Estimulación cerebral profunda abierta y cerrada en trastornos neurológicos hacia una estimulación cerebral...

Neurological and psychiatric disorders such as Parkinson's disease, essential tremor, epilepsy, Tourette's syndrome, depression, and chronic pain remain major causes of disability worldwide. For patients who fail to respond to medication, neuromodulation, particularly deep-brain stimulation (DBS), has become a cornerstone therapy. Traditional open-loop DBS delivers continuous stimulation using pre-set parameters, yielding substantial clinical benefits but also limitations, including side effects, energy inefficiency, and lack of adaptability to dynamic brain states. These drawbacks have motivated the development of closed-loop, or adaptive, DBS systems, which incorporate real-time biomarkers to adjust stimulation in response to neural or physiological signals. Emerging clinical studies demonstrate that closed-loop approaches can improve symptom control in selected disorders, while consistently reducing stimulation time and prolonging device longevity. Despite promising results, outcomes remain heterogeneous across patients, largely due to variability in biomarkers, algorithms, and methodological approaches. Ethical considerations and technical challenges also remain significant barriers to widespread implementation. This narrative review synthesizes evidence on open- and closed-loop neuromodulation across neurological and psychiatric disorders, emphasizing their comparative advantages, limitations, and translational challenges. We highlight the role of biomarkers, adaptive algorithms, and machine learning in shaping personalized neuromodulation and argue that closed-loop stimulation represents a paradigm shift toward precision medicine. Ultimately, the integration of robust biomarkers, predictive algorithms, and scalable clinical frameworks will be critical to realizing the full potential of closed-loop neuromodulation in transforming brain stimulation therapies.