Scheijbeler Elliz P, van Nifterick Anne M, Stam Cornelis J, Hillebrand Arjan, Gouw Alida A, de Haan Willem
Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
Netw Neurosci. 2022 Jun 1;6(2):382-400. doi: 10.1162/netn_a_00224. eCollection 2022 Jun.
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPE), corrected for volume conduction. The JPE showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPE features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPE was at least as good as the relative theta power benchmark. The JPE is therefore a potential biomarker for early-stage AD that should be explored in larger studies.
越来越多的证据表明，信号变异性和复杂性的测量可能为阿尔茨海默病（AD）提供有前景的生物标志物。然而，早期研究仅限于局部活动的特征描述。在此，我们研究排列熵的网络版本是否可作为早期AD的一种新型生物标志物。对18名主观认知下降（SCD）受试者和18名轻度认知障碍（MCI）受试者进行静息态源空间脑磁图记录。用排列熵（PE）表征局部活动。使用经体积传导校正的倒置联合排列熵（JPE）研究网络水平的相互作用。JPE显示MCI受试者在θ和α频段的非线性连接性降低。局部PE显示θ频段熵增加。组间差异广泛分布于脑区。对MCI与SCD受试者分类的受试者工作特征（ROC）分析表明，基于JPE特征训练的逻辑回归模型（78.4%[62.5 - 93.3%]）略优于PE（76.9%[60.3 - 93.4%]）和基于相对θ功率的模型（76.9%[60.4 - 93.3%]）。θ JPE的分类性能至少与相对θ功率基准一样好。因此，JPE是早期AD的一种潜在生物标志物，应在更大规模的研究中进行探索。
