Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom.
PLoS One. 2011;6(8):e22790. doi: 10.1371/journal.pone.0022790. Epub 2011 Aug 2.
Generative models of neuroimaging and electrophysiological data present new opportunities for accessing hidden or latent brain states. Dynamic causal modeling (DCM) uses Bayesian model inversion and selection to infer the synaptic mechanisms underlying empirically observed brain responses. DCM for electrophysiological data, in particular, aims to estimate the relative strength of synaptic transmission at different cell types and via specific neurotransmitters. Here, we report a DCM validation study concerning inference on excitatory and inhibitory synaptic transmission, using different doses of a volatile anaesthetic agent (isoflurane) to parametrically modify excitatory and inhibitory synaptic processing while recording local field potentials (LFPs) from primary auditory cortex (A1) and the posterior auditory field (PAF) in the auditory belt region in rodents. We test whether DCM can infer, from the LFP measurements, the expected drug-induced changes in synaptic transmission mediated via fast ionotropic receptors; i.e., excitatory (glutamatergic) AMPA and inhibitory GABA(A) receptors. Cross- and auto-spectra from the two regions were used to optimise three DCMs based on biologically plausible neural mass models and specific network architectures. Consistent with known extrinsic connectivity patterns in sensory hierarchies, we found that a model comprising forward connections from A1 to PAF and backward connections from PAF to A1 outperformed a model with forward connections from PAF to A1 and backward connections from A1 to PAF and a model with reciprocal lateral connections. The parameter estimates from the most plausible model indicated that the amplitude of fast glutamatergic excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) behaved as predicted by previous neurophysiological studies. Specifically, with increasing levels of anaesthesia, glutamatergic EPSPs decreased linearly, whereas fast GABAergic IPSPs displayed a nonlinear (saturating) increase. The consistency of our model-based in vivo results with experimental in vitro results lends further validity to the capacity of DCM to infer on synaptic processes using macroscopic neurophysiological data.
神经影像学和电生理数据的生成模型为获取隐藏或潜在的大脑状态提供了新的机会。动态因果建模 (DCM) 使用贝叶斯模型反演和选择来推断经验观察到的大脑反应背后的突触机制。特别是，电生理数据的 DCM 旨在估计不同细胞类型和特定神经递质的突触传递相对强度。在这里，我们报告了一项关于使用不同剂量挥发性麻醉剂（异氟醚）对兴奋性和抑制性突触处理进行参数修改的 DCM 验证研究，同时在啮齿动物听觉带区的初级听觉皮层 (A1) 和后听觉场 (PAF) 记录局部场电位 (LFP)。我们测试了 DCM 是否可以从 LFP 测量中推断出通过快速离子型受体介导的预期药物诱导的突触传递变化；即兴奋性 (谷氨酸能) AMPA 和抑制性 GABA(A) 受体。来自两个区域的交叉和自谱被用于基于生物上合理的神经质量模型和特定的网络结构优化三个 DCM。与感官层次结构中已知的外在连接模式一致，我们发现，一个由从 A1 到 PAF 的前向连接和从 PAF 到 A1 的后向连接组成的模型优于一个由从 PAF 到 A1 的前向连接和从 A1 到 PAF 的后向连接组成的模型，以及一个具有互相互连的模型。最合理模型的参数估计表明，快速谷氨酸能兴奋性突触后电位 (EPSP) 和抑制性突触后电位 (IPSP) 的幅度表现与先前神经生理学研究预测的一致。具体来说，随着麻醉水平的增加，谷氨酸能 EPSP 呈线性下降，而快速 GABA 能 IPSP 呈非线性（饱和）增加。我们基于模型的体内结果与实验体外结果的一致性进一步证明了 DCM 使用宏观神经生理数据推断突触过程的能力。
