Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease

阿尔茨海默病深度学习神经影像内表型的遗传学

• 批准号: 10653800
• 负责人: MYRIAM FORNAGE
• 金额: $ 37.37万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10653800
• 关键词: 3-Dimensional; Address; Administrative Supplement; Affect; Aging; Alzheimer' s Disease; American; Artificial Intelligence; Attention; Award; Brain; Caregivers; Data; Data Reporting; Dependence; Evaluation; Genes; Genetic; Genetic Databases; Genetic Processes; Genome; Goals; Heritability; Image; Image Analysis; Learning; Light; Literature; Magnetic Resonance Imaging; Maps; Methods; Modality; Neurodegenerative Disorders; Pain; Parents; Partner in relationship; Patients; Performance; Process; Research; Resolution; Scheme; Societies; Structure; base; brain magnetic resonance imaging; cost; deep learning; deep neural network; design; endophenotype; gene discovery; genetic information; genome wide association study; high dimensionality; imaging genetics; learning strategy; machine learning method; neuroimaging; novel; parent project

Project Summary/Abstract Alzheimer's disease (AD) is a severe neurodegenerative disease affecting aging Americans and generates dra- matic costs and pains on patients, caregivers, and the society. This administrative supplement proposes to develop new deep learning methods that integrate neuroimaging data with genetic information, thereby generating enriched gene-related data representations. There are at least three challenges to accomplish this research objective. Firstly, commonly used image encoders employ deep neural networks to extract high-level features for downstream tasks, but gene-related information can be contained in features at different levels and resolutions in deep neural networks. Secondly, using deep learning to process genetic data has been largely unexplored in literature. It is hard to design an effective encoder to tackle high-dimensional and discrete genetic data based on deep learning methods. Thirdly, there lacks a principled contrastive learning framework to learn from both imaging and genetic data for GWAS pur- poses. In this administrative supplement, we propose a novel trans-modality contrastive learning framework (TM-CL) to address these limitations and then faithfully accomplish our research goal. Our TM-CL contains novel and spe- cially designed imaging and genetic encoders to process brain MRI data and high-dimensional genetic data as well as a novel contrastive learning scheme to learn enriched gene-related information to benefit downstream tasks such as GWAS. Specifically, TM-CL contains a uniquely designed MRI encoder to integrate features at different scales and resolutions. In addition, our MRI encoder contains an attention-based multi-scale global transformation to extract global information from MRI data. Overall, gene-related information contained in MRI data can be largely captured in the representations. We also design a transformer-based genetic encoder for computing genetic representations. As genetic data is high-dimensional and discrete, it is hard to design deep learning based encoders to process such data. Our genetic encoder is proposed based on swin-transformer, where window attention and shifted window attention are designed to perform attention within splitting windows. By doing this, our genetic encoder can aggregate sufficient gene-related information, while largely reducing the computing cost. More importantly, when performing attention in our genetic encoder, the computed attention scores can capture three types of dependencies among different SNPs, As a result, the complicated genetic dependencies of the input genome data can be effectively captured. Finally, we propose a novel trans-modality contrastive learning scheme for integrating imaging data and genetics. Based on the proposed MRI and genetic encoders, we perform mutual information maximization between the MRI representation and genetic representation as the learning objective. Our contrastive framework is able to generate more informa- tive and discriminate gene-related representations, boosting the performance of downstream tasks such as GWAS. By faithfully accomplishing these research goals, we expect to facilitate the discovery of new genes relevant to AD, thereby shedding light on the causes and cures of AD.

项目摘要/摘要 阿尔茨海默氏病（AD）是一种严重的神经退行性疾病 对患者，看护人和社会的痛苦和痛苦。这项行政补充提案 将神经影像数据与遗传信息整合在一起的新深度学习方法，从而产生丰富的 与基因相关的数据表示。实现这一研究目标至少有三个挑战。首先， 常用的图像编码器员工深神经网络以提取下游任务的高级功能， 但是，与基因相关的信息可以包含在不同级别的特征和深度神经网络中的分辨率中。 其次，在文献中，使用深度学习来处理遗传数据。很难设计 一种有效的编码器，可根据深度学习方法处理高维和离散遗传数据。第三， 那里缺乏主要的对比学习框架，可以从成像和遗传数据中学习，以 姿势。在这种行政补充中，我们提出了一个新颖的跨模式对比度学习框架（TM-CL） 解决这些局限性，然后忠实地实现我们的研究目标。我们的TM-CL包含小说和Spe- 经过CER设计的成像和遗传编码器，以处理大脑MRI数据和高维遗传数据 作为一种新颖的对比学习计划，用于学习丰富的基因相关信息，以便在下游任务中受益 作为GWAS。特定的TM-CL包含一个独特设计的MRI编码器，以在不同尺度上整合特征 和决议。此外，我们的MRI编码器包含一个基于注意力的多尺度全局转换来提取 来自MRI数据的全球信息。总体而言，可以在很大程度上捕获MRI数据中包含的基因相关信息 在表示中。我们还设计了一个基于变压器的遗传编码器，用于计算遗传表示。作为 遗传数据是高维和离散的，很难设计基于深度学习的编码器来处理此类数据。 我们的遗传编码器是基于Swin-Transformer提出的，窗户的注意力和转移的窗户注意 旨在在拆分窗户内进行注意。通过这样做，我们的遗传编码器可以汇总 与基因相关的信息，同时大大降低了计算成本。更重要的是，在执行注意力时 在我们的遗传编码器中，计算出的注意分数可以捕获不同SNP之间的三种类型的依赖项： 结果，可以有效捕获输入基因组数据的复杂遗传依赖性。最后，我们 提出一种新型的跨模式对比度学习方案，用于整合成像数据和遗传学。基于 提出的MRI和遗传编码器，我们在MRI表示之间执行相互信息最大化 和遗传表示作为学习目标。我们的对比框架能够产生更多信息 与基因相关的表示形式，促进了诸如GWAS之类的下游任务的性能。 通过忠实地完成这些研究目标，我们希望促进发现与AD相关的新基因， 从而阐明了AD的原因和治疗方法。