Inter-rater Reliablility of Sharp Wave Ripple Detection in Recently Trained Experts

Sharp wave ripples (SWR) are a brain phenomenon that originate in the hippocampus, a region crucial for memory and learning. These events occur during periods of rest or slow-wave sleep and are characterized by fast oscillatory bursts (150-250Hz) and are thought to be important for the brain to consolidate memory. SWR are believed to facilitate the transfer of information from the hippocampus to the cortex, strengthening synaptic connections & enabling long-term memory storage. Accurate detection & assessment of SWRs is essential for advancing how the brain processes and stores information. This study examines the accuracy/consistency of SWR image selection and assessment among three recently trained technicians, using a combination of open-source tools to assist in SWR identification, reducing novice technicians' learning curve. These tools included automated detection algorithms and interactive interfaces, provided immediate feedback and improved technician proficiency. In this study, technicians identified SWRs from neural recordings, utilizing both manual inspection & software-assisted techniques. Results demonstrate that using open-source software enhances accuracy in ripple detection and also standardizes the identification process across users, decreasing inter-rater variability. By integrating these open-source resources, laboratories can train novice technicians more efficiently, ensuring consistency in SWR detection. Reducing the learning curve for ripple detection This study highlights the increasing demand for accurate, rapid, & reproducible SWR analysis and the growing potential of open-source tools to democratize neurophysiological research, as a tool that can be deployed to train undergraduate volunteers and research assistants, and making advanced neural analysis accessible to more researchers and institutions.