Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease.
Short timescale abnormalities in the states of spontaneous synchrony in the functional neural networks in Alzheimer's disease.
复制标题
阿尔茨海默氏病功能神经网络自发同步状态的短时间尺度异常。
DOI：
10.1016/j.nicl.2018.05.028
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Salat,DavidH
中科院分区：
文献类型：
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作者：
Sitnikova,TatianaA;Hughes,JeremyW;Ahlfors,SeppoP;Woolrich,MarkW;Salat,DavidH
阿尔茨海默病（AD）是一种常见的神经退行性疾病，可导致严重的认知和功能退化。功能性磁共振成像（fMRI）揭示了AD在所谓的默认模式网络（DMN）中空间分离区域之间的内在同步异常。为了了解这种大规模同步性中断与AD认知障碍之间的关系，关键是要确定fMRI记录的低频血流动力学波动的缺陷是否以及如何转化为记忆和其他认知过程的更快时间尺度。本研究采用脑磁图（MEG）和隐马尔可夫模型（HMM）的方法来估计高时间分辨率的功能神经网络的自发同步变化。在一组认知健康（CH）的老年人中，我们发现短暂的（平均持续时间为150-250 ms）网络活动状态，这是在一个快速的连续访问，其特征是空间协调的变化的幅度源本地化的电生理振荡。推断的状态与先前使用MEG在年轻参与者中观察到的状态相似，并且状态特异性活动的估计皮质源分布类似于经典的功能性神经网络，例如DMN。在AD患者中，推断的网络状态与CH组在短尺度定时和振荡特征上不同。在AD患者中，与DMN重叠的区域中的振荡幅度增加的状态不太常见，并且持续时间较短，这表明该网络中的自发同步在患者中不太可能且不太稳定。在访问该状态期间，在一些DMN节点中，AD组的高频（8-30 Hz）振荡的幅度变化不如CH组稳健。这些发现突出了研究功能神经网络中自发活动的短尺度时间演变与理解AD病理生理学的相关性。DMN中灵活的内在同步能力可能对记忆和其他高级认知功能至关重要。我们的分析产生了量化AD中神经同步障碍的不同特征的指标，并可能为未来的研究提供神经网络健康的敏感指标。
Alzheimer's disease (AD) is a prevalent neurodegenerative condition that can lead to severe cognitive and functional deterioration. Functional magnetic resonance imaging (fMRI) revealed abnormalities in AD in intrinsic synchronization between spatially separate regions in the so-called default mode network (DMN) of the brain. To understand the relationship between this disruption in large-scale synchrony and the cognitive impairment in AD, it is critical to determine whether and how the deficit in the low frequency hemodynamic fluctuations recorded by fMRI translates to much faster timescales of memory and other cognitive processes. The present study employed magnetoencephalography (MEG) and a Hidden Markov Model (HMM) approach to estimate spontaneous synchrony variations in the functional neural networks with high temporal resolution. In a group of cognitively healthy (CH) older adults, we found transient (mean duration of 150–250 ms) network activity states, which were visited in a rapid succession, and were characterized by spatially coordinated changes in the amplitude of source-localized electrophysiological oscillations. The inferred states were similar to those previously observed in younger participants using MEG, and the estimated cortical source distributions of the state-specific activity resembled the classic functional neural networks, such as the DMN. In patients with AD, inferred network states were different from those of the CH group in short-scale timing and oscillatory features. The state of increased oscillatory amplitudes in the regions overlapping the DMN was visited less often in AD and for shorter periods of time, suggesting that spontaneous synchronization in this network was less likely and less stable in the patients. During the visits to this state, in some DMN nodes, the amplitude change in the higher-frequency (8–30 Hz) oscillations was less robust in the AD than CH group. These findings highlight relevance of studying short-scale temporal evolution of spontaneous activity in functional neural networks to understanding the AD pathophysiology. Capacity of flexible intrinsic synchronization in the DMN may be crucial for memory and other higher cognitive functions. Our analysis yielded metrics that quantify distinct features of the neural synchrony disorder in AD and may offer sensitive indicators of the neural network health for future investigations.