PDF(Pubmed)
Abstract：
The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person\'s brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual\'s brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.
摘要：
大脑的形态在整个衰老过程中都会发生变化，利用大脑形态特征准确预测一个人的大脑年龄和性别可以帮助检测非典型的大脑模式。基于神经成像的大脑年龄估计通常用于评估个体相对于典型衰老轨迹的大脑健康，虽然从神经影像学数据中准确地分类性别可以为男性和女性之间内在的神经系统差异提供有价值的见解。在这项研究中,我们的目的是比较经典机器学习模型和量子机器学习方法变分量子电路在估计大脑年龄和基于结构磁共振成像数据预测性别方面的功效。我们使用组合和子数据集评估了六个经典机器学习模型以及量子机器学习模型。其中包括来自内部收集和公共来源的数据。参与者总数为1157人，年龄从14岁到89岁不等，性别分布为607名男性和550名女性。使用训练集和测试集在每个数据集内进行性能评估。与使用组合数据集时的经典机器学习算法相比，变分量子电路模型通常在估计大脑年龄和性别分类方面表现出优越的性能。此外,在基准子数据集中，与以前使用相同数据集进行脑年龄预测的研究相比,我们的方法表现出更好的性能.因此，我们的结果表明，变分量子算法在大脑年龄和性别预测方面都表现出与经典机器学习算法相当的有效性，潜在地提供减少的误差和提高的准确性。
