Centre for Data Science, Coventry University, Coventry CV1 2JH, UK.
Stephenson School of Biomedical Engineering, The University of Oklahoma, Tulsa, OK 74135, USA; Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Laureate Institute for Brain Research, Tulsa, OK 74136, USA.
Neuroscience. 2021 Mar 15;458:213-228. doi: 10.1016/j.neuroscience.2020.12.001. Epub 2020 Dec 11.
The human nervous system is one of the most complicated systems in nature. Complex nonlinear behaviours have been shown from the single neuron level to the system level. For decades, linear connectivity analysis methods, such as correlation, coherence and Granger causality, have been extensively used to assess the neural connectivities and input-output interconnections in neural systems. Recent studies indicate that these linear methods can only capture a certain amount of neural activities and functional relationships, and therefore cannot describe neural behaviours in a precise or complete way. In this review, we highlight recent advances in nonlinear system identification of neural systems, corresponding time and frequency domain analysis, and novel neural connectivity measures based on nonlinear system identification techniques. We argue that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively. These approaches can hopefully provide new insights to advance our understanding of neurophysiological mechanisms underlying neural functions. These nonlinear approaches also have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.
人类神经系统是自然界中最复杂的系统之一。从单个神经元水平到系统水平，已经显示出复杂的非线性行为。几十年来，线性连接分析方法，如相关性、相干性和格兰杰因果关系，已被广泛用于评估神经网络中的神经连接和输入-输出互连。最近的研究表明，这些线性方法只能捕捉到一定数量的神经活动和功能关系，因此不能精确或完整地描述神经行为。在这篇综述中，我们强调了神经系统非线性系统识别、相应的时频域分析以及基于非线性系统识别技术的新型神经连接度量的最新进展。我们认为，非线性建模和分析对于定量研究神经系统中的神经元处理和信号传递是必要的。这些方法有望为我们深入了解神经功能的神经生理机制提供新的见解。这些非线性方法也有可能产生敏感的生物标志物，以促进开发用于评估神经障碍和靶向干预效果的精确诊断工具。
