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皮质-纹状体网络中的β波和高γ振荡反映了概率反转学习任务中的奖励确定性。

Beta and high gamma oscillations in the cortico-striatal network reflect reward certainty on a probabilistic reversal learning task.

作者信息

Koloski Miranda F, Salimi Morteza, Hulyalkar Sidharth, Tang Tianzhi, Barnes Sam A, Mishra Joyti, S Ramanathan Dhakshin

机构信息

Mental Health Service, VA San Diego Healthcare Syst., La Jolla, CA, 92161.

Dept. of Psychiatry, UC San Diego, La Jolla, CA, 92093.

出版信息

J Neurosci. 2025 Aug 26. doi: 10.1523/JNEUROSCI.0858-25.2025.

Abstract

Behavioral outcomes are rarely certain, requiring subjects to discriminate between available choices by using feedback to guide future decisions. Probabilistic reversal learning (PRL) tasks test subjects' ability to learn and flexibly adapt to changes in reward contingencies. Cortico-striatal circuitry has been broadly implicated in flexible decision-making - though what role these circuits play remains complicated. In this study we leveraged the fast temporal dynamics of local field potentials to precisely identify the role that cortico-striatal networks play during PRL reward-feedback. We measured widespread (32-CH) local field potential activity of male Long-Evans rats during a PRL task wherein a target response delivered reward on 80% of trials while a non-target response delivered reward on 20% of trials. When subjects learned those reward probabilities, contingencies were reversed. We found that reward-evoked oscillations at beta (15-30Hz) and high gamma (>70Hz) frequencies, marked positive reward valence and reflected probability of reward. Activity and connectivity at beta-frequencies between orbitofrontal cortex, anterior insula, medial prefrontal cortex, and ventral striatum during expected rewards was correlated with behavioral performance and specific aspects of value/exploitative behavior as defined by a reinforcement learning computational model. Finally, we found that modulating beta activity in orbitofrontal cortex with optogenetic (20Hz) stimulation promoted maladaptive behavior when stimulation was provided during non-target responses, consistent with our data and computational model predictions. Reward-evoked beta oscillations may reflect a crucial component underlying reward learning and erroneous elevations in this physiological signal may contribute to maladaptive task performance and behavioral disruptions. We examined how oscillatory dynamics throughout cortico-striatal regions may represent reward value when reward delivery is uncertain. Beta and high gamma oscillations mark positive reward valence, reflect reward likelihood and show greater connectivity between areas of the cortico-striatal network during successful task performance. Optogenetic stimulation of orbitofrontal cortex at beta frequencies (20 Hz) modulates task performance based on which trial (high or low probability of reward) it is applied. Together, our findings suggest that beta oscillations in the cortico-striatal network represent learned reward value that may be particularly important under conditions of uncertain or changing reward contingencies.

摘要

行为结果很少是确定的,这就要求受试者通过利用反馈来指导未来的决策,从而在可用选择之间进行区分。概率反转学习(PRL)任务测试受试者学习并灵活适应奖励偶然性变化的能力。皮质-纹状体回路广泛参与灵活决策——尽管这些回路所起的作用仍然很复杂。在本研究中,我们利用局部场电位的快速时间动态来精确确定皮质-纹状体网络在PRL奖励反馈过程中所起的作用。我们在一项PRL任务中测量了雄性Long-Evans大鼠广泛的(32通道)局部场电位活动,在该任务中,目标反应在80%的试验中给予奖励,而非目标反应在20%的试验中给予奖励。当受试者了解了这些奖励概率后,偶然性发生了反转。我们发现,β(15-30Hz)和高γ(>70Hz)频率的奖励诱发振荡标志着积极的奖励效价,并反映了奖励概率。在预期奖励期间,眶额皮质、前岛叶、内侧前额叶皮质和腹侧纹状体之间β频率的活动和连接性与行为表现以及强化学习计算模型所定义的价值/利用行为的特定方面相关。最后,我们发现,当在非目标反应期间提供光遗传学(20Hz)刺激来调节眶额皮质中的β活动时,会促进适应不良行为,这与我们的数据和计算模型预测一致。奖励诱发的β振荡可能反映了奖励学习的一个关键组成部分,而这种生理信号的错误升高可能导致适应不良的任务表现和行为紊乱。我们研究了在奖励交付不确定时,整个皮质-纹状体区域的振荡动力学如何代表奖励价值。β和高γ振荡标志着积极奖励效价,反映奖励可能性,并在成功的任务表现期间显示皮质-纹状体网络区域之间有更强的连接性。在β频率(20Hz)对眶额皮质进行光遗传学刺激会根据其应用于哪种试验(高或低奖励概率)来调节任务表现。总之,我们的研究结果表明,皮质-纹状体网络中的β振荡代表了习得的奖励价值,这在奖励偶然性不确定或变化的情况下可能尤为重要。

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