Ricciardi Lucia, Apps Matthew, Little Simon
Neurosciences Research Centre, Molecular and Clinical Sciences Research Institute, St George's University of London, London, UK.
Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
NPJ Parkinsons Dis. 2023 Sep 21;9(1):136. doi: 10.1038/s41531-023-00567-0.
Neuropsychiatric mood and motivation symptoms (depression, anxiety, apathy, impulse control disorders) in Parkinson's disease (PD) are highly disabling, difficult to treat and exacerbated by current medications and deep brain stimulation therapies. High-resolution intracranial recording techniques have the potential to undercover the network dysfunction and cognitive processes that drive these symptoms, towards a principled re-tuning of circuits. We highlight intracranial recording as a valuable tool for mapping and desegregating neural networks and their contribution to mood, motivation and behavioral symptoms, via the ability to dissect multiplexed overlapping spatial and temporal neural components. This technique can be powerfully combined with behavioral paradigms and emerging computational techniques to model underlying latent behavioral states. We review the literature of intracranial recording studies investigating mood, motivation and behavioral symptomatology with reference to 1) emotional processing, 2) executive control 3) subjective valuation (reward & cost evaluation) 4) motor control and 5) learning and updating. This reveals associations between different frequency specific network activities and underlying cognitive processes of reward decision making and action control. If validated, these signals represent potential computational biomarkers of motivational and behavioural states and could lead to principled therapy development for mood, motivation and behavioral symptoms in PD.
帕金森病（PD）中的神经精神性情绪和动机症状（抑郁、焦虑、冷漠、冲动控制障碍）极具致残性，难以治疗，且当前药物和深部脑刺激疗法会使其恶化。高分辨率颅内记录技术有潜力揭示驱动这些症状的网络功能障碍和认知过程，从而对神经回路进行有原则的重新调整。我们强调颅内记录是一种有价值的工具，通过剖析多重重叠的空间和时间神经成分，来绘制和分离神经网络及其对情绪、动机和行为症状的影响。该技术可与行为范式和新兴计算技术有力结合，以模拟潜在的行为状态。我们参考1）情绪加工、2）执行控制、3）主观评估（奖励与成本评估）、4）运动控制以及5）学习与更新，回顾了颅内记录研究中关于情绪、动机和行为症状学的文献。这揭示了不同频率特异性网络活动与奖励决策和行动控制的潜在认知过程之间的关联。如果得到验证，这些信号代表了动机和行为状态的潜在计算生物标志物，并可能为帕金森病中情绪、动机和行为症状的有原则治疗发展提供指导。
