  Both functional and structural magnetic resonance imaging (fMRI and sMRI) are
widely used for the diagnosis of mental disorder. However, combining
complementary information from these two modalities is challenging due to their
heterogeneity. Many existing methods fall short of capturing the interaction
between these modalities, frequently defaulting to a simple combination of
latent features. In this paper, we propose a novel Cross-Attentive Multi-modal
Fusion framework (CAMF), which aims to capture both intra-modal and inter-modal
relationships between fMRI and sMRI, enhancing multi-modal data representation.
Specifically, our CAMF framework employs self-attention modules to identify
interactions within each modality while cross-attention modules identify
interactions between modalities. Subsequently, our approach optimizes the
integration of latent features from both modalities. This approach
significantly improves classification accuracy, as demonstrated by our
evaluations on two extensive multi-modal brain imaging datasets, where CAMF
consistently outperforms existing methods. Furthermore, the gradient-guided
Score-CAM is applied to interpret critical functional networks and brain
regions involved in schizophrenia. The bio-markers identified by CAMF align
with established research, potentially offering new insights into the diagnosis
and pathological endophenotypes of schizophrenia.
