Chén Oliver Y, Vũ Duy Thanh, Diaz Christelle Schneuwly, Bodelet Julien S, Phan Huy, Allali Gilles, Nguyen Viet-Dung, Cao Hengyi, He Xingru, Müller Yannick, Zhi Bangdong, Shou Haochang, Zhang Haoyu, He Wei, Wang Xiaojun, Munafò Marcus, Trung Nguyen Linh, Nagels Guy, Ryvlin Philippe, Pantaleo Giuseppe
Département Médecine de Laboratoire et Pathologie, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland.
Faculté de Biologie et de Médecine, Université de Lausanne (UNIL), Lausanne, Switzerland.
bioRxiv. 2024 Mar 27:2024.03.11.584383. doi: 10.1101/2024.03.11.584383.
Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package () available at https://github.com/thanhvd18/rePLS.
阿尔茨海默病（AD）是痴呆症的主要病因。它会导致皮质厚度变化，并与认知和行为能力下降有关。这种下降会影响多项重要的日常功能，包括记忆、语言、定向、判断和问题解决能力。最近的研究在识别与单一结果相关的脑区方面取得了重要进展，比如个体的AD状态和总体认知衰退。然而，从多个脑区到多个AD结果的复杂投射仍知之甚少。这使得对多个AD结果的评估，尤其是预测变得具有挑战性，因为每个结果可能揭示该疾病一个不可或缺但又不同的方面，特别是当其中一些结果之间相关性不强时。在此，我们将残差学习、偏最小二乘法（PLS）和预测建模相结合，开发了一种可解释、可推广且可重复的方法，称为（重新PLS学习），用于（1）绘制大规模多变量脑皮质厚度数据（输入）与多变量疾病和行为数据（结果）之间的路径；（2）同时预测多个非成对相关的结果；（3）控制影响输入和结果以及两者之间路径的混杂变量（如年龄和性别）；（4）进行纵向AD疾病状态分类和疾病严重程度预测。我们在来自阿尔茨海默病神经影像倡议（ADNI）的AD患者、轻度认知障碍（MCI）受试者和认知正常个体的数据上，将所提出方法的性能与各种替代方法进行了评估。我们的结果揭示了颞叶、额叶、感觉运动区和扣带区的一些脑区，其皮质厚度可能与AD中不同认知和行为子领域的衰退相关。最后，我们描述了重新PLS的几何解释和数学支持，以提供有意义的神经生物学见解，并提供了一个可在https://github.com/thanhvd18/rePLS获取的开源软件包（）。
