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

从单细胞到认知表型：开发具有临床应用的多模式基础人工智能模型

• 批准号: 2749643
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2749643
• 关键词: single; cells; cognitive; phenotypes; development

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.

神经退行性疾病与不同尺度的变化相关：从细胞水平的低水平变化到表型水平的高水平变化（例如，大脑形态和认知能力的变化）。捕捉不同尺度的变化，并了解这些变化如何相互关联，对于更好地了解不同病症的机制和预后以及确定个体化治疗至关重要。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，这具有挑战性、耗时且昂贵。因此，目前缺乏具有这些数据模态的大规模多模态数据集，而主要是大型单模态数据集（跨数据模态的参与者很少或没有重叠）。因此，该博士项目的目标有两个：1）通过实施和训练新型多模态基础人工智能模型来开发方法，该模型可用于利用多个当前可用的大规模数据集并学习信息结构 以及跨数据模式（例如，单细胞和认知/行为数据）泛化的转换，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，特别关注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），反之亦然。