Atypical perception in autism spectrum disorder:Combining computational models, functional neuroimaging and MR spectroscopy to understand aberrant perceptual mechanisms

自闭症谱系障碍的非典型知觉：结合计算模型、功能神经影像和磁共振波谱来了解异常知觉机制

• 批准号: 417284407
• 负责人: Professorin Dr. Gabriele Ende
• 金额: --
• 依托单位: Klinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/417284407?language=en
• 关键词: Atypical; perception; autism; spectrum; disorder

Autism spectrum disorder (ASD) is a lifelong developmental condition that is characterized by aberrant perception. Predictive coding has recently emerged as a unifying framework that is not only able to describe the pathophysiology in ASD, but can also create testable hypotheses of altered autistic perception at the neuronal and synaptic level. Within this framework, core abnormalities of ASD are considered as a consequence of aberrant Bayesian inference, either due to a low precision of the prior belief ("hypo-Priors") and/or an overly precise likelihood distribution. Autistic perception is thus dominated by sensory input and less modulated by top-down regularizations of prior experience.In the present project, we aim to uncover the neural mechanisms underlying altered perceptual inference in ASD, in particular with regard to categorization processes. We will use a multimodal approach, combining computational modelling, behavioral assessments, functional neuroimaging, and MR spectroscopy. The project consists of three work packages. In work package 1, we study face identification using a computational modelling approach. Impaired face identification is a major behavioral deficit is ASD. We aim to develop a computational model that describes the learning of face identities at the behavioral level. We will then correlate central model parameters (e.g. prediction error signals) with neural activity to understand how aberrant perceptual inference is associated with alterations at the neural system level ("model-based fMRI"). In work package 2, we study the categorization of emotions, in which patients with ASD are typically impaired. We will use a newly developed emotion paradigm in which subjects have to categorize facial emotions of variable ambiguity. Using neural network analyses, we will investigate whether top-down modulations from prefrontal cortices to sensory face and emotion perception areas are attenuated in ASD patients, as predicted by the predictive coding theory. In work package 3, we yet take another approach and test hypotheses about the putative neurophysiological counterparts of aberrant perceptual inference in ASD. At the neural level, the predictive coding framework links core abnormalities of ASD with altered local GABAergic interneuron activity. This hypothesis will be tested with MR spectroscopy in core regions of the face perception and emotion processing network.

自闭症谱系障碍（ASD）是一种终身发育状况，其特征是异常感知。预测性编码最近已成为一个统一的框架，不仅能够描述ASD中的病理生理学，而且还可以在神经元和突触水平上创建可改变自闭症感知的可检验的假设。在此框架内，ASD的核心异常被认为是由于贝叶斯的异常推断而被认为是由于先前信念的较低精度（“低位者”）和/或过度精确的可能性分布。因此，自闭症感知由感官输入主导，而不是由先前经验的自上而下的正规化所占据的主导。在本项目中，我们旨在揭示ASD中改变感知推断的神经机制，尤其是在分类过程中。我们将使用一种多模式方法，结合了计算建模，行为评估，功能神经影像学和MR光谱法。该项目由三个工作包组成。在工作包1中，我们使用计算建模方法研究面部识别。受损的面部识别是ASD的主要行为赤字。我们旨在开发一个计算模型，描述在行为层面学习面部身份的学习。然后，我们将将中心模型参数（例如预测误差信号）与神经活动相关联，以了解异常感知推断如何与神经系统级别的变化有关（“基于模型的fMRI”）。在工作包2中，我们研究了情绪的分类，其中通常会损害ASD患者。我们将使用新发展的情感范式，其中受试者必须对可变歧义的面部情绪进行分类。通过使用神经网络分析，我们将调查从前额叶皮层到感觉面部和情感感知区域的自上而下的调节，如预测性编码理论所预测的那样。在工作包3中，我们仍采用另一种方法并检验有关ASD异常感知推断的假定神经生理学的假设。在神经层面，预测编码框架将ASD的核心异常与局部Gabaergic Interneuron活性联系起来。该假设将在面部感知和情感处理网络的核心区域中通过MR光谱进行检验。