Publication avec DOI
Récupéré
Publication
crossref
Laurie‐Anne Sapey‐Triomphe, Romain Bouet, Jérémie Mattout, Sandrine Sonié, Christina Schmitz, Françoise Lecaignard
(2025).
Systematic Review and Meta‐Analysis of Mismatch Negativity in Autism: Insights Into Predictive Mechanisms.
Autism Research, 18(12), 2431-2450.
Wiley.
- Date de publication
-
30/10/2025
- Identifiant
-
10.1002/aur.70131
- Auteurs
-
Laurie‐Anne Sapey‐Triomphe,
Romain Bouet,
Jérémie Mattout,
Sandrine Sonié,
Christina Schmitz,
Françoise Lecaignard
- Source
- Autism Research
- Détails
- 18(12), 2431-2450
- Type de référence
- article
- Éditeur
- Wiley
- Source de métadonnées
- crossref
Résumé
ABSTRACT Mismatch negativity (MMN) has been frequently used to assess auditory processing and change detection in autism spectrum disorder (ASD), but findings have been fairly inconsistent. To address this issue, we conducted a systematic review and meta‐analysis of MMN amplitude (76 effect sizes) and latency (62 effect sizes) in ASD to identify factors contributing to this heterogeneity and to interpret findings within the predictive coding framework. While residual heterogeneity remained, significant effects of the interaction between age group and design type (unifeature vs. multifeature, i.e., one or several types of deviants) and deviant type were found for MMN amplitude. In multifeature designs, autistic children and adolescents exhibited reduced MMN amplitudes compared to neurotypical peers (g = 0.25, p = 0.01), whereas autistic adults showed increased MMN amplitudes (g = −0.26, p = 0.02). In addition, autistic individuals had significantly smaller MMN amplitudes than neurotypical individuals in paradigms using phoneme deviants (g = 0.41, p < 0.001). Across designs, no significant MMN latency differences were observed between neurotypical and autistic individuals. These results are discussed within the predictive coding framework, as MMN responses are thought to reflect prediction errors, aligning with theories suggesting heightened prediction errors in autistic adults. Future studies with larger samples and improved data reporting are needed to further clarify the developmental trajectory and variability of MMN responses in ASD. Additionally, computational modeling approaches can help characterize learning dynamics and disentangle predictive coding accounts among autistic individuals.
Auteur·ices de l’étude dans cette référence citée