arXiv:2607.01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders. However, most large-scale datasets are unimodal, providing either neuroimaging or genetics data. We propose CALM, a framework that learns interpretable associations between brain ROIs and genetic pathways from completely disjoint populations. CALM aligns the two modalities in a shared latent space via linear projections that simultaneously match the class-conditional latent distributions and ensure group separability. These projectio
The increasing availability of large, albeit unimodal, biological datasets across genetics and neuroimaging is creating an urgent need for methods to integrate them. Advances in AI, particularly in latent space learning, are making it possible to address this data fragmentation now.
This development is crucial for accelerating biomarker discovery in complex neuropsychiatric disorders, potentially leading to more targeted diagnostics and therapies. By finding connections between genetic and neurological factors, it offers a pathway to understanding underlying disease mechanisms.
This framework changes how researchers can leverage previously incompatible datasets, enabling cross-modal analysis even when data sources are from different populations. It shifts the paradigm from requiring perfectly matched multimodal data to inferring relationships from unpaired sources.
- · Pharmaceutical companies
- · Biotech researchers
- · Neuropsychiatry
- · AI in healthcare
- · Traditional biomarker discovery methods
CALM facilitates the discovery of novel genetic and neurological biomarkers for complex brain disorders.
This improved understanding of disease mechanisms could lead to the development of more effective, personalized treatments and earlier diagnostic tools.
The success of CALM could accelerate the adoption of similar AI-driven interpretable alignment frameworks across other fragmented biomedical data domains.
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