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.
失配负性(MMN)经常被用于评估自闭症谱系障碍(ASD)的听觉加工和变化检测,但研究结果相当不一致。为了解决这个问题,我们对ASD的MMN振幅(76个效应量)和潜伏期(62个效应量)进行了系统回顾和荟萃分析,以确定导致这种异质性的因素,并在预测编码框架内解释研究结果。虽然残余异质性仍然存在,但发现年龄组与设计类型(单特征vs多特征,即一种或几种偏差类型)和偏差类型之间的相互作用对MMN振幅有显著影响。在多特征设计中,自闭症儿童和青少年的MMN波幅比神经正常的同龄人减少(g = 0.25, p = 0.01),而自闭症成人的MMN波幅增加(g = -0.26, p = 0.02)。此外,在使用音素偏差的范式中,自闭症个体的MMN振幅显著小于神经正常个体(g = 0.41, p
{"title":"Systematic Review and Meta-Analysis of Mismatch Negativity in Autism: Insights Into Predictive Mechanisms","authors":"Laurie-Anne Sapey-Triomphe, Romain Bouet, Jérémie Mattout, Sandrine Sonié, Christina Schmitz, Françoise Lecaignard","doi":"10.1002/aur.70131","DOIUrl":"10.1002/aur.70131","url":null,"abstract":"<p>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 (<i>g</i> = 0.25, <i>p</i> = 0.01), whereas autistic adults showed increased MMN amplitudes (<i>g</i> = −0.26, <i>p</i> = 0.02). In addition, autistic individuals had significantly smaller MMN amplitudes than neurotypical individuals in paradigms using phoneme deviants (<i>g</i> = 0.41, <i>p</i> < 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.</p>","PeriodicalId":131,"journal":{"name":"Autism Research","volume":"18 12","pages":"2431-2450"},"PeriodicalIF":5.6,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/aur.70131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145410850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}