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

我们早期的基础研究表明，认知任务中的脑磁图记录能够以与功能磁共振相当的方式定位大脑活动。具体地说，大脑皮层水平的β频段功率的减少与大胆的激活结果一致。然而，电生理记录，如脑磁图/脑电信号，具有细粒度的时间信息，这是其他成像技术不可能实现的。进一步的研究已经证明了在杏仁核等更深层次的结构中定位信号的能力，并研究了视觉感知与伽马波段信号的关系。我们还发现，前扣带回皮质的GABA浓度与空间定位的静息MEGβ频带功率相关。我们现在有一个更大的受试者队列，我们希望随着进一步的研究，我们将揭示伽玛能量和GABA系统之间的其他关系，这也将显示不同患者组之间的差异。 精神分裂症患者在工作记忆任务中的激活程度，特别是在额叶区域的激活程度，特别是在以β去同步化为指标的工作记忆任务中，发现与健康的同胞和正常对照志愿者相比存在差异。以前我们已经看到，这种激活揭示了与研究得很好的COMT标记的基因型之间的相互作用。在一组匹配良好的患者、兄弟姐妹和对照组的工作记忆任务绩效上，患者表现出明显的DLPFC激活减少，与相对于任务负荷增加的BOLD明显不同。脑磁图分析分离出一个工作记忆成分，它可能反映了大脑皮质处理的不同方面。我们正在扩展这些结果，以检查不同受试者之间伽马波段的差异。不同频段与血流激活模式的关系，我们希望能揭示患者潜在的神经生理学差异的更具体的靶点。 网络模式和动力学的差异是理解临床组潜在病理的关键。此前，Bassett等人已经证明，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，精神分裂症患者的伽马功率也比正常受试者低。我们发现，参与这些记忆任务的大脑区域的时间序列有明显的模式，显示出不同受试者的不同个体差异。这一点已经被扩展，使用图论测量作为一种方法来捕捉跨大脑区域的模式活动的属性。Sienenhuhner等人现在对一小群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上都发生了明显的变化。我们计划用更多的受试者来跟进这一点，并比较休息和任务相关网络。 以前的工作检查了在患者和健康人中是否可以看到功能大脑网络的重组对使用听觉任务训练的认知修复策略的反应。我们能够显示出大脑活动的力量和一致性的显著变化，这与行为表现的改善有关。这个生物标记物将允许跟踪针对神经精神障碍的特定认知缺陷的补救策略的结果。MEG能够显示广泛传播的关键动态，这些动态对于优化此类网络中的信息处理至关重要。对于适当的适应行为所需的可塑性来说，这些最优动力学可能是必不可少的。为跟踪和增强这种可塑性，计划了新的战略。

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.