意识障碍中清醒静息脑电图的不同频谱轮廓:振荡频率和拓扑的作用

IF 2.3 3区 医学 Q3 CLINICAL NEUROLOGY
Dominika Drążyk, Karol Przewrocki, Urszula Górska-Klimowska, Marek Binder
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引用次数: 0

摘要

长时间意识障碍(PDOC)给准确的临床诊断带来了挑战,这主要是由于患者的行为反应很少或不明确。对大脑活动的测量可以帮助更好地进行诊断,而不受运动限制的影响。基于静息态脑电图频谱分析的方法似乎是一条很有前景的途径,它能揭示 PDOC 患者大脑内部动态的特定变化。在这项研究中,我们使用了一种稳健的静息态脑电图功率谱参数提取方法来识别不同类型 PDOC 的不同频谱特性。60 名患者和 37 名健康志愿者参与了这项研究。患者组包括 22 名无反应清醒患者、25 名微弱意识患者和 13 名摆脱微弱意识状态的患者。研究人员在清醒状态下采集了十分钟的静息脑电图,并将其转换成单个功率谱。使用频谱分解算法,我们提取了每位患者顶中央区 1-14 Hz 范围内的最大峰值频率,以及最大频率峰值的前后(AP)梯度。所有患者均使用昏迷恢复量表修订版(CRS-R)进行行为诊断。通过 CRS-R 总分和 PDOC 诊断,1-14 Hz 范围内的最大峰值频率成功预测了患者的神经行为能力。此外,在 1-14 Hz 范围内仅观察到一个峰值的患者中,AP 梯度对预测的准确性也有显著贡献。我们发现患者有三种不同的频谱特征,可能代表丘脑皮层功能的不同神经生理学模式。病因对所得结果没有重大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Distinct Spectral Profiles of Awake Resting EEG in Disorders of Consciousness: The Role of Frequency and Topography of Oscillations

Distinct Spectral Profiles of Awake Resting EEG in Disorders of Consciousness: The Role of Frequency and Topography of Oscillations

The prolonged disorders of consciousness (PDOC) pose a challenge for an accurate clinical diagnosis, mainly due to patients’ scarce or ambiguous behavioral responsiveness. Measurement of brain activity can support better diagnosis, independent of motor restrictions. Methods based on spectral analysis of resting-state EEG appear as a promising path, revealing specific changes within the internal brain dynamics in PDOC patients. In this study we used a robust method of resting-state EEG power spectrum parameter extraction to identify distinct spectral properties for different types of PDOC. Sixty patients and 37 healthy volunteers participated in this study. Patient group consisted of 22 unresponsive wakefulness patients, 25 minimally conscious patients and 13 patients emerging from the minimally conscious state. Ten minutes of resting EEG was acquired during wakefulness and transformed into individual power spectra. For each patient, using the spectral decomposition algorithm, we extracted maximum peak frequency within 1–14 Hz range in the centro-parietal region, and the antero-posterior (AP) gradient of the maximal frequency peak. All patients were behaviorally diagnosed using coma recovery scale-revised (CRS-R). The maximal peak frequency in the 1–14 Hz range successfully predicted both neurobehavioral capacity of patients as indicated by CRS-R total score and PDOC diagnosis. Additionally, in patients in whom only one peak within the 1–14 Hz range was observed, the AP gradient significantly contributed to the accuracy of prediction. We have identified three distinct spectral profiles of patients, likely representing separate neurophysiological modes of thalamocortical functioning. Etiology did not have significant influence on the obtained results.

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来源期刊
Brain Topography
Brain Topography 医学-临床神经学
CiteScore
4.70
自引率
7.40%
发文量
41
审稿时长
3 months
期刊介绍: Brain Topography publishes clinical and basic research on cognitive neuroscience and functional neurophysiology using the full range of imaging techniques including EEG, MEG, fMRI, TMS, diffusion imaging, spectroscopy, intracranial recordings, lesion studies, and related methods. Submissions combining multiple techniques are particularly encouraged, as well as reports of new and innovative methodologies.
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