Department of Psychology, University of Oklahoma, 455 W. Lindsey Street, Dale Hall Tower, Room 705, Norman, OK, 73019-2007, USA.
Department of Pediatrics, Section on Developmental and Behavioral Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
Sci Rep. 2024 Oct 3;14(1):22982. doi: 10.1038/s41598-024-72935-6.
Recent failures translating preclinical behavioral treatment effects to positive clinical trial results in humans with Fragile X Syndrome (FXS) support refocusing attention on biological pathways and associated measures, such as electroencephalography (EEG), with strong translational potential and small molecule target engagement. This study utilized guided machine learning to test promising translational EEG measures (resting power and auditory chirp oscillatory variables) in a large heterogeneous sample of individuals with FXS to identify best performing EEG variables for reliably separating individuals with FXS, and genetically-mediated subgroups within FXS, from typically developing controls. Best performing variables included resting relative frontal theta power, all combined posterior-head resting power bands, posterior peak alpha frequency (PAF), combined PAF across all measured regions, combined theta, alpha, and gamma power during the chirp, and all combined chirp oscillatory variables. Sub-group analyses for resting EEG best discriminated non-mosaic FXS males via frontal theta resting relative power (AUC = 0.8759), even with data reduced to a 20-channel clinical montage (AUC = 0.9062). In the chirp task, FXS females and non-mosaic males were nearly perfectly discriminated by combined theta, alpha, and gamma power (AUC = 0.9444) and a combination of all variables (AUC = 0.9610), respectively. Results support use of resting and auditory oscillatory tasks to reliably identify neural deficit in FXS, and to identify specific translational targets for genetically-mediated sub-groups, supporting potential points for stratification.
最近在脆性 X 综合征 (FXS) 患者中将临床前行为治疗效果转化为阳性临床试验结果的失败，支持重新关注具有强大转化潜力和小分子靶标结合的生物学途径和相关测量，例如脑电图 (EEG)。本研究利用有指导的机器学习，在一个大型异质 FXS 个体样本中测试了有前途的转化 EEG 测量（静息功率和听觉啁啾振荡变量），以确定最佳的 EEG 变量，用于可靠地区分 FXS 个体、FXS 中的遗传介导亚组和正常发育对照。表现最好的变量包括静息相对额部θ功率、所有组合的后头部静息功率带、后峰值α频率 (PAF)、所有测量区域的组合 PAF、啁啾期间的组合θ、α 和γ功率，以及所有组合的啁啾振荡变量。对于静息 EEG 的亚组分析，通过额部θ静息相对功率最佳区分非镶嵌 FXS 男性（AUC = 0.8759），即使数据减少到 20 通道临床蒙太奇（AUC = 0.9062）。在啁啾任务中，FXS 女性和非镶嵌男性通过组合的θ、α 和γ功率（AUC = 0.9444）和所有变量的组合（AUC = 0.9610）几乎完美地被区分。结果支持使用静息和听觉振荡任务来可靠地识别 FXS 中的神经缺陷，并识别遗传介导亚组的特定转化靶点，支持分层的潜在点。
