Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings.

BACKGROUND: 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. NEW METHOD: 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. RESULTS: 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. COMPARISON WITH EXISTING METHODS: Existing methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts. CONCLUSIONS: This pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.

Electrical Stimulation Evokes Rotational Behavior In Tandem with Exocytotic-like Increases in Dopamine Measured by In Vivo Intracerebral Microdialysis.

BACKGROUND: Electrical Stimulation is a traditional tool in neuroscience and is commonly used in vivo to evoke behavior and in vitro to study neural mechanisms. In vivo intracerebral microdialysis, also a traditional technique, is used to assay neurotransmitter release. However, the combination of these techniques is highly limited to studies using anesthetized animals; therefore, evoking and measuring exocytotic neurotransmitter release in awake models is lacking. Combining these techniques in an awake animal preparation is presented here with evidence to support the mechanistic action of electrical stimulation in vivo. NEW METHODS: This report presents converging evidence to validate the combination of intracerebral electrical stimulation with microdialysis as a novel procedure to study exocytotic-like dopamine release in behaving animals. RESULTS: It is shown that electrical stimulation of the medial forebrain bundle can be used to evoke frequency- and intensity-dependent exocytotic-like dopamine overflow and rotational behavior that are sensitive to Na+ channel blockade and Ca++ availability. COMPARISON WITH EXISTING METHODS: Studies using modern techniques to evoke neurotransmitter release, combined with in vivo intracerebral microdialysis, and measured behavioral output are scarce. In contrast, commonly used pharmacological methods often are less precise and inefficient to evoke exocytotic dopamine release and behavior. Here we demonstrate, the combination of in vivo intracerebral microdialysis with electrical stimulation as a simple approach to simultaneously assess physiologically relevant neurotransmitter 'release' and behavior. CONCLUSIONS: Research that aims to understand how dopamine neurotransmission is altered in behavioral disorders can utilize this innovative combination of electrical stimulation with in vivo intracerebral microdialysis.