Background

In the era of Big Data, large scale electrophysiological data from animal and human studies are abundant. These data contain information at multiple spatiotemporal scales. However, current approaches for the analysis of electrophysiological data often focus on a single spatiotemporal scale only.

New method

We discuss a multiscale approach for the analysis of electrophysiological data. This is based on combining neural models that describe brain data at different scales. It allows us to make laminar-specific inferences about neurobiological properties of cortical sources using non invasive human electrophysiology data.

Results

We provide a mathematical proof of this approach using statistical decision theory. We also consider its extensions to brain imaging studies including data from the same subjects performing different tasks. As an illustration, we show that changes in gamma oscillations between different people might originate from differences in recurrent connection strengths of inhibitory interneurons in layers 5/6.

Comparison with existing methods

This is a new approach that follows up on our recent work. It is different from other approaches where the scale of spatiotemporal dynamics is fixed.

Conclusions

We discuss a multiscale approach for the analysis of human MEG data. This uses a neural mass model that includes constraints informed by a compartmental model. This has two advantages. First, it allows us to find differences in cortical laminar dynamics and understand neurobiological properties like neuromodulation, excitation to inhibition balance etc. using non invasive data. Second, it allows us to validate macroscale models by exploiting animal data.

中文翻译：

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统计决策理论和人脑数据的多尺度分析。

背景

在大数据时代，来自动物和人类研究的大规模电生理数据非常丰富。这些数据包含多个时空尺度的信息。但是，当前用于分析电生理数据的方法通常只关注单个时空尺度。

新方法

我们讨论了一种用于电生理数据分析的多尺度方法。这是基于组合描述不同规模大脑数据的神经模型的。它使我们能够使用非侵入性人类电生理数据对皮质来源的神经生物学特性进行层流特定的推断。

结果

我们使用统计决策理论提供了这种方法的数学证明。我们还考虑将其扩展到脑成像研究，包括来自执行不同任务的同一受试者的数据。作为说明，我们表明，不同人之间的伽马振荡变化可能源自5/6层中抑制性中间神经元的反复连接强度差异。

与现有方法的比较

这是我们最近工作的一种新方法。它不同于固定时空动态范围的其他方法。

结论

我们讨论了一种用于人类MEG数据分析的多尺度方法。这使用了神经质量模型，该神经质量模型包括由隔室模型告知的约束。这有两个优点。首先，它使我们能够利用非侵入性数据发现皮质层流动力学的差异，并了解神经生物学特性，例如神经调节，激发抑制平衡等。其次，它允许我们通过利用动物数据来验证宏观模型。