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CAMM: Confidence-Aligned Multiview Multimodal Fusion for Brain Disorders Prediction With Imaging Transcriptomics
IEEE Journal of Biomedical and Health Informatics
|March 18, 2026
Summary
This study introduces CAMM, a novel framework integrating brain imaging with gene expression data for improved brain disorder prediction. It enhances model robustness by prioritizing high-confidence samples, leading to better biomarker discovery.
Area of Science:
- Neuroscience
- Genomics
- Computational Biology
Background:
- Neuroimaging offers structural and functional brain insights but lacks molecular context.
- Transcriptomic data provides molecular details but is difficult to obtain at the individual level.
- Integrating imaging and molecular data is crucial for understanding brain disorders.
Purpose of the Study:
- To develop a confidence-aware multi-modal framework (CAMM) for brain disorder prediction.
- To embed molecular context from transcriptomic data into neuroimaging features.
- To enhance model robustness and interpretability in precision medicine.
Main Methods:
- CAMM integrates transcriptomic priors with imaging features for multi-modal fusion.
- A confidence calibration-regularization strategy adapts modality contributions per sample.
- High-confidence samples inform predictions for low-confidence samples, improving robustness.
Main Results:
- CAMM consistently outperformed state-of-the-art baselines on large neuroimaging cohorts.
- The framework identified biologically meaningful biomarkers for brain disorders.
- CAMM demonstrated effective integration of molecular and imaging data.
Conclusions:
- CAMM successfully bridges molecular mechanisms and imaging data for brain disorder prediction.
- The confidence-aware approach enhances model robustness and interpretability.
- This framework advances precision modeling for neurological conditions.
