A multi-layer network approach to MEG connectivity analysis.
A multi-layer network approach to MEG connectivity analysis.
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DOI：
10.1016/j.neuroimage.2016.02.045
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发表时间：
2016-05-15
期刊：
影响因子：
5.7
通讯作者：
Morris PG
中科院分区：
文献类型：
--
作者：
Brookes MJ;Tewarie PK;Hunt BAE;Robson SE;Gascoyne LE;Liddle EB;Liddle PF;Morris PG
近年来，区域间神经网络连接对于支持健康的大脑功能至关重要。这种连通性可以使用诸如MEG的神经成像技术来测量，然而电生理信号的丰富性使得获得完整的图像具有挑战性。具体来说，连接性可以计算为大范围不同频带内神经振荡之间的统计相互依赖性。此外，可以计算频带之间的连接性。这种泛光谱网络层次结构可能有助于调解多个大脑网络的同时形成，这些网络支持持续的任务需求。然而，到目前为止，它在很大程度上被忽视了，许多电生理功能连接研究孤立地处理个别频段。在这里，我们结合联合收割机振荡包络的功能连接性指标与多层网络框架，以获得一个更完整的图片内和频率之间的连接。我们测试这种方法，使用MEG数据记录在视觉任务，突出的同时和短暂的形成运动网络的β带，视觉网络的γ带和β-γ的相互作用。在测试了我们的方法后，我们用它来证明精神分裂症患者与健康对照组相比枕叶α带连接性的差异。我们进一步表明，这些连接差异可以预测疾病持续症状的严重程度，突出了它们的临床相关性。我们的研究结果证明了MEG在抑制神经网络形成和溶解方面的独特潜力。此外，我们增加了重量的论点，即连接障碍是精神分裂症神经病理学的核心特征。多层网络框架被应用于脑磁包络连通性分析。模型允许完整了解频段内和频段之间的连接情况。方法使用可视化任务中的数据进行验证。方法显示精神分裂症患者的α带连接异常。精神分裂症的发现可能与患者注意力机制的改变有关。
Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia. A multi-layer network framework is applied to MEG envelope connectivity analysis. Model allows a complete picture of within and between frequency band connectivity. Method is validated using data from a visuomotor task. Method shows abnormalities in alpha band connectivity in schizophrenia patients. Schizophrenia finding may relate to altered attentional mechanisms in patients.