Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings

• Published in: Journal of neuroscience methods 2023-05, Vol.391, p.109865-109865, Article 109865
• Main Authors: Chen, Ziao, Headley, Drew B., Gomez-Alatorre, Luisa F., Kanta, Vasiliki, Ho, K.C., Pare, Denis, Nair, Satish S.
• Format: Article
• Language: English
• Subjects: Algorithms; Brain - physiology; Electroencephalogram; Electroencephalography - methods; Electrophysiological Phenomena; Humans; Local field potential; Oscillations; Reproducibility of Results; ROC; Signal detection
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2023.109865

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. •Two algorithms facilitate individualized evaluation of oscillatory burst detection schemes.•The first parameterizes the LFP for synthesizing a ground truth LFP.•The second uses the ground truth to evaluate detection methods using ROC analysis.•Found that in our dataset with gamma bursts the optimal detection threshold is lower than what is commonly used.•Algorithm pipeline packaged as an open-source Matlab toolbox.