Data Sheet 1_Features of mismatch negativity in an at-risk mental state with the traits associated with the autistic spectrum.docx

IntroductionAccurately distinguishing individuals with autism spectrum disorder (ASD) from those with schizophrenia spectrum disorder (SSD) can be challenging, especially in individuals with an at-risk mental state (ARMS) for psychosis. Given the need for objective markers, we focused on mismatch negativity (MMN). This study aimed to determine whether ARMS individuals with ASD traits exhibit different MMN patterns compared to ARMS individuals without such traits and healthy controls.
MethodsForty-nine individuals with ARMS and 45 healthy controls were enrolled. The Autism-Spectrum Quotient Japanese Version (AQ-J) was used to assess ASD traits, with a cut-off of 33+ indicating high ASD traits [AQ(+)] and scores below that low ASD traits [AQ(-)]. An electroencephalogram was recorded while the participants heard standard and deviant tones in two auditory oddball paradigms: a duration-deviant (dMMN) and a frequency-deviant (fMMN). MMN amplitude and latency were analyzed at Fz and group differences were compared between patients with ARMS and healthy controls. Further, within the ARMS group, AQ(-) (n = 33) vs. AQ(+) (n = 16) subgroups were examined. Correlation analyses were also performed to explore the relationships between MMN measures and clinical/cognitive indices.
ResultsNo significant differences in MMN amplitude or latency were observed between the ARMS group and healthy controls. In contrast, fMMN latency in the AQ (+) group was significantly shorter than that in the AQ(-) group. Within the entire ARMS group, fMMN latency had a significant negative correlation with total AQ-J scores, especially the Communication subscale, i.e., higher ASD traits were associated with shorter fMMN latency.
ConclusionThe key finding of this study was that ARMS individuals with higher ASD traits showed a shortened fMMN latency compared to those without. Distinguishing ARMS from ASD based solely on clinical symptoms is sometimes difficult, and using an objective measurement tool such as MMN latency could help identify underlying ASD features and guide more tailored interventions.