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加权网络测度揭示不同类型痴呆症之间的差异:一项 EEG 研究。

Weighted network measures reveal differences between dementia types: An EEG study.

机构信息

Institute of Neuroscience, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.

NIHR Newcastle Biomedical Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, UK.

出版信息

Hum Brain Mapp. 2020 Apr 15;41(6):1573-1590. doi: 10.1002/hbm.24896. Epub 2019 Dec 9.

Abstract

The diagnosis of dementia with Lewy bodies (DLB) versus Alzheimer's disease (AD) can be difficult especially early in the disease process. However, one inexpensive and non-invasive biomarker which could help is electroencephalography (EEG). Previous studies have shown that the brain network architecture assessed by EEG is altered in AD patients compared with age-matched healthy control people (HC). However, similar studies in Lewy body diseases, that is, DLB and Parkinson's disease dementia (PDD) are still lacking. In this work, we (a) compared brain network connectivity patterns across conditions, AD, DLB and PDD, in order to infer EEG network biomarkers that differentiate between these conditions, and (b) tested whether opting for weighted matrices led to more reliable results by better preserving the topology of the network. Our results indicate that dementia groups present with reduced connectivity in the EEG α band, whereas DLB shows weaker posterior-anterior patterns within the β-band and greater network segregation within the θ-band compared with AD. Weighted network measures were more consistent across global thresholding levels, and the network properties reflected reduction in connectivity strength in the dementia groups. In conclusion, β- and θ-band network measures may be suitable as biomarkers for discriminating DLB from AD, whereas the α-band network is similarly affected in DLB and PDD compared with HC. These variations may reflect the impairment of attentional networks in Parkinsonian diseases such as DLB and PDD.

摘要

路易体痴呆症 (DLB) 与阿尔茨海默病 (AD) 的诊断可能很困难,尤其是在疾病早期。然而,一种廉价且非侵入性的生物标志物可能会有所帮助,即脑电图 (EEG)。先前的研究表明,与年龄匹配的健康对照组相比,AD 患者的大脑网络结构通过 EEG 评估发生了改变。然而,在路易体疾病中,即 DLB 和帕金森病痴呆症 (PDD) 中,类似的研究仍然缺乏。在这项工作中,我们 (a) 比较了 AD、DLB 和 PDD 这几种条件下大脑网络连接模式,以推断出区分这些条件的 EEG 网络生物标志物;(b) 测试了选择加权矩阵是否通过更好地保留网络拓扑结构来产生更可靠的结果。我们的结果表明,痴呆症组的 EEG α 波段的连接性降低,而与 AD 相比,DLB 在 β 波段内表现出较弱的后前模式,而在 θ 波段内表现出更大的网络分离。加权网络测量在全局阈值水平上更一致,并且网络属性反映了痴呆症组的连接强度降低。总之,β-和 θ-波段的网络测量可能适合作为区分 DLB 和 AD 的生物标志物,而与 HC 相比,α-波段的网络在 DLB 和 PDD 中也受到了类似的影响。这些变化可能反映了帕金森病等 DLB 和 PDD 注意力网络的损伤。

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