Yamanashi Takehiko, Malicoat Johnny R, Steffen Kalvon T, Zarei Kasra, Li Rui, Purnell Benton S, Najafi Annice, Saito Kenji, Singh Uday, Toth Brandon A, Lee Shelley, Dailey Michael E, Cui Huxing, Kaneko Koichi, Cho Hyunkeun Ryan, Iwata Masaaki, Buchanan Gordon F, Shinozaki Gen
University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USA; Tottori University Faculty of Medicine, Department of Neuropsychiatry, Yonago, Japan.
University of Iowa Carver College of Medicine, Department of Psychiatry, Iowa City, IA, USA.
J Psychiatr Res. 2021 Jan;133:205-211. doi: 10.1016/j.jpsychires.2020.12.036. Epub 2020 Dec 15.
Most of the animal studies using inflammation-induced cognitive change have relied on behavioral testing without objective and biologically solid methods to quantify the severity of cognitive disturbances. We have developed a bispectral EEG (BSEEG) method using a novel algorithm in clinical study. This method effectively differentiates between patients with and without delirium, and predict long-term mortality. In the present study, we aimed to apply our bispectral EEG (BSEEG) method, which can detect patients with delirium, to a mouse model of delirium with systemic inflammation induced by lipopolysaccharides (LPS) injection. We recorded EEG after LPS injection using wildtype early adulthood mice (2~3-month-old) and aged mice (18-19-month-old). Animal EEG recordings were converted for power spectral density to calculate BSEEG score using the similar BSEEG algorithm previously developed for our human study. The BSEEG score was relatively stable and slightly high during the day. Alternatively, the BSEEG score was erratic and low in average during the night. LPS injection increased the BSEEG score dose-dependently and diminished the diurnal changes. The mean BSEEG score increased much more in the aged mice group as dosage increased. Our results suggest that BSEEG method can objectively "quantify" level of neuro-Inflammation induced by systemic inflammation (LPS), and that this BSEEG method can be useful as a model of delirium in mice.
大多数利用炎症诱导认知变化的动物研究都依赖行为测试,而缺乏客观且生物学上可靠的方法来量化认知障碍的严重程度。我们在临床研究中开发了一种使用新型算法的双谱脑电图(BSEEG)方法。该方法能有效区分谵妄患者和非谵妄患者,并预测长期死亡率。在本研究中,我们旨在将能够检测谵妄患者的双谱脑电图(BSEEG)方法应用于脂多糖(LPS)注射诱导全身性炎症的小鼠谵妄模型。我们使用野生型成年早期小鼠(2至3个月大)和老年小鼠(18至19个月大)在注射LPS后记录脑电图。动物脑电图记录被转换为功率谱密度,使用先前为我们的人体研究开发的类似BSEEG算法来计算BSEEG评分。BSEEG评分在白天相对稳定且略高。相反,BSEEG评分在夜间不稳定且平均较低。LPS注射使BSEEG评分呈剂量依赖性增加,并减少了昼夜变化。随着剂量增加,老年小鼠组的平均BSEEG评分增加得更多。我们的结果表明,BSEEG方法可以客观地“量化”全身性炎症(LPS)诱导的神经炎症水平,并且这种BSEEG方法可作为小鼠谵妄模型。