Functional brain network identification and fMRI augmentation using a VAE-GAN framework
Functional brain network identification and fMRI augmentation using a VAE-GAN framework
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DOI：
10.1016/j.compbiomed.2023.107395
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发表时间：
2023-09
影响因子：
7.7
通讯作者：
Ning Qiang;Jie Gao;Qinglin Dong;Huiji Yue;Hongtao Liang;Lili Liu;Jingjing Yu;Jing Hu;
中科院分区：
文献类型：
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作者：
Ning Qiang;Jie Gao;Qinglin Dong;Huiji Yue;Hongtao Liang;Lili Liu;Jingjing Yu;Jing Hu;
最近，与传统方法相比，深度学习模型在从功能性磁共振成像（fMRI）数据映射功能性脑网络方面取得了上级性能。然而，由于缺乏足够的数据和大脑体积的高维性，fMRI的深度学习模型往往会出现过拟合。此外，现有的方法很少研究fMRI数据增强及其应用。为了解决这些问题，我们开发了一个VAE-GAN框架，该框架将VAE（变分自动编码器）与GAN（生成对抗网络）相结合，用于功能性脑网络识别和fMRI增强。作为一个生成模型，VAE-GAN模型的分布fMRI，使它能够提取更广义的功能，从而缓解过拟合问题。VAE-GAN比标准GAN更容易在fMRI上训练，因为它使用VAE的潜变量来生成假数据，而不是依赖于GAN中使用的随机噪声，并且它可以生成比VAE更高质量的假数据，因为VAE可以促进生成器的训练。换句话说，VAE-GAN继承了VAE和GAN的优点，并避免了它们在fMRI数据建模方面的局限性。在HCP任务fMRI数据集上的大量实验证明了所提出的VAE-GAN框架在识别时间特征和功能脑网络方面的有效性和优越性，并且假数据的质量高于VAE和GAN。注意缺陷多动障碍（ADHD）-200数据集的静息状态fMRI结果进一步证明，VAE-GAN生成的假数据有助于提高脑网络建模和ADHD分类的性能。
Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application. To address these issues, we developed a VAE-GAN framework that combined a VAE (variational auto-encoder) with a GAN (generative adversarial net) for functional brain network identification and fMRI augmentation. As a generative model, the VAE-GAN models the distribution of fMRI so that it enables the extraction of more generalized features, and thus relieve the overfitting issue. The VAE-GAN is easier to train on fMRI than a standard GAN since it uses latent variables from VAE to generate fake data rather than relying on random noise that is used in a GAN, and it can generate higher quality of fake data than VAE since the discriminator can promote the training of the generator. In other words, the VAE-GAN inherits the advantages of VAE and GAN and avoids their limitations in modeling of fMRI data. Extensive experiments on task fMRI datasets from HCP have proved the effectiveness and superiority of the proposed VAE-GAN framework for identifying both temporal features and functional brain networks compared with existing models, and the quality of fake data is higher than those from VAE and GAN. The results on resting state fMRI of Attention Deficit Hyperactivity Disorder (ADHD)-200 dataset further demonstrated that the fake data generated by the VAE-GAN can help improve the performance of brain network modeling and ADHD classification.