PDF(Pubmed)
Abstract：
UNASSIGNED: This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data.
UNASSIGNED: We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution.
UNASSIGNED: We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals.
UNASSIGNED: The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.
UNASSIGNED: We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution.
UNASSIGNED: We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals.
UNASSIGNED: The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.
摘要：
■这项研究旨在通过开发一种利用夜间脑电图（EEG）数据的新型深度学习模型来改善大脑年龄估计。
■我们通过提出在多个队列数据上训练和评估的模型来解决当前大脑年龄预测方法的局限性，涵盖广泛的年龄范围。该模型采用一维SwinTransformer从睡眠EEG信号中有效提取复杂模式，并采用具有注意机制的卷积神经网络来总结睡眠结构特征。基于多流学习的框架专注地融合了这两个特征，利用睡眠结构信息来指导和增强脑电图特征。后预测模型旨在整晚整合与年龄相关的特征。此外，我们提出了一个DecadeCE损失函数来解决年龄分布不均匀的问题。
■我们利用来自13,616名受试者的18,767个多导睡眠图（PSG）来开发和评估所提出的模型。该模型在混合队列测试集上的平均绝对误差（MAE）为4.19，相关性为0.97，在独立测试集上，MAE为6.18年，相关性为0.78。与其他也使用EEG的研究相比，我们的大脑年龄估计工作将误差减少了1年以上，达到神经成像水平。估计的大脑年龄指数显示出纵向敏感性，并且相对于健康个体，患有精神病或神经系统疾病的个体显着增加了1.27年。
■本研究提出的多流深度学习模型，基于夜间脑电图，代表了一种更准确的估计大脑年龄的方法。利用夜间睡眠脑电图预测大脑年龄既具有成本效益，又善于捕捉动态变化。这些发现证明了脑电图在预测大脑年龄方面的潜力，提出了一种非侵入性和可访问的方法来评估大脑老化。
■我们通过提出在多个队列数据上训练和评估的模型来解决当前大脑年龄预测方法的局限性，涵盖广泛的年龄范围。该模型采用一维SwinTransformer从睡眠EEG信号中有效提取复杂模式，并采用具有注意机制的卷积神经网络来总结睡眠结构特征。基于多流学习的框架专注地融合了这两个特征，利用睡眠结构信息来指导和增强脑电图特征。后预测模型旨在整晚整合与年龄相关的特征。此外，我们提出了一个DecadeCE损失函数来解决年龄分布不均匀的问题。
■我们利用来自13,616名受试者的18,767个多导睡眠图（PSG）来开发和评估所提出的模型。该模型在混合队列测试集上的平均绝对误差（MAE）为4.19，相关性为0.97，在独立测试集上，MAE为6.18年，相关性为0.78。与其他也使用EEG的研究相比，我们的大脑年龄估计工作将误差减少了1年以上，达到神经成像水平。估计的大脑年龄指数显示出纵向敏感性，并且相对于健康个体，患有精神病或神经系统疾病的个体显着增加了1.27年。
■本研究提出的多流深度学习模型，基于夜间脑电图，代表了一种更准确的估计大脑年龄的方法。利用夜间睡眠脑电图预测大脑年龄既具有成本效益，又善于捕捉动态变化。这些发现证明了脑电图在预测大脑年龄方面的潜力，提出了一种非侵入性和可访问的方法来评估大脑老化。
