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

• Applicants: Professorin Dr. Gabriele Ende; Professor Dominik M. Endres, Ph.D.; Professor Dr. Andreas Jansen; Professorin Dr. Inge Kamp-Becker
• Subject Area: Clinical Psychiatry, Psychotherapy, Child and Adolescent Psychiatry ; General, Cognitive and Mathematical Psychology ; Human Cognitive and Systems Neuroscience
• Term: from 2018 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 417284407

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.

• DFG Programme: Research Grants