Testing a computational model of neural responses in autism

项目摘要/摘要 这项提议将检验一种新的、基于计算的自闭症神经功能障碍假说。 谱系障碍(ASD)。ASD是一种病因不明的异质性神经发育障碍。 然而，众多提议的一个统一主题是，神经的普遍颠覆 兴奋/抑制(E/I)平衡。E/I假设的一个主要局限性是它描述了 单个神经元；这种特性如何扩展到神经电路，以及它如何与行为相关-水平 哪个ASD被描述-没有很好地说明。神经计算模型提供了一种弥合鸿沟的方法 单一单位属性和行为之间的关系，并带来必要的特异性来测试E/I中可能的变化 在ASD中。一种与E/I直接相关的成熟的神经计算是“除性归一化”，即 以净兴奋与净兴奋之比来表征神经反应的计算框架 压抑的输入。在这里，我们的目标是检验ASD涉及中断的分裂归一化的假设 视觉作为一种模型系统。我们将测试两种可能的削弱分裂正常化的机制。第一 是传统上认为的局部、区域内调节抑制性驱动的电路中断。第二个是 一种基于我们实验室最新经验发现的新假说。我们表现出了更强的抑制力 ASD患者视觉加工较高阶段对较低阶段反应的反馈。我们 提示这种增强的抑制反馈减少了神经元的反应，否则就会参与 分裂的正规化。这一假设对扰乱的条件做出了具体的预测 ASD将观察到分裂的正常化。我们将使用泛函组合来测试这些预测 磁共振成像、事件相关电位和弥散成像。

PROJECT SUMMARY/ABSTRACT This proposal will test a novel, computationally-motivated hypothesis about neural dysfunction in autism spectrum disorder (ASD). ASD is a heterogeneous neurodevelopmental disorder of unknown etiology. However, a unifying theme of numerous proposals is that there is a pervasive disruption of neural excitatory/inhibitory (E/I) balance. A major limitation of the E/I hypothesis is that it describes a property of individual neurons; how that property scales up to neural circuits and how it relates to behavior – the level at which ASD is described – is not well specified. Neural computational models offer a way to bridge the divide between single-unit properties and behavior, and bring the necessary specificity to test possible changes in E/I in ASD. One well-established neural computation that directly relates to E/I is “divisive normalization”, a computational framework that characterizes neural responses as the ratio of net excitatory relative to net suppressive input. Here we aim to test the hypothesis that ASD involves disrupted divisive normalization using vision as a model system. We will test two possible mechanisms of weakened divisive normalization. The first is the traditionally posited disruption of local, within-area circuits that mediate suppressive drive. The second is a novel hypothesis based on recent empirical findings in our lab. We have shown enhanced suppressive feedback of responses from higher stages to lower stages of visual processing in individuals with ASD. We suggest this enhanced suppressive feedback reduces responses of neurons that would otherwise participate in divisive normalization. This hypothesis makes specific predictions about the conditions under which disrupted divisive normalization will be observed in ASD. We will test these predictions using a combination of functional MRI, ERP, and diffusion MRI.