Division of Sleep Medicine, Department of Neurology, LSU Health Sciences Center, Shreveport, LA, USA.
Department of Pulmonary & Critical Care Medicine, Johns Hopkins Medicine, Baltimore, MD, USA.
Psychiatry Res. 2014 Dec 30;224(3):335-40. doi: 10.1016/j.pscychresns.2014.10.004. Epub 2014 Oct 14.
Analysis of brain recurrence (ABR) is a novel computational method that uses two variables for sleep depth and two for sleep fragmentation to quantify temporal changes in non-random brain electrical activity. We postulated that ABR of the sleep-staged EEG could identify an EEG signature specific for the presence of mental health symptoms. Using the Mental Health Inventory Questionnaire (MHI-5) as ground truth, psychological distress was assessed in a study cohort obtained from the Sleep Heart Health Study. Subjects with MHI-5 <50 (N=34) were matched for sex, BMI, age, and race with 34 subjects who had MHI-5 scores >50. Sixteen ABR markers derived from the EEG were analyzed using linear discriminant analysis to identify marker combinations that reliably classified individual subjects. A biomarker function computed from 12 of the markers accurately classified the subjects based on their MHI-5 scores (AUROC=82%). Use of additional markers did not improve classification accuracy. Subgroup analysis (20 highest and 20 lowest MHI-5 scores) improved classification accuracy (AUROC=89%). Biomarker values for individual subjects were significantly correlated with MHI-5 score (r=0.36, 0.54 for N=68, 40, respectively). ABR of EEGs obtained during sleep successfully classified subjects with regard to the severity of mental health symptoms, indicating that mood systems were reflected in brain electrical activity.
脑复发分析（ABR）是一种新的计算方法，它使用两个变量来表示睡眠深度，两个变量来表示睡眠碎片化，以量化非随机脑电活动的时间变化。我们假设睡眠分期脑电图的 ABR 可以识别出特定心理健康症状存在的脑电图特征。使用心理健康量表问卷（MHI-5）作为基准，使用睡眠心脏健康研究中的研究队列评估了心理困扰。将 MHI-5<50（N=34）的受试者与 MHI-5>50 的 34 名受试者按性别、BMI、年龄和种族进行匹配。使用线性判别分析对从 EEG 中得出的 16 个 ABR 标记物进行分析，以识别可可靠分类个体受试者的标记物组合。根据其 MHI-5 评分，从 12 个标记物中计算出的生物标志物函数准确地对受试者进行分类（AUROC=82%）。使用更多标记物并不能提高分类准确性。亚组分析（MHI-5 评分最高和最低的 20 名受试者）提高了分类准确性（AUROC=89%）。个体受试者的生物标志物值与 MHI-5 评分显著相关（r=0.36，0.54，N=68，40）。睡眠期间获得的 EEG 的 ABR 成功地对心理健康症状严重程度的受试者进行了分类，表明情绪系统反映在脑电活动中。
