Big Data in electrophysiology

[Digital precision medicine in rhythmology : Risk prediction of recurrences, sudden cardiac death, and outcome]

Digital precision medicine is gaining increasing importance in rhythmology, especially in the treatment of cardiac arrhythmias. This trend is driven by the advancing digitization in healthcare and the availability of large amounts of data from various sources such as electrocardiograms (ECGs), implants like pacemakers and implantable cardioverter-defibrillators (ICDs), as well as wearables like smartwatches and fitness trackers. Through the analysis of this data, physicians can develop more precise and individualized diagnoses and treatment strategies for patients with cardiac arrhythmias. For example, subtle changes in ECGs can be identified, indicating potentially dangerous arrhythmias. Genetic analyses and resulting large datasets also play an increasingly significant role, especially in hereditary ion channel disorders such as long QT syndrome (LQTS) and Brugada syndrome (BrS), as well as in lone atrial fibrillation (AF). Precision medicine enables the development of individualized treatment approaches tailored to the specific needs and risk factors of each patient. This can help improve screening strategies, reduce adverse events, and ultimately enhance the quality of life for patients. Technological advancements such as big data, artificial intelligence, machine learning, and predictive analytics play a crucial role in predicting the risk of arrhythmias and sudden cardiac death. These concepts enable more precise and personalized predictions and support physicians in the treatment and monitoring of their patients.

Lateral Orbitofrontal Cortex Encodes Presence of Risk and Subjective Risk Preference During Decision-Making

Adaptive decision-making requires consideration of objective risks and rewards associated with each option, as well as subjective preference for risky/safe alternatives. Inaccurate risk/reward estimations can engender excessive risk-taking, a central trait in many psychiatric disorders. The lateral orbitofrontal cortex (lOFC) has been linked to many disorders associated with excessively risky behavior and is ideally situated to mediate risky decision-making. Here, we used single-unit electrophysiology to measure neuronal activity from lOFC of freely moving rats performing in a punishment-based risky decision-making task. Subjects chose between a small, safe reward and a large reward associated with either 0% or 50% risk of concurrent punishment. lOFC activity repeatedly encoded current risk in the environment throughout the decision-making sequence, signaling risk before, during, and after a choice. In addition, lOFC encoded reward magnitude, although this information was only evident during action selection. A Random Forest classifier successfully used neural data accurately to predict the risk of punishment in any given trial, and the ability to predict choice via lOFC activity differentiated between and risk-preferring and risk-averse rats. Finally, risk preferring subjects demonstrated reduced lOFC encoding of risk and increased encoding of reward magnitude. These findings suggest lOFC may serve as a central decision-making hub in which external, environmental information converges with internal, subjective information to guide decision-making in the face of punishment risk.