Abstract：
BACKGROUND: Dementia is caused by neurodegenerative conditions and characterized by cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer\'s Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson\'s Disease with dementia (PDD), remains challenging.
METHODS: Here, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions.
RESULTS: Classic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4-8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4-15 Hz frequency range, encompassing the actual dominant frequency of all individuals. FFT results for this range yielded wider significant discriminators vs AR, also detecting differences between AD and DLB. Analyses of the inclusive dominant / peak frequency range (4-15 Hz) indicated slowing and reduced variability, also discriminating between synucleinopathies vs controls and AD. Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-specific slowing of brain oscillatory peaks during EC and EO for all groups. Distinct and robust differences were particularly strong for aperiodic parameters, suggesting their potential diagnostic power in detecting specific changes resulting from age and cognitive status.
CONCLUSIONS: Our findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future.
METHODS: Here, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions.
RESULTS: Classic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4-8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4-15 Hz frequency range, encompassing the actual dominant frequency of all individuals. FFT results for this range yielded wider significant discriminators vs AR, also detecting differences between AD and DLB. Analyses of the inclusive dominant / peak frequency range (4-15 Hz) indicated slowing and reduced variability, also discriminating between synucleinopathies vs controls and AD. Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-specific slowing of brain oscillatory peaks during EC and EO for all groups. Distinct and robust differences were particularly strong for aperiodic parameters, suggesting their potential diagnostic power in detecting specific changes resulting from age and cognitive status.
CONCLUSIONS: Our findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future.
摘要：
背景：痴呆是由神经退行性疾病引起的，其特征是认知功能下降。痴呆亚型的诊断准确性,如阿尔茨海默病（AD），路易体痴呆（DLB）和帕金森病痴呆（PDD），仍然具有挑战性。
方法：这里，在闭眼(EC)和睁眼(EO)条件下,采用不同的定量脑电图(qEEG)分析方法来评估其区分痴呆亚型与健康对照的有效性.
结果：经典的快速傅立叶变换（FFT）和自回归（AR）功率分析在4-8Hzθ范围内的所有条件之间进行区分。只有路易体痴呆（DLB）的个体在更宽的4-15Hz频率范围内与健康受试者不同，包含所有个体的实际主导频率。此范围的FFT结果产生了更广泛的显著鉴别器与AR，还检测AD和DLB之间的差异。对包含的主/峰值频率范围（4-15Hz）的分析表明，变异性减慢且降低，还区分突触核蛋白病与对照和AD。使用Fitting-Oscillation-＆-One-Over-F（FOOF）模型分解AR光谱的周期性振荡和非周期性成分，可在EC和EO期间可靠且特定于亚型的大脑振荡峰减慢所有组。对于非周期性参数，明显和稳健的差异特别强，表明他们在检测年龄和认知状态引起的特定变化方面的潜在诊断能力。
结论：我们的发现表明qEEG方法可以可靠地检测痴呆亚型。光谱分析包括综合，在EC和EO条件下区分周期性和非周期性分量的多参数EEG方法可能会提高将来的诊断准确性。
方法：这里，在闭眼(EC)和睁眼(EO)条件下,采用不同的定量脑电图(qEEG)分析方法来评估其区分痴呆亚型与健康对照的有效性.
结果：经典的快速傅立叶变换（FFT）和自回归（AR）功率分析在4-8Hzθ范围内的所有条件之间进行区分。只有路易体痴呆（DLB）的个体在更宽的4-15Hz频率范围内与健康受试者不同，包含所有个体的实际主导频率。此范围的FFT结果产生了更广泛的显著鉴别器与AR，还检测AD和DLB之间的差异。对包含的主/峰值频率范围（4-15Hz）的分析表明，变异性减慢且降低，还区分突触核蛋白病与对照和AD。使用Fitting-Oscillation-＆-One-Over-F（FOOF）模型分解AR光谱的周期性振荡和非周期性成分，可在EC和EO期间可靠且特定于亚型的大脑振荡峰减慢所有组。对于非周期性参数，明显和稳健的差异特别强，表明他们在检测年龄和认知状态引起的特定变化方面的潜在诊断能力。
结论：我们的发现表明qEEG方法可以可靠地检测痴呆亚型。光谱分析包括综合，在EC和EO条件下区分周期性和非周期性分量的多参数EEG方法可能会提高将来的诊断准确性。
