Consensus-based Sharp-Wave Ripple detection and its application in an alcohol administration model.

In the hippocampus, slow waves are accompanied by brief population bursts of high-frequency oscillations (150-250 Hz) known as Sharp-Wave Ripples (SWRs), a phenomenon associated with memory consolidation during offline brain states and Non-Rapid Eye Movement (NREM) sleep. Despite the relevance of SWRs, no standardized criterion for their automatic detection has been established. This work introduces a consensus-based algorithm that first identifies sharp waves and then detects ripples occurring within these intervals. Events are designated as true SWRs only when at least two principal methodologies report overlapping detections. Comparative analyses showed that one detector generated more candidate events but with reduced precision, whereas the other was more selective but computationally slower. The consensus strategy improved reliability by emphasizing the concurrence of independent detectors, contributing to efforts toward standardized and reproducible SWR analysis. The algorithm was used within an alcohol administration model to quantify SWR rate, duration, and peak frequency across control, vehicle, and treated groups. Although no significant group-level differences emerged under the short-term exposure protocol, a significant increase in SWR peak frequency was observed in the treated group after the open field test, suggesting the presence of a transient compensation mechanism. These findings shed light on the brain's ability to adapt temporarily to specific behavioral tasks. However, it is essential to emphasize that additional research is crucial to fully understand the long-term implications and associations with alcohol-induced changes in brain structures and SWR generation.