Mild, blast-induced traumatic brain injury (mbTBI) is a common combat brain injury characterized by typically normal neuroimaging findings, with unpredictable future cognitive recovery. Traditional methods of electroencephalography (EEG) analysis (e.g., spectral analysis) have not been successful in detecting the degree of cognitive and functional impairment in mbTBI. We therefore collected resting state EEG (5 minutes, 64 leads) from twelve patients with a history of mbTBI, along with repeat neuropsychological testing (D-KEFS Tower test) to compare two new methods for analyzing EEG (multifractal detrended fluctuation analysis (MF-DFA) and information transfer modeling (ITM)) with spectral analysis. For MF-DFA, we extracted relevant parameters from the resultant multifractal spectrum from all leads and compared with traditional power by frequency band for spectral analysis. For ITM, because the number of parameters from each lead far exceeded the number of subjects, we utilized a reduced set of 10 leads which were compared with spectral analysis. We utilized separate 30 second EEG segments for training and testing statistical models based upon regression tree analysis. ITM and MF-DFA models both generally had improved accuracy at correlating with relevant measures of cognitive performance as compared to spectral analytic models ITM and MF-DFA both merit additional research as analytic tools for EEG and cognition in TBI.

中文翻译：

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轻度爆炸诱发 TBI 中脑电图的信息传输和多重分形分析


轻度爆炸引起的创伤性脑损伤 (mbTBI) 是一种常见的战斗脑损伤，其特征是典型的正常神经影像学结果，但未来的认知恢复情况不可预测。传统的脑电图 (EEG) 分析方法（例如频谱分析）未能成功检测 mbTBI 的认知和功能障碍程度。因此，我们收集了 12 名有 mbTBI 病史的患者的静息态脑电图（5 分钟，64 导联），并进行重复神经心理学测试（D-KEFS Tower 测试），以比较两种分析脑电图的新方法（多重分形去趋势波动分析（MF- DFA）和信息传输建模（ITM））以及频谱分析。对于MF-DFA，我们从所有导联的多重分形频谱中提取相关参数，并按频段与传统功率进行比较进行频谱分析。对于 ITM，由于每个导联的参数数量远远超过了受试者的数量，因此我们使用了 10 个导联的减少组来与频谱分析进行比较。我们利用单独的 30 秒 EEG 片段来训练和测试基于回归树分析的统计模型。与频谱分析模型相比，ITM 和 MF-DFA 模型在与认知表现相关测量相关方面通常具有更高的准确性，ITM 和 MF-DFA 都值得进一步研究，作为 TBI 脑电图和认知的分析工具。