Statsenko Yauhen, Habuza Tetiana, Charykova Inna, Gorkom Klaus Neidl-Van, Zaki Nazar, Almansoori Taleb M, Baylis Gordon, Ljubisavljevic Milos, Belghali Maroua
College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates.
Big Data Analytics Center (BIDAC), United Arab Emirates University, Al Ain, United Arab Emirates.
Front Aging Neurosci. 2021 Jul 12;13:661514. doi: 10.3389/fnagi.2021.661514. eCollection 2021.
Neuronal reactions and cognitive processes slow down during aging. The onset, rate, and extent of changes vary considerably from individual to individual. Assessing the changes throughout the lifespan is a challenging task. No existing test covers all domains, and batteries of tests are administered. The best strategy is to study each functional domain separately by applying different behavioral tasks whereby the tests reflect the conceptual structure of cognition. Such an approach has limitations that are described in the article. Our aim was to improve the diagnosis of early cognitive decline. We estimated the onset of cognitive decline in a healthy population, using behavioral tests, and predicted the age group of an individual. The comparison between the predicted ("cognitive") and chronological age will contribute to the early diagnosis of accelerated aging. We used publicly available datasets (POBA, SSCT) and Pearson correlation coefficients to assess the relationship between age and tests results, Kruskal-Wallis test to compare distribution, clustering methods to find an onset of cognitive decline, feature selection to enhance performance of the clustering algorithms, and classification methods to predict an age group from cognitive tests results. The major results of the psychophysiological tests followed a U-shape function across the lifespan, which reflected the known inverted function of white matter volume changes. Optimal values were observed in those aged over 35 years, with a period of stability and accelerated decline after 55-60 years of age. The shape of the age-related variance of the performance of major cognitive tests was linear, which followed the trend of lifespan gray matter volume changes starting from adolescence. There was no significant sex difference in lifelong dynamics of major tests estimates. The performance of the classification model for identifying subject age groups was high. ML models can be designed and utilized as computer-aided detectors of neurocognitive decline. Our study demonstrated great promise for the utility of classification models to predict age-related changes. These findings encourage further explorations combining several tests from the cognitive and psychophysiological test battery to derive the most reliable set of tests toward the development of a highly-accurate ML model.
衰老过程中神经元反应和认知过程会减慢。变化的起始、速率和程度在个体之间差异很大。评估整个生命周期的变化是一项具有挑战性的任务。现有的测试都不能涵盖所有领域，因此需要进行一系列测试。最佳策略是通过应用不同的行为任务分别研究每个功能领域，从而使测试反映认知的概念结构。本文描述了这种方法存在的局限性。我们的目标是改善早期认知衰退的诊断。我们使用行为测试估计健康人群中认知衰退的起始时间，并预测个体的年龄组。预测（“认知”）年龄与实际年龄之间的比较将有助于早期诊断加速衰老。我们使用公开可用的数据集（POBA、SSCT）和皮尔逊相关系数来评估年龄与测试结果之间的关系，使用克鲁斯卡尔-沃利斯检验来比较分布，使用聚类方法来找出认知衰退的起始时间，使用特征选择来提高聚类算法的性能，以及使用分类方法根据认知测试结果预测年龄组。心理生理测试的主要结果在整个生命周期中呈U形函数，这反映了已知的白质体积变化的倒U形函数。在35岁以上的人群中观察到最佳值，55 - 60岁后出现一段稳定期和加速衰退期。主要认知测试表现的年龄相关方差形状呈线性，这与从青春期开始的全生命周期灰质体积变化趋势一致。主要测试估计值的终身动态在性别上没有显著差异。识别受试者年龄组的分类模型表现良好。机器学习模型可以设计并用作神经认知衰退的计算机辅助检测工具。我们的研究表明分类模型在预测年龄相关变化方面具有很大的应用前景。这些发现鼓励进一步探索，结合认知和心理生理测试组合中的多项测试，以得出最可靠的测试集，用于开发高度准确的机器学习模型。
