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）是一种以异常感知为特征的终身发育状况。预测编码最近已经成为一个统一的框架，不仅能够描述ASD的病理生理学，而且还可以在神经元和突触水平上创建可测试的自闭症感知改变的假设。在该框架内，ASD的核心异常被认为是异常贝叶斯推断的结果，这是由于先验信念的低精度（“次先验”）和/或过于精确的似然分布。因此，自闭症的知觉是由感官输入为主，较少调制的自上而下的规则化的先前experience.In本项目中，我们的目标是揭示神经机制改变的感知推理在ASD，特别是关于分类过程。我们将使用多模态方法，结合计算建模，行为评估，功能性神经成像和MR波谱。该项目包括三个工作包。在工作包1中，我们使用计算建模方法研究人脸识别。面部识别受损是ASD的主要行为缺陷。我们的目标是开发一个计算模型，描述了在行为水平上的人脸识别的学习。然后，我们将把中央模型参数（例如预测误差信号）与神经活动相关联，以了解异常的感知推断如何与神经系统水平的改变相关联（“基于模型的fMRI”）。在工作包2中，我们研究了情绪的分类，其中ASD患者通常受损。我们将使用一个新开发的情感范式，其中受试者必须对变量模糊的面部情绪进行分类。使用神经网络分析，我们将调查是否自上而下的调制，从前额叶皮层的感觉面和情绪感知区的ASD患者衰减，预测编码理论预测。在工作包3中，我们还采取了另一种方法，并测试假设的神经生理学对应的异常知觉推理ASD。在神经水平上，预测编码框架将ASD的核心异常与改变的局部GABA能中间神经元活性联系起来。这一假设将在面部感知和情绪处理网络的核心区域中用MR波谱进行测试。