Neural activity is organized at multiple scales, ranging from the cellular to the whole brain level. Connecting neural dynamics at different scales is important for understanding brain pathology. Neurological diseases and disorders arise from interactions between factors that are expressed in multiple scales. Here, we suggest a new way to link microscopic and macroscopic dynamics through combinations of computational models. This exploits results from statistical decision theory and Bayesian inference. To validate our approach, we used two independent MEG datasets. In both, we found that variability in visually induced oscillations recorded from different people in simple visual perception tasks resulted from differences in the level of inhibition specific to deep cortical layers. This suggests differences in feedback to sensory areas and each subject’s hypotheses about sensations due to differences in their prior experience. Our approach provides a new link between non-invasive brain imaging data, laminar dynamics and top-down control.

神经活动是在多个尺度上组织的，从细胞水平到整个大脑水平。连接不同尺度的神经动力学对于理解大脑病理学非常重要。神经系统疾病和紊乱是由多种尺度表达的因素之间的相互作用引起的。在这里，我们提出了一种通过计算模型的组合来连接微观和宏观动力学的新方法。这利用了统计决策理论和贝叶斯推理的结果。为了验证我们的方法，我们使用了两个独立的 MEG 数据集。在这两者中，我们发现不同人在简单视觉感知任务中记录的视觉诱发振荡的变异性是由于深层皮质层特有的抑制水平的差异造成的。这表明由于先前经验的差异，对感觉区域的反馈以及每个受试者对感觉的假设存在差异。我们的方法提供了非侵入性脑成像数据、层流动力学和自上而下控制之间的新联系。