Improved prediction of brain age using multimodal neuroimaging data
Improved prediction of brain age using multimodal neuroimaging data
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DOI：
10.1002/hbm.24899
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发表时间：
2019-12-14
影响因子：
4.8
通讯作者：
Liang, Hualou
中科院分区：
文献类型：
--
作者：
Niu, Xin;Zhang, Fengqing;Liang, Hualou
基于成像数据和机器学习（ML）方法的脑年龄预测具有巨大的潜力，可以为认知和精神障碍的发展提供见解。尽管已经提出了不同的机器学习模型，但仍然需要结合不同模态的成像特征对机器学习模型进行系统比较。在本研究中，我们评估了 36 种成像特征和 ML 模型（包括深度学习）组合的预测性能。我们利用来自 839 名受试者的大型数据集的单模态和多模态脑成像数据，包括 MRI、DTI 和 rs-fMRI。我们的研究是初始工作（Liang et al., 2019. Human Brain Mapping）的后续工作，旨在研究不同的分析策略，将 MRI、DTI 和 rs-fMRI 的数据结合起来，以提高大脑年龄预测的准确性。此外，预测大脑年龄差距的传统方法已被证明存在系统偏差。大脑年龄差距和实际年龄之间潜在的非线性关系尚未得到彻底测试。在这里，我们提出了一种新方法，通过考虑性别、实际年龄及其相互作用来纠正大脑年龄差距的系统偏差。由于真实的大脑年龄未知，并且可能偏离实际年龄，我们进一步检查受试者的不同行为表现水平是否可以预测根据神经影像数据估计的大脑年龄。这是量化大脑年龄预测的实际意义的重要一步。我们的研究结果有助于推进优化脑年龄预测中不同分析方法的实践。
Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs-fMRI from a large data set with 839 subjects. Our study is a follow-up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs-fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.