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ComBat-Predict mejora la generalización de los modelos de neuroimagen a nuevos sitios
bioRxiv : the preprint server for biology
|September 5, 2025
Resumen
Este estudio presenta ComBat-Predict (CB-Predict), un nuevo método de armonización para los datos de neuroimagen. CB-Predict aborda efectivamente el sesgo relacionado con el sitio, lo que permite un análisis preciso del desarrollo cerebral en diversos conjuntos de datos y nuevos sitios de investigación.
Área de la Ciencia:
- La neuroimagen y la neurociencia computacional
- La bioestadística y la armonización de datos
- Investigación de las enfermedades neurodegenerativas
Sus antecedentes:
- La neuroimagen es crucial para estudiar el envejecimiento del cerebro y enfermedades como el Alzheimer.
- Los estudios de múltiples sitios son esenciales para la investigación del desarrollo cerebral a gran escala, pero introducen sesgos específicos del sitio.
- Los métodos de armonización existentes luchan por generalizarse a nuevos sitios de datos no vistos.
Objetivo del estudio:
- Desarrollar un nuevo método de armonización, ComBat-Predict (CB-Predict), que se generalice en nuevos sitios.
- Para mitigar el sesgo relacionado con el sitio en conjuntos de datos de neuroimagen de múltiples sitios.
- Mejorar la traducción de los modelos de neuroimagen a nuevos entornos clínicos y de investigación.
Principales métodos:
- ComBat-Predict propuesto (CB-Predict), una extensión del método ComBat para el ajuste del efecto de sitio.
- Aplicado CB-Predict a los datos de la Iniciativa de Neuroimagen de la Enfermedad de Alzheimer (ADNI) y el Consorcio de Gráficos Cerebrales de la Duración de la Vida (LBCC).
- Se evaluó la capacidad de CB-Predict para generalizar a nuevos sitios con datos limitados y efectos de sitio desconocidos.
Principales resultados:
- CB-Predict mitigó efectivamente el sesgo en las medidas de espesor cortical de ADNI al generalizar a nuevos datos.
- El método demostró una alta precisión en la predicción del grosor cortical.
- CB-Predict redujo con éxito la varianza relacionada con el sitio en las puntuaciones del centílio del conjunto de datos LBCC.
Conclusiones:
- ComBat-Predict (CB-Predict) ofrece una solución robusta para armonizar los datos de neuroimagen en múltiples sitios, incluidos sitios nuevos e invisibles.
- El método mejora la generalizabilidad y el potencial de traducción de la investigación de neuroimagen.
- CB-Predict facilita estudios a gran escala más confiables sobre el desarrollo cerebral y la neurodegeneración.
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