Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland
Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland.
J Neurosci. 2020 Jul 15;40(29):5658-5668. doi: 10.1523/JNEUROSCI.3069-19.2020. Epub 2020 Jun 19.
The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, neurobiological interpretations of predictive coding view perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs), and disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a hierarchical Bayesian model to single-trial EEG data from healthy human volunteers of either sex who received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207 ms poststimulus), while high-level PEs (about transition probability) are reflected by later components (152-199 and 215-277 ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts the inference on abstract statistical regularities. Our findings suggest that NMDAR dysfunction impairs hierarchical Bayesian inference about the world's statistical structure. Beyond the relevance of this finding for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential pathophysiological mechanisms.
听觉失匹配负波（MMN）在精神分裂症中显著减少。值得注意的是，NMDA 受体（NMDAR）拮抗剂也可以产生类似的 MMN 减少。这两种现象都被解释为反映了预测编码的损伤，或者更普遍地说，反映了“贝叶斯大脑”的概念，即大脑不断更新分层模型以推断其感官输入的原因。具体来说，预测编码的神经生物学解释将感知推断视为一种依赖于 NMDAR 的过程，该过程最小化分层精度加权预测误差（PE），而这种假定过程的干扰在精神分裂症的分层贝叶斯理论中起着关键作用。在这里，我们提供了该理论的经验证据，证明在“游动 MMN”范式中存在多个分层相关的 PE。我们将分层贝叶斯模型应用于健康男女志愿者的单试 EEG 数据，他们以安慰剂对照、双盲、自身对照的方式接受 NMDAR 拮抗剂 S-氯胺酮。通过对整个时间-传感器空间进行无限制的分析，我们的逐试分析表明，低水平的 PE（大约是刺激转换）在早期（刺激后 102-207 毫秒）表达，而高水平的 PE（大约是转换概率）则由后续成分（152-199 和 215-277 毫秒）反映。此外，我们发现氯胺酮显著减弱了高水平 PE 反应的表达，这意味着 NMDAR 拮抗作用破坏了对抽象统计规律的推断。我们的发现表明，NMDAR 功能障碍损害了对世界统计结构的分层贝叶斯推断。除了这一发现对精神分裂症的相关性之外，我们的结果还说明了计算单试分析评估潜在病理生理机制的潜力。
