Gao Ming-Shan, Tsai Fu-Sheng, Lee Chi-Chun
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5472-5475. doi: 10.1109/EMBC44109.2020.9175789.
Automated diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) from brain's functional imaging has gained more interest due to its high prevalence rates among children. While phenotypic information, such as age and gender, is known to be important in diagnosing ADHD and critically affects the representation derived from fMRI brain images, limited studies have integrated phenotypic information when learning discriminative embedding from brain imaging for such an automatic classification task. In this work, we propose to integrate age and gender attributes through attention mechanism that is jointly optimized when learning a brain connectivity embedding using convolutional variational autoencoder derived from resting state functional magnetic resonance imaging (rs-fMRI) data. Our proposed framework achieves a state-of-the-art average of 86.22% accuracy in ADHD vs. typical develop control (TDC) binary classification task evaluated across five public ADHD-200 competition datasets. Furthermore, our analysis points out that there are insufficient linked connections to the brain region of precuneus in the ADHD group.
由于注意力缺陷多动障碍（ADHD）在儿童中发病率很高，基于大脑功能成像的ADHD自动诊断受到了更多关注。虽然已知年龄和性别等表型信息在ADHD诊断中很重要，并且会严重影响从功能磁共振成像（fMRI）脑图像中得出的表征，但在为这种自动分类任务从脑成像中学习判别性嵌入时，整合表型信息的研究却很有限。在这项工作中，我们建议通过注意力机制整合年龄和性别属性，在使用源自静息态功能磁共振成像（rs-fMRI）数据的卷积变分自编码器学习脑连接嵌入时，对注意力机制进行联合优化。在五个公开的ADHD-200竞赛数据集上评估的ADHD与典型发育对照（TDC）二元分类任务中，我们提出的框架达到了86.22%的准确率这一领先水平。此外，我们的分析指出，ADHD组中与楔前叶脑区的连接不足。
