Buchanan Derrick Matthew, Ros Tomas, Nahas Richard
Department of Neuroscience, Carleton University, Ottawa, ON K1S 5B6, Canada.
The Seekers Centre, Ottawa, ON K1Z 5Z9, Canada.
Brain Sci. 2021 Apr 24;11(5):537. doi: 10.3390/brainsci11050537.
(1) Background: Mild traumatic brain injury produces significant changes in neurotransmission including brain oscillations. We investigated potential quantitative electroencephalography biomarkers in 57 patients with post-concussive syndrome and chronic pain following motor vehicle collision, and 54 healthy nearly age- and sex-matched controls. (2) Methods: Electroencephalography processing was completed in MATLAB, statistical modeling in SPSS, and machine learning modeling in Rapid Miner. Group differences were calculated using current-source density estimation, yielding whole-brain topographical distributions of absolute power, relative power and phase-locking functional connectivity. Groups were compared using independent sample Mann-Whitney U tests. Effect sizes and Pearson correlations were also computed. Machine learning analysis leveraged a post hoc supervised learning support vector non-probabilistic binary linear kernel classification to generate predictive models from the derived EEG signatures. (3) Results: Patients displayed significantly elevated and slowed power compared to controls: delta ( = 0.000000, = 0.6) and theta power ( < 0.0001, = 0.4), and relative delta power ( < 0.00001) and decreased relative alpha power ( < 0.001). Absolute delta and theta power together yielded the strongest machine learning classification accuracy (87.6%). Changes in absolute power were moderately correlated with duration and persistence of symptoms in the slow wave frequency spectrum (<15 Hz). (4) Conclusions: Distributed increases in slow wave oscillatory power are concurrent with post-concussive syndrome and chronic pain.
(1) 背景：轻度创伤性脑损伤会导致神经传递发生显著变化，包括脑振荡。我们对57例机动车碰撞后患有脑震荡后综合征和慢性疼痛的患者以及54名年龄和性别相近的健康对照者进行了潜在定量脑电图生物标志物的研究。(2) 方法：在MATLAB中完成脑电图处理，在SPSS中进行统计建模，在Rapid Miner中进行机器学习建模。使用电流源密度估计计算组间差异，得出绝对功率、相对功率和锁相功能连接的全脑地形图分布。使用独立样本曼-惠特尼U检验比较各组。还计算了效应大小和皮尔逊相关性。机器学习分析利用事后监督学习支持向量非概率二元线性核分类，从导出的脑电图特征生成预测模型。(3) 结果：与对照组相比，患者的功率显著升高且减慢：δ波（ = 0.000000， = 0.6）和θ波功率（ < 0.0001， = 0.4），以及相对δ波功率（ < 0.00001）和相对α波功率降低（ < 0.001）。绝对δ波和θ波功率共同产生了最强的机器学习分类准确率（87.6%）。绝对功率的变化与慢波频谱（<15 Hz）中症状的持续时间和持久性中度相关。(4) 结论：慢波振荡功率的分布式增加与脑震荡后综合征和慢性疼痛同时出现。
