Institute for Adaptive and Neural Computation, Edinburgh University, UK; Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh University, UK.
Division of Psychiatry, Royal Edinburgh Hospital, Edinburgh University, UK.
Neurosci Biobehav Rev. 2017 Dec;83:631-646. doi: 10.1016/j.neubiorev.2017.08.022. Epub 2017 Sep 1.
Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.
（1）使用行为的抽象模型（例如贝叶斯模型-精神病理学的自上而下描述性模型）来研究精神分裂症中观察到的各种症状；（2）使用涉及异常神经调制和/或受体失衡的生物现实模型（例如连接主义和神经网络-神经过程的自下而上现实模型）来研究这些症状的原因。这些不同的分析层次已被用于回答有关该疾病性质的不同问题（即了解行为与神经生物学异常）。因此，这些计算研究主要支持了精神分裂症病理生理学的不同假设，导致文献的扩展并不总是一致。然而，其中一些假设可以使用新的经验证据进行修订。在这里，我们首先回顾了支持多巴胺、谷氨酸、GABA、连接中断和贝叶斯推理假说的精神分裂症和精神病症状的计算模型文献，并将其分类，其次，我们将模型预测与积累的经验证据进行比较，最后，我们确定了一些相对研究较少的特定假设。
