information processing neurophysiology neuropsychiatry remediation response volunteer

项目摘要

Our earlier basic studies have shown that MEG recordings during cognitive tasks have the ability to localize brain activity in comparable fashion to functional MRI. Specifically reductions in of power in the beta frequency band at the cortical level has been found to agree with BOLD activation results. However, electrophysiological recordings such as the MEG/EEG have fine grained temporal information not possible with other imaging techniques. Further studies have demonstrated the ability to localize signals in deeper structures such as the amygdala and to investigate the relation of visual awareness to gamma band signals. We have also found that GABA (gamma-aminobutyric acid) concentration in the anterior cingulate cortex correlates with spatially localized resting MEG beta band power. We now have a much larger cohort of subjects that we hope that with further investigation will reveal additional relationships between gamma power and GABA systems and that will also show difference across patient groups. Differences in the degree of activation especially in frontal regions as indexed by beta desynchronization during a working memory task have been found between patients with schizophrenia compared to well siblings and normal control volunteers. Previously we have seen that this activation reveals an interaction with genotype for the well studied COMT marker.. With a well matched set of patients, siblings and controlsfor working memory task performance patients show a distinct reduced DLPFC activation in apparent distinction to increased BOLD relative to task load. The MEG analysis isolates a working memory component that may reflect a different aspect of cortical processing. We are extending these results to examine difference in gamma band across the subject groups. The relation of the different frequency bands to patterns of blood flow activation we hope will reveal more specific targets of for the neurophysiology underlying patient differences. Differences in network patterns and dynamics are key to understanding underlying pathology in clinical groups. Previously Bassett et al have shown that functional network variations in patient groups can be related to behavioral outcomes on cognitive activities. Even at rest patients with schizophrenia have gamma power reduction compared to normal subjects.. We have found distinct patterns of the temporal sequence of brain regions involved in these memory tasks that show a variety of individual differences across subjects. This has been extended using graph theoretic measures as a way to capture properties of the pattern activity across brain regions. Sienenhuhner et al have now done an extensive analysis of functional connectivity patterns during working memory in a small group of patients with schizophrenia and healthy controls. They have found distinct changes in the topology of these networks both in complexity and in the relation of cross-frequency interactions. We plan to follow this up with a larger number of subjects and to compare resting as well as task related networks. Previous work examined whether reorganization of functional brain networks can be seen in response to cognitive remediation strategies using auditory task training in both patients and healthy. We were able to show significant changes in power and coherence in brain activity that were associated with improved behavioral performance. This biomarker would allow tracking the outcome of remediation strategies targeting specific cognitive deficits in neuropsychiatric disorders. MEG is able to show wide spread critical dynamics that are crucial to optimize information processing in such networks. These optimal dynamics may be essential for the plasticity necessary for appropriate adaptive behavior. New strategies for tracking and enhancing this plasticity are planned.

我们较早的基础研究表明，认知任务中的MEG记录具有与功能性MRI相当的方式定位大脑活动的能力。发现皮质水平的β频带中功率的特异性降低与大胆的激活结果一致。但是，使用其他成像技术，诸如MEG/EEG之类的电生理记录具有细菌性的时间信息。进一步的研究表明，可以将信号定位在杏仁核等更深的结构中，并研究视觉意识与伽马频带信号的关系。我们还发现，前扣带回皮层中的GABA（γ-氨基丁酸）浓度与空间局部静止的MEGβ带功率相关。现在，我们拥有更多的主题，希望通过进一步的研究将揭示伽马功率和GABA系统之间的其他关系，并且这也将在患者群体之间显示出差异。与兄弟姐妹和正常对照志愿者相比，精神分裂症患者之间在工作记忆任务期间，在工作记忆任务期间通过Beta Denchronation指数的额叶区域的激活程度差异。以前，我们已经看到，这种激活揭示了研究经过良好研究的COMT标记的基因型的相互作用。.具有匹配的患者，兄弟姐妹和对照组的工作记忆任务绩效患者的匹配，显示出明显降低的DLPFC激活，显然与大胆相对于大胆而言，相对于大胆而言，相对于大胆而言，相对于大胆而言，则显示出明显的降低。任务负载。 MEG分析隔离了可能反映皮质处理不同方面的工作记忆组件。我们正在扩展这些结果，以检查整个受试者群体中伽马频段的差异。我们希望不同频带与血流激活模式的关系将揭示患者差异的神经生理学的更具体的靶标。网络模式和动态的差异是理解临床组潜在病理的关键。以前，Bassett等人已经表明，患者组的功能网络变化可能与认知活动的行为结果有关。即使在患有精神分裂症的休息患者中，与正常受试者相比，也具有伽马功率的降低。.我们发现了这些记忆任务中涉及的大脑区域的时间序列的不同模式，这些模式显示出各种受试者的各种个体差异。使用图理论措施将其扩展为捕获跨大脑区域的模式活动的特性的一种方式。 Sienenhuhner等人现在对一小组精神分裂症和健康对照患者的工作记忆过程中的功能连通性模式进行了广泛的分析。他们在复杂性和跨频相互作用的关系方面发现了这些网络拓扑的明显变化。我们计划跟进更多主题，并比较休息和与任务相关的网络。先前的工作检查了是否可以使用患者和健康的听觉任务培训来响应认知补救策略来重新组织功能性脑网络的重组。我们能够显示出与改善行为表现有关的大脑活动的功率和连贯性的显着变化。该生物标志物将允许跟踪针对神经精神疾病中特定认知缺陷的补救策略的结果。 MEG能够显示出广泛的关键动态，这对于优化此类网络中的信息处理至关重要。这些最佳动力学对于适当的适应性行为所需的可塑性可能至关重要。计划了跟踪和增强这种可塑性的新策略。