Song Deying, Chung Daniel W, Ermentrout G Bard
Joint Program in Neural Computation and Machine Learning, Neuroscience Institute, and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA, 15213.
Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA, 15213.
Res Sq. 2024 Feb 14:rs.3.rs-3938805. doi: 10.21203/rs.3.rs-3938805/v1.
Deficient gamma oscillations in the prefrontal cortex (PFC) of individuals with schizophrenia (SZ) are proposed to arise from alterations in the excitatory drive to fast-spiking interneurons and in the inhibitory drive from these interneurons to excitatory neurons . Consistent with this idea, prior postmortem studies showed lower levels of molecular and structural markers for the strength of and synapses and also greater variability in synaptic strength in PFC of SZ. Moreover, simulating these alterations in a network of quadratic integrate-and-fire (QIF) neurons revealed a synergistic effect of their interactions on reducing gamma power. In this study, we aimed to investigate the dynamical nature of this synergistic interaction at macroscopic level by deriving a mean-field description of the QIF model network that consists of all-to-all connected excitatory neurons and fast-spiking interneurons. Through a series of numerical simulations and bifurcation analyses, findings from our mean-field model showed that the macroscopic dynamics of gamma oscillations are synergistically disrupted by the interactions among lower strength of and synapses and greater variability in synaptic strength. Furthermore, the two-dimensional bifurcation analyses showed that this synergistic interaction is primarily driven by the shift in Hopf bifurcation due to lower synaptic strength. Together, these simulations predict the nature of dynamical mechanisms by which multiple synaptic alterations interact to robustly reduce PFC gamma power in SZ, and highlight the utility of mean-field model to study macroscopic neural dynamics and their alterations in the illness.
精神分裂症（SZ）患者前额叶皮质（PFC）中γ振荡不足被认为是由于对快发放中间神经元的兴奋性驱动以及这些中间神经元对兴奋性神经元的抑制性驱动发生改变所致。与此观点一致的是，先前的尸检研究表明，SZ患者PFC中关于 和 突触强度的分子和结构标记物水平较低，并且 突触强度的变异性也更大。此外，在二次积分发放（QIF）神经元网络中模拟这些改变，揭示了它们相互作用对降低γ功率的协同效应。在本研究中，我们旨在通过推导由全连接兴奋性神经元和快发放中间神经元组成的QIF模型网络的平均场描述，在宏观水平上研究这种协同相互作用的动力学性质。通过一系列数值模拟和分岔分析，我们平均场模型的研究结果表明，γ振荡的宏观动力学被 和 突触强度较低以及 突触强度变异性较大之间的相互作用协同破坏。此外，二维分岔分析表明，这种协同相互作用主要是由 突触强度降低导致的霍普夫分岔的偏移所驱动。总之，这些模拟预测了多种突触改变相互作用以有力降低SZ患者PFCγ功率的动力学机制的性质，并突出了平均场模型在研究宏观神经动力学及其在疾病中的改变方面的效用。
