Department of Psychological Sciences, Kennesaw State University, GA, USA.
Departments of Psychiatry, Neurology, and Psychology and School of Nursing, University of Michigan, Ann Arbor, MI, USA.
Int J Psychophysiol. 2022 Jul;177:213-219. doi: 10.1016/j.ijpsycho.2022.05.012. Epub 2022 May 23.
Finding the baseline resting-state EEG markers for early identification of cognitive decline can contribute to the identification of individuals at risk of further change. Potential applications include identifying participants for clinical trials, early treatment, and evaluation of treatment, accessible even from a community setting.
Analyses were completed on a sample of 99 (ages 60-90) consensus-diagnosed, community-dwelling African Americans (58 cognitively typical/HC, and 41 mildly cognitively impaired/MCI), who were recruited from the Michigan Alzheimer's Disease Research Center (MADRC) and the Wayne State University Institute of Gerontology. In addition to neuropsychological testing with CogState and Toolbox computerized batteries, resting-state EEGs (rsEEG, eyes closed) were acquired before and after participants were engaged in a visual motion direction discrimination task. rsEEG frontal alpha asymmetry (FAA) and frontal beta asymmetry (FBA) were calculated.
FAA showed no difference across groups for the pre-task resting state. FBA was significantly different between groups, with more asymmetric frontal beta in MCI. Both physiological indices, however, along with computerized neuropsychological tests were significant predictors in logistic regression classification of MCI vs. control participants.
rsEEG asymmetries can contribute significantly to successful discrimination of older persons with MCI from those without, over and above cognitive testing, alone.
寻找基线静息态脑电图标志物以早期识别认知能力下降,可以帮助识别出有进一步变化风险的个体。潜在的应用包括从社区环境中识别出临床试验、早期治疗和治疗评估的参与者。
对 99 名(年龄 60-90 岁)经共识诊断的、居住在社区的非裔美国人(58 名认知正常/HC 和 41 名轻度认知障碍/MCI)进行了分析,这些参与者是从密歇根阿尔茨海默病研究中心(MADRC)和韦恩州立大学老年学研究所招募的。除了使用 CogState 和 Toolbox 计算机化电池进行神经心理学测试外,还在参与者进行视觉运动方向辨别任务之前和之后采集静息态脑电图(rsEEG,闭眼)。计算了 rsEEG 额区 alpha 不对称(FAA)和额区 beta 不对称(FBA)。
在任务前静息状态下,FAA 在组间没有差异。FBA 在组间存在显著差异,MCI 组的额区 beta 不对称更为明显。然而,这两种生理指标以及计算机化神经心理学测试都是 MCI 与对照组参与者分类的逻辑回归的显著预测因素。
rsEEG 不对称性可以显著有助于成功区分有和无 MCI 的老年人,而不仅仅是认知测试。