Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification (2407.19385v1)

Abstract: Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors. Its complex symptoms and overlap with other psychiatric conditions challenge traditional diagnostic methods, necessitating advanced systems to improve precision. Existing research studies have mostly focused on imaging data, such as structural and functional MRI, for SZ diagnosis. There has been less focus on the integration of genomic features despite their potential in identifying heritable SZ traits. In this study, we introduce a Multi-modal Imaging Genomics Transformer (MIGTrans), that attentively integrates genomics with structural and functional imaging data to capture SZ-related neuroanatomical and connectome abnormalities. MIGTrans demonstrated improved SZ classification performance with an accuracy of 86.05% (+/- 0.02), offering clear interpretations and identifying significant genomic locations and brain morphological/connectivity patterns associated with SZ.

• The paper presents MIGTrans, a transformer-based model that integrates structural and functional MRI with genomic biomarkers to classify schizophrenia.
• It employs cross-modal multi-head attention to fuse features from SNPs, connectome data, and structural MRI for enhanced diagnostic precision.
• Experimental results on the FBIRN dataset show a 3.72% improvement over single-modality methods, highlighting the benefits of multi-modal integration.

Insights into Multi-modal Imaging Genomics Transformer for Schizophrenia Classification

The discussed paper examines the development and application of a Multi-modal Imaging Genomics Transformer (MIGTrans) designed for the classification of schizophrenia, a complex and debilitating psychiatric disorder. The approach integrates different data modalities, specifically structural and functional magnetic resonance imaging (MRI), with genomic data, emphasizing the critical integration of genomics with imaging data to enhance diagnostic accuracy for schizophrenia.

Methodological Overview

MIGTrans employs a transformer-based architecture to address the challenge of integrating multi-modal data for schizophrenia classification. This model uniquely combines genomic, connectome (derived from functional MRI), and structural MRI data, representing a significant advancement over traditional single-modality approaches and simplistic fusion strategies. The application of cross-modal multi-head attention mechanisms plays a central role in attentively integrating these disparate data sources.

The development of MIGTrans consists of several methodologically distinct modules:

1. Genomic and Connectome Encoders: These modules extract discriminative features from single nucleotide polymorphisms (SNPs) and functional network connectivity (FNC), respectively. The genomic encoder uses dense layers with Gaussian Error Linear Unit (GELU) activation to learn latent genomic features, while the connectome encoder employs a similar architecture optimized for connectome data.
2. Structural MRI Encoder with Spatial Sequence Attention (SSA): Incorporating a pre-trained 3D DenseNet121, this module extracts morphological features from structural MRI scans, subsequently using SSA to capture spatial and channel dependencies, thus refining the extracted features.
3. Fusion Transformer: The model introduces a two-step fusion mechanism. First, genomic and connectome features are attentively integrated using cross-modal multi-head attention. The second step further integrates these fused features with sMRI data, leveraging inter-modal relationships to generate a comprehensive feature representation for prediction tasks.

Experimental Evaluation

The paper reports experimental results utilizing a subset of the Function Biomedical Informatics Research Network (FBIRN) dataset, featuring a total of 186 participants with schizophrenia or as healthy controls. The MIGTrans model undergoes 5-fold cross-validation to test its predictive capabilities.

The results, as summarized in Table 1 of the paper, highlight the superiority of the proposed multi-modal transformer approach over existing single-modality and feature concatenation methods. The MIGTrans demonstrates an accuracy of 86.05%, outperforming the best single-modality approach (connectome) by approximately 3.72%. The systematic integration via attentive mechanisms showcases enhanced performance via cross-modal feature learning, underscoring the utility of capturing intricate interdependencies within multi-modal data.

Implications and Future Directions

The introduction of the MIGTrans model aligns with the ongoing trend in neuroscience and bioinformatics to leverage complex data integration for improved diagnostic precision. The strong performance metrics achieved by this model suggest substantial implications for enhancing clinical decision-making and offering more personalized psychiatric assessments.

However, the research implicates several avenues for future exploration. One potential area is the extension of the model architecture to other psychiatric disorders, potentially expanding the applicability of similar integration frameworks beyond schizophrenia. Moreover, continuous refinement of genomic data representations and the exploration of additional modality-specific encoder-decoder structures could further bolster the model’s diagnostic capabilities.

Overall, the paper presents a significant contribution to the multi-modal integration discourse, detailing a sophisticated approach to marrying genomics with imaging data, thus representing a resourceful methodology for advancing schizophrenia research and treatment strategies.