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The effect of aperiodic components in distinguishing Alzheimer’s disease from frontotemporal dementia

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Abstract

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.

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Data availability

The datasets analyzed during the current study are available in the ds004504 repository, https://github.com/OpenNeuroDatasets/ds004504. The custom codes used for the current study are available at https://github.com/dcgggg/ds004504_Aperdiodic.

Abbreviations

AD :

Alzheimer’s disease

ANOVA :

Analysis of variance

AUC :

Area under curve

CN :

Healthy controls

EEG :

Electroencephalography

E/I :

Excitation/inhibition

FDR :

False discovery rate

FTD :

Frontotemporal dementia

MMSE :

Mini-Mental State Examination

NFTs :

Neurofibrillary tangles

PSD :

Power spectral density

SMOTE :

Synthetic Minority Oversampling Technique

SVM :

Support vector machine

TAR :

Theta alpha power ratio

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Acknowledgements

We acknowledge the support of the 2nd Department of Neurology of AHEPA General University Hospital of Thessaloniki for graciously sharing the datasets. All the participants who volunteered for this study deserve our gratitude. We also thank ChatGPT (version 3.5, Open AI, San Francisco, CA, USA) for its assistance in the visualization of the results.

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Authors and Affiliations

  1. Neurosurgery Center, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China On Diagnosis and Treatment of Cerebrovascular Disease, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, The Neurosurgery Institute of Guangdong Province, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510280, People’s Republic of China

    Zhuyong Wang, Anyang Liu, Jianshen Yu, Pengfei Wang, Yuewei Bi, Sha Xue, Hongbo Guo & Wangming Zhang

  2. Guangdong Province Key Laboratory of Computational Science, School of Mathematics, Sun Yat-Sen University, No. 135, Xingang Xi Road, Guangzhou, People’s Republic of China

    Jiajun Zhang

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Contributions

Z.W. and W.Z. provided the concept and design; Z.W., A.L., J.Y., P.W., and Y.B. analyzed and explained the data; J.Z. and S.X. guided the data processing in this study; Z.W. and W.Z. wrote the main manuscript; W.Z., B.G., and J.Z. supervised the study. All authors read and approved the final manuscript.

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Correspondence to Jiajun Zhang, Hongbo Guo or Wangming Zhang.

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Ethical approval was already approved for data acquisition in the previous work [32, 33].

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Wang, Z., Liu, A., Yu, J. et al. The effect of aperiodic components in distinguishing Alzheimer’s disease from frontotemporal dementia. GeroScience 46, 751–768 (2024). https://doi.org/10.1007/s11357-023-01041-8

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