Democratising Machine Learning for researchers working in Alzheimer's space

研究综述 阿尔茨海默病(AD)的关键挑战之一是及早发现有患病风险的个人 发展这种情况，然后预测他们的情况将以多快的速度发展。 为了促进这一点，本提案的母公司赠款(R01-AG058676-01A1)将fiVe领先的油井- 阐明阿尔茨海默病危险因素和保护因素的阿尔茨海默氏症特征队列：成年儿童 研究(ACS)、阿尔茨海默病神经成像倡议(ADNI)、澳大利亚成像、生物标记物和 生活方式老龄化旗舰研究(AIBL)、显性遗传性阿尔茨海默病网络(DIAN)和国家 阿尔茨海默病协调中心(NACC)。由此产生的队列对于理解以下因素至关重要 加速或推迟痴呆症诊断。这个数据集的巨大规模推动了机器学习的应用-- ING(ML)和ArtifiSocial Intelligence(AI)方法，以解决对个人进行分类的关键问题 处于阿尔茨海默病的风险中，并根据疾病、症状和 功能。 然而，AD领域和ML/AI技术拥有的领域之间存在着鲜明的对比 已经习惯了巨大的成功，例如计算机视觉、文本挖掘和音频分析。特别是，越小 样本大小，即使是迄今为止收集的最大数据集，也意味着高度表达的模型同样有可能 将数据中的技术伪像检测为真实的生物信号，特别是在查看高维数据时。 最适合AI/ML的队形。此外，标准的ML/AI方法通常不能与 缺失值，这是临床AD队列的常见特征，其中某些类型的测量不同于fi 对方付费。最后，AI/ML使Fullfi实现了他们的承诺的领域是那些对这两个ML都有进入障碍的领域 而领域专家也减少了。这种民主化在很大程度上是通过提供法官- 标记已处理以移除技术人工制品的数据集，具有全面的文档和 坚持可查找、可访问、可互操作和可重复使用(公平)数据原则。 在这笔赠款中，我们寻求进行高分辨率、多模式协调和跨数据集归因于 five领导的AD团队旨在提高其对AI/ML方法的适用性，以以下形式证明这些 公平、文档齐全、易于与AI/ML框架一起使用。当母基金执行一些和声时- 汇总统计数据(例如，来自PET成像的平均淀粉样蛋白水平、认知测试汇总统计数据)和 使用经典方法进行补偿，该扩展寻求提高粒度级别(例如，整个图像， 认知测试中的单个或一小组问题)，在这些问题上执行协调，并结合 将生物先验知识转化为数据推算。这将通过利用最近在al-All方面的进展来实现。 算法偏差消除和矩阵补全，并将纳入我们对疾病过程和 正在分析的模式的性质。后者将对推论施加额外的限制，从而允许 它能够产生更准确、更合理的测量估计。我们将提供经过精心策划的版本 这些数据集，以及软件，使研究人员能够调整协调和归罪的水平 根据需要，根据开发和实施的底层算法的优缺点 这项提议。 根据本提案的母公司赠款，整合five最大的纵向AD队列可提供 这是一个探索AI/ML力量的宝贵机会，可以提高我们及早发现AD并制定 对个人随时间变化的准确预测。这项提议寻求将现代AI/ML方法 fiRMLY通过生成可无缝集成到中的经过管理的去偏向数据集 大多数现有的ML管道。通过以比以往更高的分辨率提高基础数据的质量 以前做过的，从这项工作中得出的预测和预测模型可能要强大得多 而不是过去的方法。

RESEARCH SUMMARY One of the key challenges in Alzheimer’s disease (AD) is the early detection of individuals who are at risk of developing the condition, and subsequently making predictions about how rapidly their condition will progress. To facilitate this, the parent grant of this proposal (R01-AG058676-01A1) brings together five leading well char- acterized Alzheimer’s cohorts to clarify risk and protective factors for Alzheimer’s dementia: the Adult Children Study (ACS), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing (AIBL), the Dominantly Inherited Alzheimer Network (DIAN) and the National Alzheimer’s Coordinating Center (NACC). The resulting cohort is critical for understanding the factors which precipitate or delay dementia diagnosis. The large size of this dataset motivates the application of machine learn- ing(ML) and artificial intelligence (AI) methodologies to address the critical questions of classifying individuals as at risk of AD and for making predictions about the future progression in terms of the disease, symptoms and function. However, there are stark contrasts between the AD space and those domains where ML/AI techniques have been used to great success, such as computer vision, text mining and audio analytics. In particular, the smaller sample size, even for the largest datasets collected to date, means that highly expressive models are just as likely to detect technical artefacts in the data as real biological signal, especially when looking at high-dimensional in- formation where AI/ML is most appropriate. Moreover, standard ML/AI approaches do not typically work with missing values, a common feature of clinical AD cohorts, where certain types of measurements are difficult to collect. Finally, the areas where AI/ML have fulfilled their promise are those where the barrier to entry for both ML and domain specialists has been reduced. This democratisation has been achieved largely by providing bench- mark datasets that have been processed to remove technical artefacts, have comprehensive documentation and adhere to under Findable, Accessible, Interoperable, and Reusable (FAIR) data principles. In this grant, we seek to conduct high-resolution, multi-modal harmonisation and cross-dataset imputation of the five leading AD cohorts aiming to improve their suitability for AI/ML approaches, proving these in a form that is FAIR, well-documented and easy to use with AI/ML frameworks. While the parent grant performs some harmoni- sation of summary statistics (e.g. average amyloid levels from PET imaging, cognitive test summary statistics) and imputation using classical approaches, this extension seeks to improve the level of granularity (e.g entire images, individual or small groups of questions in cognitive tests) at which harmonisation is performed, and incorporate biological prior knowledge into data imputation. This will be achieved by leveraging recent advancements in al- gorithmic bias-removal and matrix completion, and will incorporate our understanding of disease processes and the nature of the modalities being analysed. The latter will put additional constraints on the inferential, allowing it to produce more accurate and sensible estimations of measurements. We will provide FAIR-curated version of these datasets, along with software to enable researchers to adjust the level of harmonisation and imputation as needed, based on the strengths and weaknesses of the underlying algorithms developed and implemented in this proposal. The integration of the five largest longitudinal AD cohorts, as per the parent grant of this proposal, provides an invaluable opportunity to explore the power of AI/ML to improve our ability to detect AD early on and make accurate forecasts about individual’s change over time. This proposal seeks to bring modern AI/ML methods firmly into the AD community by producing curated de-biased datasets that can be seamlessly integrated into most existing ML pipelines. By improving the quality of the underlying data at a higher resolution than has been done before, predictive and prognostics models derived from this work are likely to be substantially more powerful than past approaches.