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    Fed-ComBat ofrece un marco federado para armonizar datos de neuroimagen entre múltiples centros, preservando la privacidad del paciente al evitar la centralización de datos. Este enfoque aborda eficazmente los efectos de lote en estudios de afecciones como la enfermedad de Alzheimer y el trastorno del espectro autista.

    Palabras clave:
    armonización de datosefectos de loteaprendizaje federadoneuroimagenestudios multicéntricosprivacidad de datosComBatdatos descentralizadosenfermedad de Alzheimertrastorno del espectro autista

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    Área de la Ciencia:

    • Neurociencia
    • Bioestadística
    • Ciencia de Datos

    Sus antecedentes:

    • Los estudios multicéntricos requieren la armonización de datos para abordar sesgos y garantizar la interoperabilidad.
    • Los métodos de armonización de vanguardia actuales, como ComBat, se basan en la modelización de efectos aleatorios pero a menudo requieren la centralización de datos, lo que plantea riesgos de privacidad y gobernanza.
    • El análisis de datos descentralizado es crucial para estudios a gran escala que involucran información sensible del paciente.

    Objetivo del estudio:

    • Introducir Fed-ComBat, un marco federado novedoso para la armonización de efectos de lote en datos descentralizados.
    • Permitir la preservación de efectos de covariables no lineales sin centralización de datos o suposiciones paramétricas previas.
    • Evaluar el rendimiento de Fed-ComBat frente a los métodos de armonización centralizados y distribuidos existentes.

    Principales métodos:

    • Desarrollo de Fed-ComBat, un marco de aprendizaje federado para la corrección de efectos de lote.
    • Implementación de la modelización de efectos aleatorios en un entorno de datos descentralizado.
    • Validación utilizando datos simulados extensos y análisis de 7 cohortes de neuroimagen del mundo real (controles sanos, EP, EA, TEA).

    Principales resultados:

    • Fed-ComBat logra resultados de armonización comparables a los métodos centralizados en escenarios lineales y no lineales.
    • El análisis de datos de neuroimagen reales demostró un rendimiento comparable entre los modelos centralizados y federados para armonizar las trayectorias del grosor del hipocampo a lo largo de la vida.
    • La armonización federada utilizando Fed-ComBat se alinea con los hallazgos de la literatura existente para modelos no lineales.

    Conclusiones:

    • Fed-ComBat proporciona una solución eficaz y que preserva la privacidad para la armonización de efectos de lote en estudios de neuroimagen multicéntricos descentralizados.
    • El marco maneja con éxito sesgos lineales y no lineales sin comprometer la privacidad de los datos ni requerir la centralización de datos.
    • Fed-ComBat representa un avance significativo para la investigación colaborativa en neurociencia, particularmente para estudios que involucran a poblaciones vulnerables y datos sensibles.