Distinguishing between Alzheimer’s disease (AD) and frontotemporal dementia (FTD) presents a clinical challenge. Inexpensive and accessible techniques such as electroencephalography (EEG) are increasingly being used to address this challenge. In particular, the potential relevance between aperiodic components of EEG activity and these disorders has gained interest as our understanding evolves. This study aims to determine the differences in aperiodic activity between AD and FTD and evaluate its potential for distinguishing between the two disorders. A total of 88 participants, including 36 patients with AD, 23 patients with FTD, and 29 healthy controls (CN) underwent cognitive assessment and scalp EEG acquisition. Neuronal power spectra were parameterized to decompose the EEG spectrum, enabling comparison of group differences in different components. A support vector machine was employed to assess the impact of aperiodic parameters on the differential diagnosis. Compared with the CN group, both the AD and FTD groups showed varying degrees of increased alpha power (both periodic and raw power) and theta alpha power ratio. At the channel level, theta power (both periodic and raw power) in the frontal regions was higher in the AD group compared to the FTD group, and aperiodic parameters (both exponents and offsets) in the frontal, temporal, central, and parietal regions were higher in the AD group than in the FTD group. Importantly, the inclusion of aperiodic parameters led to improved performance in distinguishing between the two disorders. These findings highlight the significance of aperiodic components in discriminating dementia-related diseases.

区分阿尔茨海默病 (AD) 和额颞叶痴呆 (FTD) 是一项临床挑战。脑电图 (EEG) 等廉价且易于使用的技术越来越多地被用来应对这一挑战。特别是，随着我们理解的发展，脑电图活动的非周期性成分与这些疾病之间的潜在相关性引起了人们的兴趣。本研究旨在确定 AD 和 FTD 之间非周期性活动的差异，并评估其区分这两种疾病的潜力。共有 88 名参与者，包括 36 名 AD 患者、23 名 FTD 患者和 29 名健康对照者 (CN) 接受了认知评估和头皮脑电图采集。神经元功率谱被参数化以分解脑电图谱，从而能够比较不同成分的组间差异。采用支持向量机来评估非周期性参数对鉴别诊断的影响。与 CN 组相比，AD 组和 FTD 组均表现出不同程度的 α 功率（周期功率和原始功率）和 θ α 功率比的增加。在通道层面，与 FTD 组相比，AD 组额叶区域的 theta 功率（周期性和原始功率）较高，并且额叶、颞叶、中央和顶叶区域的非周期性参数（指数和偏移） AD 组高于 FTD 组。重要的是，包含非周期性参数可以提高区分两种疾病的性能。这些发现强调了非周期性成分在区分痴呆相关疾病中的重要性。