Electrical Engineering & Computer Science, University of Missouri, Columbia, MO 65211, USA.
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 197 University Ave, Newark, NJ 07102, USA.
J Neurosci Methods. 2023 May 1;391:109865. doi: 10.1016/j.jneumeth.2023.109865. Epub 2023 Apr 21.
Cognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods.
To address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal's spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance.
The characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used.
Existing methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts.
This pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.
认知过程与局部场电位和脑电图的快速振荡有关。由于这些过程会受到衰老和疾病的影响，因此人们对靶向治疗这些过程的兴趣日益浓厚。然而，这是一项具有挑战性的任务，因为这些过程通常表现为短暂的爆发。此外，它们被反映其他神经过程的宽带非周期性活动所掩盖。这些特性使得开发用于估计检测方法可靠性的分析工具变得极其困难。
为了应对这一挑战，我们开发了一个带有四个处理步骤的开源工具包，该工具包可以针对特定的大脑状态和个体进行定制。首先，将功率谱分解为周期性和非周期性分量，分别估计它们的特性。其次，推导并优化对周期性分量有贡献的瞬态振荡爆发的特性，以消除非周期性分量的干扰。第三，使用爆发特性和非周期性功率谱，合成与观察信号的频谱时变特性相匹配的替代神经信号。最后，在替代信号上运行的振荡爆发检测算法进行了接收机工作特征分析，从而深入了解其性能。
特征提取算法跨多个频带和脑区提取了振荡爆发的特征，从而可以针对特定记录评估检测性能。对于我们的数据集，发现γ 爆发的最佳检测阈值低于常用的阈值。
现有的方法对功率谱进行了特征化，而我们的方法则评估了振荡爆发的检测。
该流水线方便了针对个体记录的检测算法的阈值评估。
