Abstract：
BACKGROUND: Impulse control disorders (ICD) in Parkinson\'s disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care.
METHODS: We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis.
RESULTS: Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.
CONCLUSIONS: Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.
METHODS: We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis.
RESULTS: Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use.
CONCLUSIONS: Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.
摘要：
背景：帕金森病（PD）中的冲动控制障碍（ICD）与患者和看护者的沉重负担有关。虽然可以恢复，尽管管理优化，但许多患者的ICD仍然存在。恢复中这种个体间差异的基础尚不清楚，对个性化医疗保健构成了重大挑战。
方法：我们采用计算精神病学方法，并利用纵向，前瞻性个性化帕金森项目(N=136名PD患者，诊断后5年内）将多巴胺能学习理论的fMRI与机器学习（基线）相结合，以预测随访两年后的ICD症状恢复。我们专注于整个队列中QUIP-rs的变化，无论ICD诊断如何。
结果：增益试验期间的强化学习信号更大，而基线时的损失试验则没有，包括腹侧纹状体，内侧前额叶皮质和服用药物时测得的行为准确性评分与两年后冲动控制症状的更大恢复相关。这些信号占其他已知因素解释的相关变异性的唯一比例，例如减少多巴胺激动剂的使用。
结论：我们的结果为将基于生成模型的潜在学习过程推断与基于机器学习的临床症状恢复轨迹变异性预测模型相结合提供了原理证明。因此，我们表明RL建模参数可预测PDICD症状的恢复。
方法：我们采用计算精神病学方法，并利用纵向，前瞻性个性化帕金森项目(N=136名PD患者，诊断后5年内）将多巴胺能学习理论的fMRI与机器学习（基线）相结合，以预测随访两年后的ICD症状恢复。我们专注于整个队列中QUIP-rs的变化，无论ICD诊断如何。
结果：增益试验期间的强化学习信号更大，而基线时的损失试验则没有，包括腹侧纹状体，内侧前额叶皮质和服用药物时测得的行为准确性评分与两年后冲动控制症状的更大恢复相关。这些信号占其他已知因素解释的相关变异性的唯一比例，例如减少多巴胺激动剂的使用。
结论：我们的结果为将基于生成模型的潜在学习过程推断与基于机器学习的临床症状恢复轨迹变异性预测模型相结合提供了原理证明。因此，我们表明RL建模参数可预测PDICD症状的恢复。
