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Markers of Central Neuropathic Pain in Higuchi Fractal Analysis of EEG Signals From People With Spinal Cord Injury.
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2021-08-26 , DOI: 10.3389/fnins.2021.705652 Keri Anderson 1 , Cristian Chirion 2 , Matthew Fraser 3 , Mariel Purcell 3 , Sebastian Stein 2 , Aleksandra Vuckovic 1
Frontiers in Neuroscience ( IF 4.3 ) Pub Date : 2021-08-26 , DOI: 10.3389/fnins.2021.705652 Keri Anderson 1 , Cristian Chirion 2 , Matthew Fraser 3 , Mariel Purcell 3 , Sebastian Stein 2 , Aleksandra Vuckovic 1
Affiliation
Central neuropathic pain (CNP) negatively impacts the quality of life in a large proportion of people with spinal cord injury (SCI). With no cure at present, it is crucial to improve our understanding of how CNP manifests, to develop diagnostic biomarkers for drug development, and to explore prognostic biomarkers for personalised therapy. Previous work has found early evidence of diagnostic and prognostic markers analysing Electroencephalogram (EEG) oscillatory features. In this paper, we explore whether non-linear non-oscillatory EEG features, specifically Higuchi Fractal Dimension (HFD), can be used as prognostic biomarkers to increase the repertoire of available analyses on the EEG of people with subacute SCI, where having both linear and non-linear features for classifying pain may ultimately lead to higher classification accuracy and an intrinsically transferable classifier. We focus on EEG recorded during imagined movement because of the known relation between the motor cortex over-activity and CNP. Analyses were performed on two existing datasets. The first dataset consists of EEG recordings from able-bodied participants (N = 10), participants with chronic SCI and chronic CNP (N = 10), and participants with chronic SCI and no CNP (N = 10). We tested for statistically significant differences in HFD across all pairs of groups using bootstrapping, and found significant differences between all pairs of groups at multiple electrode locations. The second dataset consists of EEG recordings from participants with subacute SCI and no CNP (N = 20). They were followed-up 6 months post recording to test for CNP, at which point (N = 10) participants had developed CNP and (N = 10) participants had not developed CNP. We tested for statistically significant differences in HFD between these two groups using bootstrapping and, encouragingly, also found significant differences at multiple electrode locations. Transferable machine learning classifiers achieved over 80% accuracy discriminating between groups of participants with chronic SCI based on only a single EEG channel as input. The most significant finding is that future and chronic CNP share common features and as a result, the same classifier can be used for both. This sheds new light on pain chronification by showing that frontal areas, involved in the affective aspects of pain and believed to be influenced by long-standing pain, are affected in a much earlier phase of pain development.
中文翻译:
脊髓损伤患者脑电信号 Higuchi 分形分析中中枢神经性疼痛的标志物。
中枢神经性疼痛 (CNP) 对大部分脊髓损伤 (SCI) 患者的生活质量产生负面影响。目前尚无治愈方法,因此提高我们对 CNP 表现方式的理解、开发用于药物开发的诊断生物标志物以及探索用于个性化治疗的预后生物标志物至关重要。以前的工作已经发现了分析脑电图 (EEG) 振荡特征的诊断和预后标志物的早期证据。在本文中,我们探讨了非线性非振荡脑电图特征,特别是 Higuchi 分形维数 (HFD),是否可以用作预后生物标志物,以增加对亚急性 SCI 患者脑电图的可用分析库,其中具有用于分类疼痛的线性和非线性特征可能最终导致更高的分类准确性和本质上可转移的分类器。由于运动皮层过度活动与 CNP 之间的已知关系,我们专注于在想象运动期间记录的 EEG。对两个现有数据集进行了分析。第一个数据集包括来自健全参与者 (N = 10)、患有慢性 SCI 和慢性 CNP 的参与者 (N = 10) 以及患有慢性 SCI 和没有 CNP 的参与者 (N = 10) 的脑电图记录。我们使用自举法测试了所有组对 HFD 的统计学显着差异,并发现在多个电极位置的所有组对之间存在显着差异。第二个数据集由亚急性 SCI 且无 CNP(N = 20)的参与者的脑电图记录组成。记录后 6 个月对他们进行了随访以测试 CNP,此时 (N = 10) 参与者已发展为 CNP,而 (N = 10) 参与者尚未发展为 CNP。我们使用自举法测试了这两组之间 HFD 的统计学显着差异,令人鼓舞的是,我们还发现多个电极位置存在显着差异。可转移的机器学习分类器仅基于单个 EEG 通道作为输入,在区分患有慢性 SCI 的参与者组之间实现了超过 80% 的准确度。最重要的发现是未来和慢性 CNP 具有共同的特征,因此,相同的分类器可以用于两者。通过显示涉及疼痛的情感方面并被认为受到长期疼痛影响的额叶区域,这为疼痛慢性化提供了新的思路,
更新日期:2021-08-26
中文翻译:
脊髓损伤患者脑电信号 Higuchi 分形分析中中枢神经性疼痛的标志物。
中枢神经性疼痛 (CNP) 对大部分脊髓损伤 (SCI) 患者的生活质量产生负面影响。目前尚无治愈方法,因此提高我们对 CNP 表现方式的理解、开发用于药物开发的诊断生物标志物以及探索用于个性化治疗的预后生物标志物至关重要。以前的工作已经发现了分析脑电图 (EEG) 振荡特征的诊断和预后标志物的早期证据。在本文中,我们探讨了非线性非振荡脑电图特征,特别是 Higuchi 分形维数 (HFD),是否可以用作预后生物标志物,以增加对亚急性 SCI 患者脑电图的可用分析库,其中具有用于分类疼痛的线性和非线性特征可能最终导致更高的分类准确性和本质上可转移的分类器。由于运动皮层过度活动与 CNP 之间的已知关系,我们专注于在想象运动期间记录的 EEG。对两个现有数据集进行了分析。第一个数据集包括来自健全参与者 (N = 10)、患有慢性 SCI 和慢性 CNP 的参与者 (N = 10) 以及患有慢性 SCI 和没有 CNP 的参与者 (N = 10) 的脑电图记录。我们使用自举法测试了所有组对 HFD 的统计学显着差异,并发现在多个电极位置的所有组对之间存在显着差异。第二个数据集由亚急性 SCI 且无 CNP(N = 20)的参与者的脑电图记录组成。记录后 6 个月对他们进行了随访以测试 CNP,此时 (N = 10) 参与者已发展为 CNP,而 (N = 10) 参与者尚未发展为 CNP。我们使用自举法测试了这两组之间 HFD 的统计学显着差异,令人鼓舞的是,我们还发现多个电极位置存在显着差异。可转移的机器学习分类器仅基于单个 EEG 通道作为输入,在区分患有慢性 SCI 的参与者组之间实现了超过 80% 的准确度。最重要的发现是未来和慢性 CNP 具有共同的特征,因此,相同的分类器可以用于两者。通过显示涉及疼痛的情感方面并被认为受到长期疼痛影响的额叶区域,这为疼痛慢性化提供了新的思路,