Combining functional, structural, and morphological networks for multimodal classification of developing autistic brains.

Accumulating neuroimaging evidence suggests that abnormal functional and structural brain connectivity plays a cardinal role in the pathophysiology of autism spectrum disorder (ASD). Here, we constructed brain networks of functional, structural, and morphological connectivity using data from functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI), respectively. The neuroimaging data from a cohort of 50 individuals with ASD and 47 age-, gender- and handedness-matched TDC (age range: 5-18 years) were selected from the Autism Brain Image Data Exchange database. The combination of the fMRI, sMRI and DTI modalities connectivity features resulted in a classification accuracy of 82.69% for differentiating individuals with ASD from TDC. This accuracy surpassed that of any single modality or combination of fMRI and DTI modalities previously examined. Among the fMRI, sMRI and DTI modalities, the most distinguishing connectivity features were observed in the temporal, parietal, and occipital lobes from the DTI modality, the prefrontal and parietal lobes from the fMRI modality, and the temporal lobe from the sMRI modality. In addition, we also found that these distinguishing connectivity features can predict abnormal social interaction behaviours in ASD. These results highlight the complementary information provided by multimodal approaches, further emphasizing the pivotal role of multimodal connectivity patterns in unravelling the intricate mechanisms involved in the pathophysiology of ASD.