A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks.
A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks.
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DOI：
10.1371/journal.pcbi.1006007
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发表时间：
2018-03
影响因子：
4.3
通讯作者：
Woolrich MW
中科院分区：
文献类型：
--
作者：
Abeysuriya RG;Hadida J;Sotiropoulos SN;Jbabdi S;Becker R;Hunt BAE;Brookes MJ;Woolrich MW
在长时间尺度上，神经元动力学可以对相当大的扰动具有鲁棒性，例如通过包括学习、老化、发育和某些疾病过程的过程的白色物质连接和灰质结构的变化。一种可能的解释是，强大的动力学是由能够动态地重新平衡大脑网络的稳态机制促进的。在这项研究中，我们模拟了一个皮层脑网络使用的Wilson-Cowan神经质量模型与传导延迟和噪声，并使用抑制性突触可塑性（ISP），以动态地实现空间局部之间的平衡兴奋和抑制。使用来自55名受试者的MEG数据，我们发现ISP使我们能够同时实现与多个功能连接性措施的高相关性，包括幅度包络相关性和相位锁定。此外，我们发现，ISP成功地实现了当地的E/I平衡，并可以始终如一地预测从真实的脑磁图数据计算的功能连接，为更广泛的模型参数范围比没有ISP的模型是可能的。最近，人们对研究突触可塑性在支持健康大脑活动中的作用很感兴趣。特别是，大脑中的兴奋和抑制之间的平衡被认为在大脑动力学中起着关键作用，并且这种平衡很可能由稳态机制调节。大脑的生物物理模型以前曾被用于预测功能连接，但通常对参数值的变化非常敏感，需要非常精细的调整才能实现逼真的动态。在这项研究中，我们研究了是否包括一个稳态可塑性机制将提高模拟神经动力学的鲁棒性。我们专注于MEG数据中的功能连接，它可以解决神经活动中的快速振荡，而不像fMRI。我们发现，包括一个简单的可塑性规则，以平衡兴奋和抑制导致更现实的模型预测，并降低敏感性模型参数的变化。
Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP. Recently there has been much interest in investigating the role of synaptic plasticity in supporting healthy brain activity. In particular, the balance between excitation and inhibition in the brain is believed to play a critical role in brain dynamics, and it is likely that this balance is regulated by homeostatic mechanisms. Biophysical models of the brain have previously been used to predict functional connectivity, but are typically extremely sensitive to changes in parameter values and require extremely fine tuning to achieve realistic dynamics. In this study, we investigated whether including a homeostatic plasticity mechanism would improve the robustness of simulated neural dynamics. We focused on functional connectivity in MEG data, which can resolve fast oscillations in neural activity, unlike fMRI. We found that including a simple plasticity rule to balance excitation and inhibition resulted in more realistic model predictions, and reduced sensitivity to changes in model parameters.
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细胞外田地和电流的起源-EEG，ECOG，LFP和尖峰。
DOI：
10.1038/nrn3241
发表时间：
2012-05-18
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Buzsáki G;Anastassiou CA;Koch C
通讯作者：
Koch C
影响因子：
5.7
作者：
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通讯作者：
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影响因子：
5.7
作者：
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通讯作者：
Smith SM
影响因子：
5.7
作者：
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通讯作者：
Deco, Gustavo
影响因子：
5.7
作者：
Colclough GL;Brookes MJ;Smith SM;Woolrich MW
通讯作者：
Woolrich MW