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归一化功率方差:脑电图分析中与功率正交的一个新领域。

Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

作者信息

Aoki Yasunori, Kazui Hiroaki, Pascual-Marqui Roberto D, Bruña Ricardo, Yoshiyama Kenji, Wada Tamiki, Kanemoto Hideki, Suzuki Yukiko, Suehiro Takashi, Satake Yuto, Yamakawa Maki, Hata Masahiro, Canuet Leonides, Ishii Ryouhei, Iwase Masao, Ikeda Manabu

机构信息

Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan.

Department of Psychiatry, Nippon Life Hospital, Osaka, Japan.

出版信息

Clin EEG Neurosci. 2023 Nov;54(6):611-619. doi: 10.1177/15500594221088736. Epub 2022 Mar 29.

Abstract

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

摘要

迄今为止,脑电图(EEG)已通过检查脑电波以及识别异常放电和基本波减慢,用于癫痫、痴呆和意识障碍的诊断。此外,脑电图功率分析与诸如精确低分辨率脑电磁断层扫描(eLORETA)之类的源估计方法相结合,该方法可根据脑电图数据计算皮质电活动的功率,已被广泛用于研究神经精神疾病中的皮质电活动。然而,最近发展起来的数学领域“信息几何”表明,脑电图还有一个与功率维度正交的维度——归一化功率方差(NPV)维度。此外,通过引入信息几何的概念,获得了一种收敛速度明显更快的NPV估计器。到目前为止,对这个NPV坐标的研究还很有限。在本研究中,我们将这种eLORETA的NPV分析应用于特发性正常压力脑积水(iNPH)患者的脑脊液(CSF)分流手术前,在该手术中传统功率分析未能检测到与CSF分流手术结果相关的任何差异。我们对eLORETA的NPV分析在17名分流反应者和19名无反应者之间的β频段高凸区检测到显著更高的NPV值。考虑到我们目前和过去关于NPV的研究结果,我们还讨论了这种NPV应用的优势,它代表了皮质损伤的敏感早期预警信号。总体而言,我们的研究结果表明,脑电图还有另一个维度——NPV维度,它包含了许多关于皮质电活动的信息,这些信息在临床实践中可能是有用的。

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