Anderson Keri, Chirion Cristian, Fraser Matthew, Purcell Mariel, Stein Sebastian, Vuckovic Aleksandra
Biomedical Engineering Division, University of Glasgow, Glasgow, United Kingdom.
School of Computing Science, University of Glasgow, Glasgow, United Kingdom.
Front Neurosci. 2021 Aug 26;15:705652. doi: 10.3389/fnins.2021.705652. eCollection 2021.
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 ( = 10), participants with chronic SCI and chronic CNP ( = 10), and participants with chronic SCI and no CNP ( = 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 ( = 20). They were followed-up 6 months post recording to test for CNP, at which point ( = 10) participants had developed CNP and ( = 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.
中枢神经性疼痛（CNP）对很大一部分脊髓损伤（SCI）患者的生活质量产生负面影响。由于目前尚无治愈方法，加深我们对CNP表现方式的理解、开发用于药物研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。先前的研究已发现分析脑电图（EEG）振荡特征的诊断和预后标志物的早期证据。在本文中，我们探讨非线性非振荡EEG特征，特别是 Higuchi 分形维数（HFD），是否可用作预后生物标志物，以增加对亚急性SCI患者EEG的可用分析方法，使用线性和非线性特征对疼痛进行分类最终可能会提高分类准确性并得到一个本质上可转移的分类器。由于运动皮层过度活跃与CNP之间的已知关系，我们专注于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但无CNP的参与者（n = 10）的EEG记录。我们使用自举法测试了所有组对之间HFD的统计学显著差异，发现在多个电极位置所有组对之间均存在显著差异。第二个数据集包括来自患有亚急性SCI但无CNP的参与者（n = 20）的EEG记录。在记录后6个月对他们进行随访以测试是否患有CNP，此时（n = 10）名参与者已患上CNP，（n = 10）名参与者未患上CNP。我们使用自举法测试了这两组之间HFD的统计学显著差异，令人鼓舞的是，在多个电极位置也发现了显著差异。可转移机器学习分类器仅以单个EEG通道作为输入，在区分慢性SCI参与者组时准确率超过80%。最显著的发现是，未来发生的和慢性的CNP具有共同特征，因此，相同的分类器可用于两者。这通过表明参与疼痛情感方面且被认为受长期疼痛影响的额叶区域在疼痛发展的更早阶段就受到影响而揭示了疼痛慢性化的新情况。
