Ethridge Lauren E, Pedapati Ernest V, Schmitt Lauren M, Norris Jordan E, Auger Emma, De Stefano Lisa A, Sweeney John A, Erickson Craig A
University of Oklahoma.
Cincinnati Children's Hospital Medical Center.
Res Sq. 2024 Jan 18:rs.3.rs-3849272. doi: 10.21203/rs.3.rs-3849272/v1.
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 whole-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 best discriminated non-mosaic FXS males via whole-head resting relative power (AUC = .9250), even with data reduced to a 20-channel clinical montage. FXS females were nearly perfectly discriminated by combined theta, alpha, and gamma power during the chirp (AUC = .9522). 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，将临床前行为治疗效果转化为脆性X综合征（FXS）患者积极的临床试验结果遭遇失败，这支持了将注意力重新聚焦于具有强大转化潜力和小分子靶点参与的生物途径及相关测量方法，如脑电图（EEG）。本研究利用引导式机器学习，在一个大型异质性FXS个体样本中测试有前景的可转化EEG测量指标（静息功率和听觉啁啾振荡变量），以确定能可靠区分FXS个体以及FXS内基因介导亚组与典型发育对照的最佳EEG变量。表现最佳的变量包括静息相对额叶θ功率、所有组合的全脑静息功率频段、后顶叶α峰值频率（PAF）、所有测量区域的组合PAF、啁啾期间组合的θ、α和γ功率，以及所有组合的啁啾振荡变量。亚组分析通过全脑静息相对功率（AUC = 0.9250）能最好地区分非嵌合型FXS男性，即使数据简化为20通道临床导联。FXS女性通过啁啾期间组合的θ、α和γ功率几乎能被完美区分（AUC = 0.9522）。结果支持使用静息和听觉振荡任务来可靠识别FXS中的神经缺陷，并识别基因介导亚组的特定可转化靶点，为分层提供潜在依据。
