Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut 06519, USA.
Biol Psychiatry. 2011 May 15;69(10):997-1005. doi: 10.1016/j.biopsych.2010.12.036. Epub 2011 Mar 11.
Various malfunctions involving working memory, semantics, prediction error, and dopamine neuromodulation have been hypothesized to cause disorganized speech and delusions in schizophrenia. Computational models may provide insights into why some mechanisms are unlikely, suggest alternative mechanisms, and tie together explanations of seemingly disparate symptoms and experimental findings.
Eight corresponding illness mechanisms were simulated in DISCERN, an artificial neural network model of narrative understanding and recall. For this study, DISCERN learned sets of autobiographical and impersonal crime stories with associated emotion coding. In addition, 20 healthy control subjects and 37 patients with schizophrenia or schizoaffective disorder matched for age, gender, and parental education were studied using a delayed story recall task. A goodness-of-fit analysis was performed to determine the mechanism best reproducing narrative breakdown profiles generated by healthy control subjects and patients with schizophrenia. Evidence of delusion-like narratives was sought in simulations best matching the narrative breakdown profile of patients.
All mechanisms were equivalent in matching the narrative breakdown profile of healthy control subjects. However, exaggerated prediction-error signaling during consolidation of episodic memories, termed hyperlearning, was statistically superior to other mechanisms in matching the narrative breakdown profile of patients. These simulations also systematically confused autobiographical agents with impersonal crime story agents to model fixed, self-referential delusions.
Findings suggest that exaggerated prediction-error signaling in schizophrenia intermingles and corrupts narrative memories when incorporated into long-term storage, thereby disrupting narrative language and producing fixed delusional narratives. If further validated by clinical studies, these computational patients could provide a platform for developing and testing novel treatments.
各种与工作记忆、语义、预测误差和多巴胺神经调制相关的功能障碍被假设为导致精神分裂症患者言语紊乱和妄想。计算模型可以深入了解某些机制为什么不太可能，提出替代机制，并将看似不同的症状和实验发现的解释联系起来。
在 DISCERN 中模拟了八个相应的疾病机制，DISCERN 是一个叙事理解和回忆的人工神经网络模型。在这项研究中，DISCERN 学习了具有相关情感编码的自传体和非个人犯罪故事集。此外，使用延迟故事回忆任务对 20 名健康对照者和 37 名年龄、性别和父母教育程度相匹配的精神分裂症或分裂情感障碍患者进行了研究。进行了拟合优度分析，以确定最能复制健康对照者和精神分裂症患者产生的叙事中断模式的机制。在最能匹配患者叙事中断模式的模拟中，寻找类似妄想的叙事的证据。
所有机制在匹配健康对照者的叙事中断模式方面都等效。然而，在巩固情景记忆期间过度的预测误差信号，称为过度学习，在匹配患者的叙事中断模式方面在统计学上优于其他机制。这些模拟还系统地将自传体代理与非个人犯罪故事代理混淆，以模拟固定的、自我参照的妄想。
研究结果表明，在纳入长期存储时，精神分裂症中过度的预测误差信号会混合和破坏叙事记忆，从而破坏叙事语言并产生固定的妄想性叙事。如果通过临床研究进一步验证，这些计算患者可以为开发和测试新的治疗方法提供平台。
