Coronel-Oliveros Carlos, Lehue Fernando, Herzog Rubén, Mindlin Iván, Gatica Marilyn, Kowalczyk-Grębska Natalia, Medel Vicente, Cruzat Josephine, Gonzalez-Gomez Raul, Hernandez Hernán, Tagliazucchi Enzo, Prado Pavel, Orio Patricio, Ibáñez Agustín
Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago, Chile.
Trinity College Dublin, The University of Dublin, Dublin, Ireland.
bioRxiv. 2025 Aug 31:2025.08.26.672269. doi: 10.1101/2025.08.26.672269.
Whole-brain models are valuable tools for understanding brain dynamics in health and disease by enabling the testing of causal mechanisms and identification of therapeutic targets through dynamic simulations. Among these models, biophysically inspired neural mass models have been widely used to simulate electrophysiological recordings, such as MEG and EEG. However, traditional models face limitations, including susceptibility to hyperexcitation, which constrains their ability to capture the full richness of neural dynamics. Here, we developed and characterized a new version of the Jansen-Rit neural mass model aimed at overcoming these limitations. Our model incorporates inhibitory synaptic plasticity (ISP), which adjusts inhibitory feedback onto pyramidal neurons to clamp their firing rates around a target value. Further, the model combined two subpopulations of neural cortical columns oscillating in α and γ, respectively, to generate a richer EEG power spectrum. We analyzed how different model parameters modulate oscillatory frequency and connectivity. We considered a model's showcase, simultaneously fitting EEG and fMRI recordings during NREM sleep. Bifurcation analysis showed that ISP increases the parameters' range in which the model exhibited sustained oscillations; the target firing rate acts as a bifurcation parameter, moving the system across the bifurcation point, producing different oscillatory regimes, from slower to faster. High frequency activity emerged from low global coupling, high firing rates, and a high proportion of γ versus α subpopulations. Importantly, ISP was necessary in the multi-frequency model to successfully fit EEG functional connectivity across frequency bands. Finally, ISP-controlled reductions in excitability reproduced both the slow-wave activity and the reduced connectivity in NREM sleep. Altogether, our model is compatible with biological evidence of the effects of E/I balance on modulating brain rhythms and connectivity, as observed in sleep, neurodegeneration, and chemical neuromodulation. This biophysical model with ISP provides a springboard for realistic brain simulations in health and disease.
全脑模型是理解健康和疾病状态下脑动力学的重要工具，它能够通过动态模拟来测试因果机制并识别治疗靶点。在这些模型中，受生物物理学启发的神经团块模型已被广泛用于模拟诸如脑磁图（MEG）和脑电图（EEG）等电生理记录。然而，传统模型存在局限性，包括易受过度兴奋影响，这限制了它们捕捉神经动力学全部丰富特征的能力。在此，我们开发并表征了一个新版本的扬森 - 里特神经团块模型，旨在克服这些局限性。我们的模型纳入了抑制性突触可塑性（ISP），它可调节对锥体神经元的抑制性反馈，以使它们的放电率围绕目标值钳制。此外，该模型结合了分别在α和γ频段振荡的两个神经皮质柱亚群，以生成更丰富的脑电功率谱。我们分析了不同模型参数如何调节振荡频率和连接性。我们考虑了一个模型的展示案例，即同时拟合非快速眼动睡眠期间的脑电图和功能磁共振成像（fMRI）记录。分岔分析表明，ISP增加了模型表现出持续振荡的参数范围；目标放电率充当分岔参数，使系统跨越分岔点，产生不同的振荡状态，从较慢到较快。高频活动源于低全局耦合、高放电率以及γ亚群与α亚群的高比例。重要的是，在多频模型中，ISP对于成功拟合跨频段的脑电图功能连接是必要的。最后，ISP控制的兴奋性降低再现了非快速眼动睡眠中的慢波活动和连接性降低。总之，我们的模型与在睡眠、神经退行性变和化学神经调节中观察到的兴奋性/抑制性（E/I）平衡对调节脑节律和连接性影响的生物学证据相符。这个具有ISP的生物物理模型为健康和疾病状态下的真实脑模拟提供了一个跳板。
