用多模态磁共振图像预测儿童发育中的脑年龄。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Hongjie Cai, Aojie Li, Guangjun Yu, Xiujun Yang, Manhua Liu
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引用次数: 1

摘要

众所周知,儿童早期的大脑发育非常迅速和复杂,大脑结构和功能的神经和生理变化是基于年龄的。脑成熟度是评价儿童正常发育的重要指标。在本文中,我们提出了一个多模态回归框架,结合结构磁共振成像(sMRI)和扩散张量成像(DTI)数据的特征进行儿童年龄预测。首先,从sMRI和DTI数据中提取三种类型的特征。其次,我们提出将稀疏编码和Q-Learning相结合,从每个模态中进行特征选择。最后,采用基于接近度的随机森林进行集合回归,融合多模态特征进行年龄预测。该方法在上海儿童医院招募了76名2岁以下幼儿和136名2 ~ 15岁儿童,共212名被试进行了评价。结果表明,综合多模态特征预测幼儿(0-2岁)年龄的准确率最高,均方根误差(RMSE)为0.208年,平均绝对误差(MAE)为0.150年;较大儿童(2-15岁)年龄预测的RMSE为1.666年,平均绝对误差(MAE)为1.087年。我们已经证明,通过Q-Learning选择的特征可以持续提高预测精度。预测结果的对比表明,该方法的预测效果优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images.

Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images.

It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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