Computational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis.
Computational Psychiatry of ADHD: Neural Gain Impairments across Marrian Levels of Analysis.
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DOI：
10.1016/j.tins.2015.12.009
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发表时间：
2016-02
影响因子：
15.9
通讯作者：
Dolan RJ
中科院分区：
文献类型：
--
作者：
Hauser TU;Fiore VG;Moutoussis M;Dolan RJ
注意力缺陷多动障碍（ADHD）是最常见的精神疾病之一，其特征是多个认知领域的反应模式不稳定。然而，解释这些特征的神经机制仍不清楚。使用一个计算的多层次的方法，我们提出，ADHD是由受损的增益调制系统，产生这种表型增加行为的变异性。使用马尔的三个层次的分析作为一个启发式的框架，我们专注于这个变量的行为，详细说明它如何可以解释算法，以及它如何可能会被实施在神经水平上通过儿茶酚胺对皮质纹状体回路的影响。这种计算的，多层次的，ADHD的方法提供了一个框架，弥合神经元活动和行为的描述之间的差距，并提供了可测试的预测受损的机制。ADHD是儿童时期最常见的精神疾病之一，但其背后的神经认知机制仍然难以捉摸。在行为上，ADHD的最佳特征是多个认知领域和时间尺度的变异性增加。通过使用马尔的三个层次的分析，我们展示了如何在神经增益损伤可以解释ADHD异常，从行为到神经活动。在算法和实现层面上，我们展示了神经增益损伤如何导致变异性增加，以及如何使用强化学习和皮质纹状体网络模型对其进行建模。我们还展示了这些水平如何与儿茶酚胺系统（多巴胺和去甲肾上腺素）的损伤有关。
Attention-deficit hyperactivity disorder (ADHD), one of the most common psychiatric disorders, is characterised by unstable response patterns across multiple cognitive domains. However, the neural mechanisms that explain these characteristic features remain unclear. Using a computational multilevel approach, we propose that ADHD is caused by impaired gain modulation in systems that generate this phenotypic increased behavioural variability. Using Marr's three levels of analysis as a heuristic framework, we focus on this variable behaviour, detail how it can be explained algorithmically, and how it might be implemented at a neural level through catecholamine influences on corticostriatal loops. This computational, multilevel, approach to ADHD provides a framework for bridging gaps between descriptions of neuronal activity and behaviour, and provides testable predictions about impaired mechanisms. ADHD is one of the most common psychiatric disorders during childhood, but the neurocognitive mechanisms behind it remain elusive. Behaviourally, ADHD is best characterized by increased variability across multiple cognitive domains and timescales. By using Marr's three levels of analysis, we show how impairments in neural gain can explain ADHD abnormalities, spanning from behaviour to neural activity. On an algorithmic and implementation level, we show how increased variability can be caused by neural gain impairments, and how it can be modelled using reinforcement learning and corticostriatal network models. We furthermore show how these levels can be linked to impairments in catecholamine systems (dopamine and noradrenaline).