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

脑磁图数据记录和信号处理方法的发展已经非常成功。对工作记忆任务和听觉处理任务的研究已经显示了定位大脑激活和解决阶段性与紧张性活动问题的能力。我们正在调查表型的措施，将适用于许多相关的研究，特别是同胞项目。 认知激活过程中的脑磁图记录显示出与功能磁共振成像非常相似的定位能力。特别是皮质水平的β去磷酸化与BOLD激活结果一致。然而，MEG/EEG允许使用其他成像技术不可能的时间信息。我们的研究结果表明，大脑结构和功能的措施是找到易感基因的有力工具。进一步的研究已经证明了定位信号在更深的结构，如杏仁核的能力，并调查视觉意识的伽马波段信号的关系。我们还发现，GABA（γ-氨基丁酸）在前扣带皮层浓度与空间定位静息脑磁图β带功率。我们希望进一步的研究将揭示伽马功率和GABA系统之间的其他关系。 在工作记忆任务中，精神分裂症患者与健康的同胞和正常对照志愿者之间的激活程度差异，特别是在额叶区域，以β去极化为指标。以前我们已经看到，这种激活揭示了一个相互作用与基因型的充分研究COMT标记。我们发现，在预期即将到来的任务需求时，前额叶皮层的活动会发生调制。我们已经将该分析扩展到一组匹配良好的患者、兄弟姐妹和对照组。当工作记忆任务完成受到控制时，患者表现出明显的DLPFC激活减少，与任务负荷相关的BOLD增加明显不同。脑磁图分析分离出一个工作记忆成分，它可能反映了皮层处理的不同方面。 我们现在发现，与健康志愿者相比，精神分裂症患者的这种成分与行为有着明显不同的关系。 我们现在正在比较这些结果，以更好地了解这些措施如何反映大脑激活。 网络模式和动力学的差异是理解临床组中潜在病理学的关键。Bassett等人已经表明，患者群体中的功能网络差异可以被证明并与认知活动的行为结果相关。Rutter等人已经表明，即使在休息时，与正常受试者相比，精神分裂症患者的伽马功率也会降低。这些发现是否与状态或性状差异有关，以及是否存在遗传关联，还有待观察。我们发现，参与这些记忆任务的大脑区域的时间序列具有不同的模式，这些模式显示出受试者之间的各种个体差异。 这已经被扩展使用图论测量作为一种方式来捕获跨大脑区域的模式活动的属性。 在人脸识别任务中，我们发现了一个独特的区域网络，这些区域通过不同振荡频率的交叉耦合进行交互。进一步的研究将检查临床组之间的差异。 以前的工作研究了功能性脑网络的重组是否可以看到在响应认知补救策略，使用听觉任务训练在患者和健康。 我们能够显示出与行为表现改善相关的大脑活动的力量和连贯性的显着变化。这可以形成生物标志物的基础，该生物标志物将允许跟踪针对神经精神障碍中特定认知缺陷的补救策略的结果。 最近的工作表明，MEG能够显示广泛传播的关键动态，这对优化此类网络中的信息处理至关重要。这些最佳动力学可能是必要的可塑性适当的适应行为。

Development of MEG data recording and signal processing methods has been very successful. Studies of working memory tasks and auditory processing tasks have shown the ability to localize brain activation and to address issues of phasic versus tonic activity. We are investigating phenotype measures that will be applicable in many related studies and specifically the sibling project. MEG recording during cognitive activation has shown the ability to localize in very comparable fashion to fMRI. Specifically beta desynchronization at the cortical level has been found to agree with BOLD activation results. However, the MEG/EEG allows for temporal information not possible with other imaging techniques. Our results show that measures of brain structure and function represent powerful tools to find susceptibility genes. 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 expect that further investigation will reveal additional relationships between gamma power and GABA systems. 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. We have found there is a modulation of prefrontal cortex activity that occurs in anticipation of the upcoming task demands. We have extended this analysis to a well matched set of patients, siblings and controls. When working memory task performance is controlled 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 now find that this component has a distinctly different relation to behavior in patients with schizophrenia compared to healthy volunteers. We are now comparing these results across modalities to better understand how these measures reflect brain activation. Differences in network patterns and dynamics are key to understanding underlying pathology in clinical groups. Bassett et al have shown that functional network differences in patient groups can be demonstrated and related to behavioral outcomes on cognitive activities. Rutter et al have shown that even at rest patients with schizophrenia have gamma power reduction compared to normal subjects. It remains to be seen whether these finding relate to state or trait differences and if there are genetic associations. 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. In a face recognition tasks we found a distinct network of regions that interact by cross-coupling of different frequencies of oscillatory. Further studies will examine the difference across clinical groups. 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 could form the basis of a biomarker that would allow tracking the outcome of remediation strategies targeting specific cognitive deficits in neuropsychiatric disorders. Recent work has shown that 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.