Zikereya Talifu, Lin Yuchen, Zhang Zhizhen, Taguas Ignacio, Shi Kaixuan, Han Chuanliang
Department of Physical Education, China University of Geosciences, Beijing, China.
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Neuroimage. 2024 Dec 15;304:120945. doi: 10.1016/j.neuroimage.2024.120945. Epub 2024 Nov 23.
The escalating global trend of aging has intensified the focus on health concerns prevalent among the elderly. Notably, Dementia related diseases, including Alzheimer's disease (AD) and frontotemporal dementia (FTD), significantly impair the quality of life for both affected seniors and their caregivers. However, the underlying neural mechanisms of these diseases remain incompletely understood, especially in terms of neural oscillations. In this study, we leveraged an open dataset containing 36 CE, 23 FTD, and 29 healthy controls (HC) to investigate these mechanisms. We accurately and clearly identified three stable oscillation targets (theta, ∼5 Hz, alpha, ∼10 Hz, and beta, ∼18 Hz) that facilitate differentiation between AD, FTD, and HC both statistically and through classification using machine learning algorithms. Overall, the differences between AD and HC were the most pronounced, with FTD exhibiting intermediate characteristics. The differences in the theta and alpha bands showed a global pattern, whereas the differences in the beta band were localized to the central-temporal region. Moreover, our analysis revealed that the relative theta power was significantly and negatively correlated with the Mini Mental State Examination (MMSE) scores, while the relative alpha and beta power showed a significant positive correlation. This study is the first to pinpoint multiple robust and effective neural oscillation targets to distinguish AD, offering a simple and convenient method that holds promise for future applications in the early screening of large-scale dementia-related diseases.
全球老龄化趋势的加剧，使得人们更加关注老年人中普遍存在的健康问题。值得注意的是，包括阿尔茨海默病（AD）和额颞叶痴呆（FTD）在内的与痴呆相关的疾病，严重损害了受影响老年人及其照顾者的生活质量。然而，这些疾病的潜在神经机制仍未完全了解，尤其是在神经振荡方面。在本研究中，我们利用一个开放数据集，其中包含36名CE患者、23名FTD患者和29名健康对照（HC），来研究这些机制。我们准确且清晰地识别出三个稳定的振荡靶点（θ波，约5Hz；α波，约10Hz；β波，约18Hz），这些靶点在统计学上以及通过使用机器学习算法进行分类，都有助于区分AD、FTD和HC。总体而言，AD与HC之间的差异最为明显，FTD表现出中间特征。θ波和α波频段的差异呈现出全局模式，而β波频段的差异则局限于中央颞区。此外，我们的分析表明，相对θ波功率与简易精神状态检查表（MMSE）评分呈显著负相关，而相对α波和β波功率呈显著正相关。本研究首次精准定位了多个强大且有效的神经振荡靶点以区分AD，提供了一种简单便捷的方法，有望在未来大规模痴呆相关疾病的早期筛查中得到应用。
