Learning heritable multimodal brain representation via contrastive learning
Xia, T.; Zhao, X.; Islam, S. S. M.; Mohammed, K. K.; Xie, Z.; Zhi, D.
Show abstract
Magnetic resonance imaging (MRI)-derived phenotypes (IDP) has enabled the discovery of numerous genomic loci associated with brain structure and function. However, most existing IDPs and learned representations are derived from a single imaging modality, missing complementary information across modalities and potentially limiting the scope of genetic discovery. Here, we introduce a multimodal contrastive learning framework to derive heritable representations from paired T1- and T2-weighted MRIs. Unlike single-modality reconstruction-based models, we designed a momentum-based contrastive learning framework. As a result, our approach offers improved prediction of traditional IDPs, age, and brain disorders. Notably, genome-wide association studies (GWAS) of the learned representations reveal a substantially higher overlap of genetic loci across modalities, indicating improved alignment of their underlying genetic architecture. Analysis of the GWAS loci identified shared protein and drug targets, yielding meaningful biological insights. Overall, our framework learns shared representations across brain imaging modalities that exhibit anatomical and genetic coherence.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A novel classification framework for genome-wide association study of whole brain MRI images using deep learning 95%
- Searching through functional space reveals distributed visual, auditory, and semantic coding in the human brain 94%
- Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data 94%
Similar papers in this journal
- Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer 94%
- COSIME: Cooperative multi-view integration with Scalable and Interpretable Model Explainer 94%
- Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks 93%
Similar papers in this journal
- SpaTM: Topic Models for Inferring Spatially Informed Transcriptional Programs 95%
- Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification 93%
- SHEST: Single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell type prediction and spatial transcriptomics reconstruction 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.