Dvornek Nicha C, Ventola Pamela, Duncan James S
Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT.
Child Study Center, Yale School of Medicine, New Haven, CT.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:725-728. doi: 10.1109/ISBI.2018.8363676. Epub 2018 May 24.
Accurate identification of autism spectrum disorder (ASD) from resting-state functional magnetic resonance imaging (rsfMRI) is a challenging task due in large part to the heterogeneity of ASD. Recent work has shown better classification accuracy using a recurrent neural network with rsfMRI time-series as inputs. However, phenotypic features, which are often available and likely carry predictive information, are excluded from the model, and combining such data with rsfMRI into the recurrent neural network is not a straightforward task. In this paper, we present several methodologies for incorporating phenotypic data with rsfMRI into a single deep learning framework for classifying ASD. We test the proposed architectures using a cross-validation framework on the large, heterogeneous first cohort from the Autism Brain Imaging Data Exchange. Our best model achieved an accuracy of 70.1%, outperforming prior work.
从静息态功能磁共振成像（rsfMRI）准确识别自闭症谱系障碍（ASD）是一项具有挑战性的任务，这在很大程度上归因于ASD的异质性。最近的研究表明，使用以rsfMRI时间序列作为输入的递归神经网络可以获得更好的分类准确率。然而，模型中排除了通常可用且可能携带预测信息的表型特征，并且将此类数据与rsfMRI结合到递归神经网络中并非易事。在本文中，我们提出了几种方法，将表型数据与rsfMRI纳入一个用于ASD分类的单一深度学习框架。我们使用交叉验证框架在来自自闭症脑成像数据交换的大型异质第一队列上测试了所提出的架构。我们的最佳模型实现了70.1%的准确率，优于先前的工作。
