Conférence CRNL avec Laurie-Anne Sapey-Triomphe (PhD) : Assessing the predictive coding accounts of Autism Spectrum Disorders – 22 mars 2022 – CH Le Vinatier – Bron

Une conférence organisée par le CRNL est prévue le mardi 22 mars 2022 de 11h00 à 13h00 au CRNL – CH Le Vinatier – Bâtiment 462 Neurocampus Michel Jouvet – Amphithéâtre Neurocampus, 95 Boulevard Pinel, Bron.

Elle se sera animée par Laurie-Anne Sapey-Triomphe (PhD) du Leuven Brain Institute et du Leuven Autism Research (LAuRes) – Louvain (Belgique)

Assessing the predictive coding accounts of Autism Spectrum Disorders

Abstract

Autism Spectrum Disorder (ASD) is characterized by heterogeneous social and non-social symptoms. Recent predictive coding theories have attempted to account for this heterogeneity in an integrated theory by suggesting that atypical perceptual learning could play a central role in ASD. Specifically, priors, which capture the underlying statistical regularities of the environment, may be atypically learnt in ASD. Other hypotheses suggest that sensory sensitivity may be too high in ASD, leading to an atypical sensory/prior ratio. We conducted a series of behavioral and neuroimaging experiments aiming at characterizing how autistic individuals learn and adjust their priors. Across these studies, we showed that autistic adults can implicitly learn a prior and have their percepts biased by their expectations. Yet, they tend to have more difficulties in adjusting their priors to the context than neurotypicals. Furthermore, we identified some neural and molecular correlates of prediction learning in ASD using model-based fMRI and Magnetic Resonance Spectroscopy. The neural network encoding prior knowledge and prediction errors was generally similar in adults with and without ASD, but the ASD group activated more strongly some regions involved in encoding high-level priors and high-level prediction errors, while no differences were found at the lower level of the hierarchy. Furthermore, we found that the glutamate/GABA ratio in a region involved in prediction learning (i.e., inferior frontal gyrus) was correlated with the ability to learn predictions. Finally, using EEG associated with fast periodic visual stimulation, behavioral tasks and questionnaires, we characterized sensory sensitivity at multiple levels to test the hypothesis of an increased sensory precision in ASD. Altogether, these results help refining the predictive coding theories of ASD and shed light on the potential underlying neural mechanisms.

Plus d’informations sur le site du CRNL : https://www.crnl.fr/fr/evenement/assessing-predictive-coding-accounts-autism-spectrum-disorders