From single cells to cognitive phenotypes: development of multimodal foundational AI models with clinical applications

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Neurodegenerative conditions are associated to changes at different scales: from low level changes at the cell level to high level ones at the phenotype level (e.g., changes in brain morphology and cognitive performance). Capturing changes across different scales, and learning how these changes are connected, is fundamental to better understand the mechanisms and prognosis of different conditions, and to identify individualised treatments.A way to achieve this would be to collect large-scale multimodal datasets with cognitive, imaging and single cell data across both healthy controls and clinical populations. However, this is challenging, time consuming and expensive. As a result, large scale multimodal datasets with these data modalities are currently lacking, and, instead, mainly big unimodal datasets (with minimal to no overlap in participants across data modalities) are available.The aims of this PhD project are, therefore, twofold: 1) methodological development through the implementation and training of novel multimodal foundational AI models that can be used to leverage multiple currently available large scale datasets and learn information constructs and transforms that generalise across data modalities (e.g., single cell and cognitive/behavioural data), 2) Clinical translatability by using these models for concrete clinical applications (e.g., subtype discovery, disease prognosis and precision medicine), with a special interest towards learning the biological changes (e.g., changes in single cell gene expression) underlying different cognitive states, and vice-versa.