A Multiview Brain Network Transformer Fusing Individualized Information for Autism Spectrum Disorder Diagnosis

• Published in: IEEE journal of biomedical and health informatics Vol. 28; no. 8; pp. 4854 - 4865
• Main Authors: Dong, Qunxi, Cai, Hongxin, Li, Zhigang, Liu, Jingyu, Hu, Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2024
• Subjects: Algorithms; Attention network; Autism; autism spectrum diso rder; Autism Spectrum Disorder - diagnostic imaging; Autism Spectrum Disorder - physiopathology; Biomarkers; Brain - diagnostic imaging; Brain modeling; brain network; Child; Computer aided diagnosis; Data models; Feature extraction; fusion; Humans; Image Interpretation, Computer-Assisted - methods; individualized; Magnetic Resonance Imaging - methods; Male; transformer; Transformers
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3396457

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.