Testing a computational model of neural responses in autism

项目概要/摘要 这项提案将测试一个新的，计算驱动的假设，关于自闭症的神经功能障碍 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，许多建议的一个统一主题是，神经系统的普遍破坏 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一个性质， 单个神经元;该属性如何扩展到神经回路以及它如何与行为相关- 哪种ASD被描述-没有很好地指定。神经计算模型提供了一种弥合鸿沟的方法 之间的单一单位的性质和行为，并带来必要的特异性，以测试可能的变化，E/I 在ASD一种与E/I直接相关的成熟的神经计算是“分裂归一化”， 一种计算框架，将神经反应表征为净兴奋性相对于净兴奋性的比率。 抑制性输入在这里，我们的目标是测试ASD涉及破坏分裂正常化的假设，使用 视觉作为一个模型系统。我们将测试两种可能的削弱分裂正常化的机制。第一 是传统上假定的局部，区域内电路，调解抑制驱动中断。二是 基于我们实验室最近的经验发现的新假设。我们已经显示出增强的抑制性 ASD患者从视觉处理的高级阶段到低级阶段的反应反馈。我们 这表明这种增强的抑制性反馈减少了神经元的反应，否则这些神经元将参与 分裂的正常化。这一假设对破坏的条件做出了具体的预测。 在ASD中将观察到分裂正常化。我们将使用以下函数组合来测试这些预测： MRI、ERP和弥散MRI。

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.