A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity
A Permutation Disalignment Index-Based Complex Network Approach to Evaluate Longitudinal Changes in Brain-Electrical Connectivity
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DOI：
10.3390/e19100548
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发表时间：
2017-10
期刊：
影响因子：
2.7
通讯作者：
N. Mammone;S. D. Salvo;C. Ieracitano;S. Marino;Angela Marra;F. Corallo;F. Morabito
中科院分区：
文献类型：
--
作者：
N. Mammone;S. D. Salvo;C. Ieracitano;S. Marino;Angela Marra;F. Corallo;F. Morabito
在神经系统疾病的研究中，脑电图（EEG）信号处理可以提供有价值的信息，因为神经元回路之间相互作用的异常可能反映在可以在头皮上检测到的宏观电位异常。轻度认知障碍（MCI）是由一种疾病退化为痴呆症引起的，它会影响大脑的连通性。基于最近发展的脑电信号耦合强度描述符排列失调指数（PDI）取得的可喜结果，本文引入了一种基于排列失调指数的复杂网络模型来评估脑电连接的纵向变化。研究人员招募了33名失忆性轻度认知障碍患者，并对他们进行了4个多月的随访。将结果与MoCA（蒙特利尔认知评估）测试进行比较，MoCA测试对患者的认知能力进行评分。MoCA变化与特征路径长度（λ）变化呈显著负相关（r = - 0）。56, p = 0。MoCA的变化与聚类系数的变化呈显著正相关（CC, r = 0）。58, p = 0。0004)，全局效率（GE, r = 0）。57, p = 0。0005)和小世界（SW, r = 0）。57, p = 0。0005)。因此，认知能力下降似乎反映了一种潜在的皮层“断开”现象：病情恶化的受试者确实表现出λ增加，CC、GE和SW下降。本研究提出的pdi连接模型可作为一种新的工具，用于客观量化MCI受试者的纵向脑电连接变化。
In the study of neurological disorders, Electroencephalographic (EEG) signal processing can provide valuable information because abnormalities in the interaction between neuron circuits may reflect on macroscopic abnormalities in the electrical potentials that can be detected on the scalp. A Mild Cognitive Impairment (MCI) condition, when caused by a disorder degenerating into dementia, affects the brain connectivity. Motivated by the promising results achieved through the recently developed descriptor of coupling strength between EEG signals, the Permutation Disalignment Index (PDI), the present paper introduces a novel PDI-based complex network model to evaluate the longitudinal variations in brain-electrical connectivity. A group of 33 amnestic MCI subjects was enrolled and followed-up with over four months. The results were compared to MoCA (Montreal Cognitive Assessment) tests, which scores the cognitive abilities of the patient. A significant negative correlation could be observed between MoCA variation and the characteristic path length ( λ ) variation ( r = - 0 . 56 , p = 0 . 0006 ), whereas a significant positive correlation could be observed between MoCA variation and the variation of clustering coefficient (CC, r = 0 . 58 , p = 0 . 0004 ), global efficiency (GE, r = 0 . 57 , p = 0 . 0005 ) and small worldness (SW, r = 0 . 57 , p = 0 . 0005 ). Cognitive decline thus seems to reflect an underlying cortical “disconnection” phenomenon: worsened subjects indeed showed an increased λ and decreased CC, GE and SW. The PDI-based connectivity model, proposed in the present work, could be a novel tool for the objective quantification of longitudinal brain-electrical connectivity changes in MCI subjects.