Brain disorder prediction can be enhanced by models that capture not only imaging phenotypes but also their underlying molecular context. Neuroimaging provides detailed structural and functional information, yet it offers limited insight into the gene-regulated processes driving these alterations. Transcriptomic atlases offer such molecular insights but are rarely available at the subject level due to invasive sampling. To address this gap, we propose CAMM, a confidence-aware multi-modal framework that integrates transcriptomic priors with imaging features to embed molecular context before fusion. CAMM further introduces a unified confidence calibration-regularization strategy that adapts modality contributions at the sample level, ensuring that information from high-confidence samples is leveraged to improve predictions for low-confidence samples, thereby enhancing robustness. Applied to large neuroimaging cohorts, CAMM consistently surpasses state-of-the-art baselines and identifies biologically meaningful biomarkers, demonstrating how transcriptomic priors can bridge molecular mechanisms and imaging for interpretable precision modeling of brain disorders.
Luo et al. (2026) studied this question.