Dementia rhythms: Unveiling the EEG dynamics for MCI detection through spectral and synchrony neuromarkers

IF 2.7 4区 医学 Q2 BIOCHEMICAL RESEARCH METHODS
Mesut Şeker, Mehmet Siraç Özerdem
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引用次数: 0

Abstract

Background

Neurological disorders arise primarily from the dysfunction of brain cells, leading to various impairments. Electroencephalography (EEG) stands out as the most popular method in the discovery of neuromarkers indicating neurological disorders. The proposed study investigates the effectiveness of spectral and synchrony neuromarkers derived from resting state EEG in the detection of Mild Cognitive Impairment (MCI) with controls.

New methods

The dataset is composed of 10 MCI and 10 HC groups. Spectral features and synchrony measures are utilized to detect slowing patterns in MCI. Efficient neuro-markers are classified by 25 classification algorithm. Independent samples t-test and Pearson’s Correlation Coefficients are applied to reveal group differences for spectral markers, and repeated measures ANOVA is tested for wPLI-based markers.

Results

Lower peak amplitudes are prominent in MCI participants for high frequencies indicating slower physiological behavior of the demented EEG. The MCI and HC groups are correctly classified with 95 % acc. using peak amplitudes of beta band with LGBM classifier. Higher wPLI values are calculated for HC participants in high frequencies. The alpha wPLI values achieve a classification accuracy of 99 % using the LGBM algorithm for MCI detection.

Comparison with existing methods

The neuro-markers including peak amplitudes, frequencies, and wPLIs with advanced machine learning techniques showcases the innovative nature of this research.

Conclusion

The findings suggest that peak amplitudes and wPLI in high frequency bands derived from resting state EEG are effective neuromarkers for detection of MCI. Spectral and synchrony neuro-markers hold great promise for accurate MCI detection.

痴呆症节律:通过频谱和同步神经标记物揭示脑电图动态,以检测 MCI。
背景:神经系统疾病主要源于脑细胞功能障碍,从而导致各种损伤。脑电图(EEG)是发现神经系统疾病神经标志物的最常用方法。拟议的研究调查了从静息状态脑电图中提取的频谱和同步神经标记物在检测轻度认知障碍(MCI)和对照组中的有效性:新方法:数据集由 10 个 MCI 组和 10 个 HC 组组成。新方法:数据集由 10 个 MCI 组和 10 个 HC 组组成,利用频谱特征和同步测量来检测 MCI 的减慢模式。采用 25 分类算法对高效神经标记进行分类。独立样本 t 检验和皮尔逊相关系数用于揭示频谱标记的组间差异,重复测量方差分析用于检验基于 wPLI 的标记:MCI 参与者的高频峰值振幅较低,表明痴呆脑电图的生理行为较慢。使用 LGBM 分类器对 MCI 组和 HC 组进行分类,正确率为 95%。在高频率下,HC 参与者的 wPLI 值较高。使用 LGBM 算法检测 MCI 时,α wPLI 值的分类准确率达到 99%:包括峰值振幅、频率和 wPLIs 在内的神经标记物与先进的机器学习技术的结合展示了本研究的创新性:研究结果表明,静息状态脑电图中高频带的峰值振幅和 wPLI 是检测 MCI 的有效神经标记。频谱和同步神经标志物在准确检测 MCI 方面大有可为。
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来源期刊
Journal of Neuroscience Methods
Journal of Neuroscience Methods 医学-神经科学
CiteScore
7.10
自引率
3.30%
发文量
226
审稿时长
52 days
期刊介绍: The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.
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