Biomarker development is currently a high priority in neurodevelopmental disorder research. For many types of biomarkers (particularly biomarkers of diagnosis), reliability over short periods is critically important. In the field of autism spectrum disorder (ASD), resting electroencephalography (EEG) power spectral densities (PSD) are well-studied for their potential as biomarkers. Classically, such data have been decomposed into pre-specified frequency bands (e.g., delta, theta, alpha, beta, and gamma). Recent technical advances, such as the Fitting Oscillations and One-Over-F (FOOOF) algorithm, allow for targeted characterization of the features that naturally emerge within an EEG PSD, permitting a more detailed characterization of the frequency band-agnostic shape of each individual’s EEG PSD. Here, using two resting EEGs collected a median of 6 days apart from 22 children with ASD and 25 typically developing (TD) controls during the Feasibility Visit of the Autism Biomarkers Consortium for Clinical Trials, we estimate test-retest reliability based on the characterization of the PSD shape in two ways: (1) Using the FOOOF algorithm we estimate six parameters (offset, slope, number of peaks, and amplitude, center frequency and bandwidth of the largest alpha peak) that characterize the shape of the EEG PSD; and (2) using nonparametric functional data analyses, we decompose the shape of the EEG PSD into a reduced set of basis functions that characterize individual power spectrum shapes. We show that individuals exhibit idiosyncratic PSD signatures that are stable over recording sessions using both characterizations. Our data show that EEG activity from a brief 2-min recording provides an efficient window into characterizing brain activity at the single-subject level with desirable psychometric characteristics that persist across different analytical decomposition methods. This is a necessary step towards analytical validation of biomarkers based on the EEG PSD and provides insights into parameters of the PSD that offer short-term reliability (and thus promise as potential biomarkers of trait or diagnosis) vs. those that are more variable over the short term (and thus may index state or other rapidly dynamic measures of brain function). Future research should address the longer-term stability of the PSD, for purposes such as monitoring development or response to treatment.

生物标志物开发目前是神经发育障碍研究的重中之重。对于许多类型的生物标志物（尤其是诊断生物标志物），短期内的可靠性至关重要。在自闭症谱系障碍 (ASD) 领域，静息脑电图 (EEG) 功率谱密度 (PSD) 因其作为生物标志物的潜力而得到充分研究。传统上，此类数据已被分解为预先指定的频带（例如，delta、theta、alpha、beta 和 gamma）。最近的技术进步，例如拟合振荡和 One-Over-F (FOOOF) 算法，允许对 EEG PSD 中自然出现的特征进行有针对性的表征，从而可以更详细地表征每个个体的频带不可知形状脑电图 PSD。这里，在自闭症生物标志物联盟的临床试验可行性访问期间，使用两个静息脑电图收集了中位数为 6 天的自闭症儿童和 25 名正常发育 (TD) 对照，我们根据 PSD 的表征估计重测可靠性形状有两种方式：（1）使用 FOOOF 算法，我们估计表征 EEG PSD 形状的六个参数（偏移量、斜率、峰值数量以及最大 α 峰值的幅度、中心频率和带宽）；(2) 使用非参数函数数据分析，我们将 EEG PSD 的形状分解为一组简化的基函数，这些基函数表征了各个功率谱的形状。我们表明，个人表现出特殊的 PSD 签名，这些签名在使用这两种特征的录制会话中是稳定的。我们的数据表明，来自 2 分钟简短记录的 EEG 活动提供了一个有效的窗口，可以在单个受试者水平上表征大脑活动，并具有在不同分析分解方法中持续存在的理想心理测量特征。这是基于 EEG PSD 对生物标志物进行分析验证的必要步骤，并提供了对 PSD 参数的见解，这些参数提供了短期可靠性（因此有望成为性状或诊断的潜在生物标志物）与那些在短期（因此可以索引状态或其他快速动态的大脑功能测量）。未来的研究应该解决 PSD 的长期稳定性，用于监测发展或对治疗的反应等目的。