Abstract
<jats:p>Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders. The absence of a clear aetiology strengthens the need to identify quantitative biomarkers capable of diagnosing the condition and assessing the severity of symptoms. We implemented a normative modelling framework based on EEG effective connectivity (EC), leveraging recordings collected in three centers, in typical development (TD) and ASD participants with age spanning from 5 to 19 years. Deviations from the normative evolution were tested as ASD biomarkers, compared with standard EC metrics. Normative EC metrics supported accurate classification of ASD vs TD participants (0.82 AUC), reaching 0.95 AUC when combined with behavioral subscales, significantly outperforming a classification based on behavioral data alone. In ASD participants, selected metrics in the Temporo-Parietal-Frontal network predicted social responsiveness scale (SRS) values with high significance (SRS Total R^2=0.42, p=0.0005). These results suggest that normative EC values constitute robust and generalizable ASD biomarkers.</jats:p>