Depresión Resistente y Neurocirugía: Estado del Arte de la Estimulación Cerebral Profunda del Área Subgenual (Área 25)
DOI:
https://doi.org/10.47924/neurotarget2024463Palabras clave:
Estimulación cerebral profunda, depresión resistente al tratamiento, área subgenual, tractografía, sustancia blanca, conectomaResumen
Introducción y Objetivo. La depresión resistente al tratamiento (DRT) es una condición debilitante que no responde a los tratamientos antidepresivos convencionales. La estimulación cerebral profunda (ECP) del área subgenual (Cg25) ha emergido como una opción prometedora para estos pacientes. Este estudio tiene como objetivo evaluar la seguridad, eficacia y mecanismos subyacentes de la ECP en la región Cg25 para tratar la DRT.
Método. Se realizó una revisión sistemática de la literatura utilizando PubMed, Scopus y Cochrane Library. Se incluyeron estudios y revisiones sobre ECP para DRT publicados entre 2005 y 2024. Se extrajeron y analizaron datos sobre mecanismos de acción, eficacia clínica, efectos adversos y factores predictivos de éxito.
Resultados. Se incluyeron un total de 14 estudios abiertos y tres ensayos controlados aleatorios (ECA) que investigaban la ECP del área subgenual. La tasa de respuesta general fue del 56% y la tasa de remisión del 35%. La variabilidad en los resultados se relacionó con diferentes parámetros de estimulación y la cronicidad de la enfermedad. Los estudios resaltaron la importancia de dirigir la estimulación hacia tractos específicos de sustancia blanca para optimizar los resultados clínicos. El uso de tractografía probabilística para mapear el "prototipo de conectoma" óptimo ha mostrado mejoras prometedoras en las tasas de respuesta y remisión.
Conclusiones. La ECP del área subgenual muestra potencial como tratamiento para la DRT, especialmente cuando se utilizan enfoques de orientación basados en conectomas. Se necesita más investigación para optimizar los parámetros de estimulación y validar biomarcadores predictivos de respuesta.
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Derechos de autor 2024 Facundo Villamil, Jorge Mandolesi, Melanie Catena Baudo, Pablo Paolinelli, Maria Florencia Vidal, Elsa Costanzo
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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