Huang Yongzhi, Geng Xinyi, Li Luming, Stein John F, Aziz Tipu Z, Green Alexander L, Wang Shouyan
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; University of Chinese Academy of Sciences, Beijing 100049, China; Nuffield Department of Surgery, University of Oxford, Oxford OX3 9DU, UK; Department of Neurosurgery, John Radcliffe Hospital, Oxford OX3 9DU, UK.
J Neurosci Methods. 2016 May 1;264:25-32. doi: 10.1016/j.jneumeth.2016.02.018. Epub 2016 Feb 27.
Multiple oscillations emerging from the same neuronal substrate serve to construct a local oscillatory network. The network usually exhibits complex behaviors of rhythmic, balancing and coupling between the oscillations, and the quantification of these behaviors would provide valuable insight into organization of the local network related to brain states.
An integrated approach to quantify rhythmic, balancing and coupling neural behaviors based upon power spectral analysis, power ratio analysis and cross-frequency power coupling analysis was presented. Deep brain local field potentials (LFPs) were recorded from the thalamus of patients with neuropathic pain and dystonic tremor. t-Test was applied to assess the difference between the two patient groups.
The rhythmic behavior measured by power spectral analysis showed significant power spectrum difference in the high beta band between the two patient groups. The balancing behavior measured by power ratio analysis showed significant power ratio differences at high beta band to 8-20 Hz, and 30-40 Hz to high beta band between the patient groups. The coupling behavior measured by cross-frequency power coupling analysis showed power coupling differences at (theta band, high beta band) and (45-55 Hz, 70-80 Hz) between the patient groups.
The study provides a strategy for studying the brain states in a multi-dimensional behavior space and a framework to screen quantitative characteristics for biomarkers related to diseases or nuclei.
The work provides a comprehensive approach for understanding the complex behaviors of deep brain LFPs and identifying quantitative biomarkers for brain states related to diseases or nuclei.
源自同一神经元基质的多种振荡有助于构建局部振荡网络。该网络通常表现出振荡之间有节奏、平衡和耦合的复杂行为，对这些行为进行量化将为深入了解与脑状态相关的局部网络组织提供有价值的见解。
提出了一种基于功率谱分析、功率比分析和交叉频率功率耦合分析来量化有节奏、平衡和耦合神经行为的综合方法。从神经性疼痛和肌张力障碍性震颤患者的丘脑记录深部脑局部场电位（LFPs）。应用t检验评估两组患者之间的差异。
通过功率谱分析测量的有节奏行为显示，两组患者在高β波段的功率谱存在显著差异。通过功率比分析测量的平衡行为显示，两组患者在高β波段与8 - 20 Hz以及30 - 40 Hz与高β波段之间的功率比存在显著差异。通过交叉频率功率耦合分析测量的耦合行为显示，两组患者在（θ波段，高β波段）和（45 - 55 Hz，70 - 80 Hz）之间存在功率耦合差异。
该研究提供了一种在多维行为空间中研究脑状态的策略以及一个筛选与疾病或核团相关生物标志物的定量特征的框架。
这项工作提供了一种全面的方法来理解深部脑LFPs的复杂行为，并识别与疾病或核团相关的脑状态的定量生物标志物。
