论脑图像与脑神经网络之间的关系。
On the relation between brain images and brain neural networks.
作者信息
Taylor J G, Krause B, Shah N J, Horwitz B, Mueller-Gaertner H W
机构信息
Department of Mathematics, King's College, Strand, London, UK..
出版信息
Hum Brain Mapp. 2000 Mar;9(3):165-82. doi: 10.1002/(SICI)1097-0193(200003)9:3<165::AID-HBM5>3.0.CO;2-P.
The relationship between brain images observed by PET and fMRI and the underlying neural activity is analysed using recent results on the detailed nature of averaged and synchronised activity of coupled neural networks and on a simplifying model of the level of blood flow caused by neural activity. The conditions on the coupled neural systems are specified that lead to structural equation models, giving support to analysis of the covariance structural equation modelling of brain imaging data. The relation between the resulting models and possible neural codes are analysed. Furthermore, a new form of structural equation model is derived, in which all neuronal activity arises as hidden variables. We discuss how the results of such analyses can be transported back to the domain of coupled temporally dynamic neural systems in the brain appropriate to EEG and MEG observations.
利用耦合神经网络平均和同步活动的详细性质以及神经活动引起的血流水平简化模型的最新研究成果，分析了正电子发射断层扫描（PET）和功能磁共振成像（fMRI）所观察到的脑图像与潜在神经活动之间的关系。明确了耦合神经系统的条件，这些条件导致了结构方程模型，为脑成像数据的协方差结构方程建模分析提供了支持。分析了所得模型与可能的神经编码之间的关系。此外，还推导了一种新的结构方程模型形式，其中所有神经元活动都作为隐藏变量出现。我们讨论了如何将这些分析结果反馈到适合脑电图（EEG）和脑磁图（MEG）观测的大脑中耦合的时间动态神经系统领域。
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