A Feature Fusion Framework for Improved Autism Spectrum Disorder Prediction Using sMRI and Phenotype Information

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and severity, posing significant challenges for accurate diagnosis. Approaches that rely on a single data source, or unimodal data, often fail to capture the disorder’s inherent heterogeneity. A multimodal approach, which integrates diverse data types, can create a more holistic and precise understanding of ASD. This paper introduces the Multimodal ASD (MMASD) framework, a novel predictive model for ASD. The MMASD framework is built upon two distinct input modalities: structural magnetic resonance imaging (sMRI) and corresponding phenotype data. The sMRI data provides detailed neuroanatomical metrics, including brain tissue segmentation, volumetric measurements, and cortical thickness. Complementing this, the phenotype data encompasses the clinical and behavioral characteristics of each individual. In the proposed framework, latent features are independently extracted from both modalities and then fused to generate a comprehensive representation of the multimodal information. These fused features are then used to predict ASD by leveraging the outputs of various classifiers. A majority voting ensemble is employed to determine the final prediction. The MMASD framework achieves a high accuracy of 97.27%, surpassing the performance of current state-of-the-art approaches and demonstrating the efficacy of integrating neuroimaging and clinical data for ASD prediction.