Discovering early biomarkers of Alzheimer's disease using genetic and physics-informed networks

利用遗传和物理信息网络发现阿尔茨海默病的早期生物标志物

• 批准号: 2904538
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2904538
• 关键词: Discovering; early; biomarkers; Alzheimer; disease; Discovering; early; biomarkers; Alzheimer; disease

Aims of the Project:To derive novel biomarkers of Alzheimer's disease derived cortical diffusion and PETTo use these to constrain physics-informed models of disease progressionTo compare against regional gene-expression through precision mapping to the Allen Brain AtlasSummary:Detecting early biomarkers of neurodegeneration is a highly challenging problem due to the complex organisational structure and high degree of variation of the human brain. Approximately 50% of dementia sufferers are thought to go undiagnosed in early stages. This limits treatment options and presents significant challenges for patient screening for clinical trials.Recent studies have indicated that measures of cortical microstructure may present effective, non-invasive markers of early neurodegeneration [1,2]. However, so far these measures have been reported as summary measures averaged across the brain, when it is well known that cellular organisation varies significantly across the cortex, and that the presentation of dementia varies across individuals.At the same time, recent work in mouse models has shown that the progression of tau pathology through the brain is extremely well constrained by neuronal connectivity, and that deviations from simple models of disease progression can be well explained by gene expression [3]. Similarly inspired models, trained on humans, have been constrained using positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Iniative (ADNI) open dataset [4]. However, thus far these have been limited to global average models of brain organisation, not considering individual variability.The goal of this project will therefore be to build precision models of the microstructural organisation of individual human brains [5-10], and to use these to constrain geometric deep learning [7, 11, 12] and biophysically-informed neural networks [4,13,14] models of Alzheimer's disease progression. Findings would be compared against gene expression, to inform mechanistic understanding of the disease, and improve early diagnosis.

该项目的目的：为阿尔茨海默氏病的新生物标志物提供了衍生的皮质扩散，PETTO使用它们来限制疾病进展的物理形式模型与区域基因表达相比与艾伦大脑的精确映射到艾尔伦大脑抗液学：对人的脑部质疑的早期挑战性，并且是对组织的高度质疑和组织的高度质疑，并且是对组织的高度质疑的。人们认为，大约50％的痴呆症患者在早期未诊断出来。这限制了治疗选择，并为临床试验的患者筛查提出了重大挑战。截止性研究表明，皮质微观结构的测量可能会出现早期神经变性的有效，无创的标记[1,2]。然而，到目前为止，这些措施已报告为整个大脑的平均措施，众所周知，众所周知，细胞组织在整个皮质中都显着变化，并且痴呆症的呈现在各个人的同一时间各不相同。在同一时间，鼠标模型中的最新工作表明，通过神经元的连接性驱动良好，可以很好地抑制tau病理学通过大脑的发展，而这些疾病的表现很大，而疾病的表达方式很大。同样的启发模型，受过人类训练的模型，也使用正电子发射断层扫描（PET）数据受到限制，来自阿尔茨海默氏病神经影像学（ADNI）开放数据集[4]。但是，到目前为止，这些仅限于大脑组织的全球平均模型，而不是考虑个体可变性。因此，该项目的目标是建立人类大脑的微观结构组织的精确模型[5-10]，并使用这些模型来限制几何深度学习[7，11，12]和生物物质上信息的神经网络[4,13,14] Alzheim的模型。将发现与基因表达相比，以告知对疾病的机械理解并改善早期诊断。