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

我们早期的基础研究表明，认知任务期间的MEG记录具有与功能性MRI相当的定位大脑活动的能力。已经发现，在皮层水平的β频带中的功率的具体减少与BOLD激活结果一致。然而，诸如MEG/EEG的电生理记录具有其他成像技术不可能的细粒度时间信息。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。我们现在有一个更大的受试者队列，我们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，并且还将显示患者组之间的差异。 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，这种激活揭示了与基因型的相互作用，为充分研究COMT标记。与一组匹配良好的患者，同胞和controlsfor工作记忆任务性能的患者表现出明显的减少DLPFC激活在明显区别于增加BOLD相对于任务负荷。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。我们正在扩展这些结果，以检查受试者组之间伽马波段的差异。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背后的神经生理学的更具体目标。 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。此前Bassett等人已经表明，患者群体中的功能网络变化可能与认知活动的行为结果有关。即使在休息时，精神分裂症患者与正常受试者相比，伽马功率也会降低。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。Sienenhuhner等人现在已经对一小组精神分裂症患者和健康对照组的工作记忆中的功能连接模式进行了广泛的分析。他们发现这些网络的拓扑结构在复杂性和跨频率相互作用的关系方面都发生了明显的变化。我们计划对更多的受试者进行随访，并比较休息和任务相关网络。 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。这种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。MEG能够显示广泛传播的临界动力学，这些动力学对于优化此类网络中的信息处理至关重要。这些最佳动力学可能是必要的可塑性适当的适应行为。跟踪和增强这种可塑性的新策略正在计划中。

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.