Pinotsis D A
Centre for Mathematical Neuroscience and Psychology and Department of Psychology, City -University of London, London EC1V 0HB, United Kingdom; The Picower Institute for Learning & Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
J Neurosci Methods. 2020 Dec 1;346:108912. doi: 10.1016/j.jneumeth.2020.108912. Epub 2020 Aug 21.
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.
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.
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.
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.
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.
在大数据时代，来自动物和人类研究的大规模电生理数据十分丰富。这些数据包含多个时空尺度的信息。然而，目前分析电生理数据的方法往往仅聚焦于单一的时空尺度。
我们探讨了一种用于分析电生理数据的多尺度方法。该方法基于结合描述不同尺度脑数据的神经模型。它使我们能够利用非侵入性人类电生理数据对皮质源的神经生物学特性进行层特异性推断。
我们使用统计决策理论对该方法进行了数学证明。我们还考虑了其在脑成像研究中的扩展，包括来自执行不同任务的同一受试者的数据。作为示例，我们表明不同人之间伽马振荡的变化可能源于5/6层抑制性中间神经元的递归连接强度差异。
这是一种基于我们近期工作的新方法。它与其他时空动力学尺度固定的方法不同。
我们讨论了一种用于分析人类脑磁图数据的多尺度方法。该方法使用了一种神经群体模型，该模型包含由隔室模型提供的约束条件。这有两个优点。第一，它使我们能够利用非侵入性数据找到皮质层动力学的差异，并理解诸如神经调节、兴奋与抑制平衡等神经生物学特性。第二，它使我们能够通过利用动物数据来验证宏观尺度模型。
