Li Xiaoxiao, Dvornek Nicha C, Papademetris Xenophon, Zhuang Juntang, Staib Lawrence H, Ventola Pamela, Duncan James S
Biomedical Engineering, Yale University, New Haven, CT USA.
Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT USA.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1252-1255. doi: 10.1109/isbi.2018.8363798. Epub 2018 May 24.
In this paper, we propose a new whole brain fMRI-analysis scheme to identify autism spectrum disorder (ASD) and explore biological markers in ASD classification. To utilize both spatial and temporal information in fMRI, our method investigates the potential benefits of using a sliding window over time to measure temporal statistics (mean and standard deviation) and using 3D convolutional neural networks (CNNs) to capture spatial features. The sliding window created 2-channel images, which were used as inputs to the 3D CNN. From the outputs of the 3D CNN convolutional layers, ASD related fMRI spatial features were directly deciphered. Input formats and sliding window parameters were investigated in our study. The power of aligning 2-channel images was shown in our proposed method. Compared with traditional machine learning classification models, our proposed 2CC3D method increased mean F-scores over 8.5%.
在本文中，我们提出了一种全新的全脑功能磁共振成像（fMRI）分析方案，用于识别自闭症谱系障碍（ASD）并探索ASD分类中的生物学标志物。为了利用fMRI中的空间和时间信息，我们的方法研究了随时间使用滑动窗口来测量时间统计量（均值和标准差）以及使用三维卷积神经网络（3D CNN）来捕捉空间特征的潜在益处。滑动窗口创建了双通道图像，这些图像被用作3D CNN的输入。从3D CNN卷积层的输出中，直接解读出与ASD相关的fMRI空间特征。我们的研究对输入格式和滑动窗口参数进行了研究。我们提出的方法展示了对齐双通道图像的功效。与传统机器学习分类模型相比，我们提出的2CC3D方法将平均F分数提高了8.5%以上。
