Department of Psychology, University of Wisconsin, Madison, Wisconsin 53706.
Department of Anesthesiology, University of Wisconsin, Madison 53703.
J Neurosci. 2021 Dec 8;41(49):10130-10147. doi: 10.1523/JNEUROSCI.1311-21.2021. Epub 2021 Nov 3.
Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way). While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling, and machine learning, we show that inferred "causal" relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audiovisual delayed match-to-sample task which elicited predictions. Predictive β feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, an NMDAR blocker, but not the control drug dexmedetomidine, perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex, and pre-activation of sensory templates in higher-order sensory cortex. This study suggests that predictions depend on alpha activity in higher-order cortex, β feedback, and NMDARs, and ketamine blocks access to learned predictive information. We learn the statistical regularities around us, creating associations between sensory stimuli. These associations can be exploited by generating predictions, which enable fast and efficient behavior. When predictions are perturbed, it can negatively influence perception and even contribute to psychiatric disorders, such as schizophrenia. Here we show that the frontal lobe generates predictions and sends them to posterior brain areas, to activate representations of predicted sensory stimuli before their appearance. Oscillations in neural activity (α and β waves) are vital for these predictive mechanisms. The drug ketamine blocks predictions and the underlying mechanisms. This suggests that the generation of predictions in the frontal lobe, and the feedback pre-activating sensory representations in advance of stimuli, depend on NMDARs.
学习刺激之间的关联使我们能够对世界进行建模并做出预测,这对于高效行为至关重要(例如,听到警笛声,我们期望看到救护车并迅速让路)。虽然有用于预测的理论和计算框架,但电路和受体水平的机制尚不清楚。我们使用高密度 EEG、贝叶斯建模和机器学习,表明在引发预测的视听延迟匹配样本任务中,推断出刺激与额部α活动之间的“因果”关系可以逐试解释反应时间(预测的代理)。预测性β反馈在预测刺激之前提前激活感觉表示。低剂量氯胺酮(NMDA 受体阻滞剂),而不是对照药物右美托咪定,会干扰预测的行为指标、它们在高级皮层中的表示、对后皮层的反馈以及在高级感觉皮层中感觉模板的预激活。这项研究表明,预测依赖于高级皮层中的α活动、β反馈和 NMDA 受体,氯胺酮阻止了对学习预测信息的访问。我们学习周围的统计规律,在感官刺激之间建立关联。这些关联可以通过生成预测来利用,从而实现快速高效的行为。当预测受到干扰时,它可能会对感知产生负面影响,甚至导致精神障碍,如精神分裂症。在这里,我们表明额叶产生预测,并将其发送到大脑后部区域,在预测的感觉刺激出现之前激活它们的表示。神经活动(α 和 β 波)的波动对于这些预测机制至关重要。药物氯胺酮会阻止预测及其潜在机制。这表明,额叶中预测的产生以及在刺激之前预先激活感觉表示的反馈,都依赖于 NMDA 受体。