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

神经退行性疾病与不同尺度的变化有关：从细胞水平的低水平变化到表型水平的高水平变化（例如，脑形态和认知表现的变化）。捕捉不同尺度的变化，并了解这些变化之间的联系，对于更好地理解不同疾病的机制和预后，以及确定个性化治疗是至关重要的。实现这一目标的一种方法是收集健康对照和临床人群的认知、成像和单细胞数据的大规模多模式数据集。然而，这是具有挑战性的，耗时且昂贵的。因此，目前缺乏具有这些数据模式的大规模多模态数据集，取而代之的是主要的大型单模态数据集（跨数据模式的参与者很少或没有重叠）。因此，这个博士项目的目的是双重的：1)通过实施和培训新的多模态基础人工智能模型来开发方法学，这些模型可用于利用多个当前可用的大规模数据集，并学习跨数据模式（例如，单细胞和认知/行为数据）的信息构建和转换；2)通过将这些模型用于具体的临床应用（例如，亚型发现、疾病预后和精准医学）来实现临床可翻译性；对学习不同认知状态下的生物学变化（例如，单细胞基因表达的变化）特别感兴趣，反之亦然。

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.