Bosl William J, Bosquet Enlow Michelle, Lock Eric F, Nelson Charles A
Center for AI & Medicine, University of San Francisco, San Francisco, CA, United States.
Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.
Front Psychiatry. 2023 Jul 17;14:1158569. doi: 10.3389/fpsyt.2023.1158569. eCollection 2023.
Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety.
We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors ( = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls.
We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders.
This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.
焦虑是青少年心理病理学最常见的表现形式，对学业、社交和适应功能产生负面影响，并增加成年后患心理健康问题的风险。焦虑症只有在临床症状出现后才会被诊断出来，这可能会错过在关键的早期前驱期进行干预的机会。在本研究中，我们采用了一种新的实证方法来提取脑电图（EEG）的非线性特征，目的是发现区分焦虑症儿童与健康儿童的脑电动力学差异。此外，我们还研究了这种方法是否能够区分患有外化性障碍的儿童与健康儿童以及患有焦虑症的儿童。
我们使用了一种新颖的监督张量分解方法，从在婴儿期以及3岁、5岁和7岁时接受评估的儿童纵向样本中的重复多频非线性脑电图测量中提取潜在因素。我们首先通过证明实际年龄与潜在脑电图复杂性因素高度相关（=0.77）来检验该方法的有效性。然后，我们使用5折交叉验证方案分别计算潜在因素，以区分患有焦虑症的儿童与健康对照组，同样地，也用于区分患有外化性障碍的儿童与健康对照组。
我们发现，需要7岁时的脑电图记录得出的潜在因素来区分患有焦虑症的儿童与健康对照组；仅靠婴儿期、3岁或5岁时的记录是不够的。然而，来自两个（5岁、7岁）或三个（3岁、5岁、7岁）记录的数据比仅7岁时的记录能得出更好的结果。使用3岁和5岁时的脑电图数据可以检测出外化性障碍，同样，两个或三个记录的数据比任何单个时间点的数据能得出更好的结果。此外，出生时指定的性别是一个重要的协变量，可提高两个障碍组的准确性，而出生体重作为协变量适度提高了外化性障碍的准确性。婴儿期脑电图记录对焦虑症或外化性障碍的分类准确性没有贡献。
本研究表明，如果在适当的年龄进行选择，从儿童脑电图记录中提取的潜在因素有望成为焦虑症和外化性障碍的候选生物标志物。
