West Timothy O, Berthouze Luc, Farmer Simon F, Cagnan Hayriye, Litvak Vladimir
Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford OX3 9DU, United Kingdom; Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford OX1 3TH, United Kingdom; Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom.
Centre for Computational Neuroscience and Robotics, University of Sussex, Falmer, United Kingdom; UCL Great Ormond Street Institute of Child Health, Guildford St., London WC1N 1EH, United Kingdom.
Neuroimage. 2021 Aug 1;236:118020. doi: 10.1016/j.neuroimage.2021.118020. Epub 2021 Apr 9.
This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental, in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependant dynamics, such as oscillatory bursts, in terms of the underlying neural circuits.
本文描述并验证了一种新颖的框架，该框架使用近似贝叶斯计算（ABC）算法进行中尺度脑网络活动模型中的参数估计和模型选择。我们给出了原理证明，首次通过使用一组皮质-基底神经节-丘脑回路的神经质量模型对该框架进行验证，这些模型是根据实验性体内记录的频谱特征进行反向构建的。这种优化方案放宽了先前频谱特征反向建模方法中采用的固定形式后验（即拉普拉斯近似）的假设。这使得能够探索超出局部线性假设近似范围的模型动态，从而拟合基础非线性方程组的显式数值解。在这第一篇论文中，我们从以下几个方面对优化过程进行了初步验证：（i）在已知数据成因的情况下，近似参数后验密度的能力；（ii）模型比较过程产生后验模型概率以识别已知生成数据的模型结构的能力；（iii）面对不同起始条件时这些过程对局部最小值的鲁棒性。最后，作为一个示例应用，我们展示了（iv）鉴于帕金森病中皮质-基底神经节-丘脑回路的已知神经生物学知识，模型比较能够得出合理的结论。这些结果为未来利用高度非线性或脆弱模型的研究奠定了基础，这些模型能够根据基础神经回路解释时间相关动态，如振荡爆发。
