基于混合3DCNN和LSTM的4D时空fMRI数据模型：一项ABIDE自闭症分类研究
A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study
摘要：
功能性磁共振成像（fMRI）捕捉神经活动的时间动态作为大脑中空间位置的函数。因此，fMRI扫描被表示为4维（3-空间+1-时间）张量。人们普遍认为，功能磁共振成像的时空模式表现为行为和临床症状。由于功能磁共振成像数据的高维性（$\sim$ 1 Million），以及数据集基数有限的限制，提取这种模式是具有挑战性的。克服这些障碍的标准方法是通过总结随时间或空间的激活来降低数据的维度，代价是可能丢失有用信息。在这里，我们介绍了一种端到端算法，该算法能够使用3-D CNN和3-D卷积LSTM从完整的4-D数据中提取时空特征。我们评估我们提出的模型上公开可用的ABIDE数据集，以证明我们的模型的能力，自闭症谱系障碍（ASD）从静息状态fMRI数据进行分类。我们的研究结果表明，所提出的模型实现了最先进的结果与F1得分为0.78和0.7的纽约大学和UM网站，分别在单个网站。
英文摘要：
Functional Magnetic Resonance Imaging (fMRI) captures the temporal dynamics of neural activity as a function of spatial location in the brain. Thus, fMRI scans are represented as 4-Dimensional (3-space + 1-time) tensors. And it is widely believed that the spatio-temporal patterns in fMRI manifests as behaviour and clinical symptoms. Because of the high dimensionality ($\sim$ 1 Million) of fMRI, and the added constraints of limited cardinality of data sets, extracting such patterns are challenging. A standard approach to overcome these hurdles is to reduce the dimensionality of the data by either summarizing activation over time or space at the expense of possible loss of useful information. Here, we introduce an end-to-end algorithm capable of extracting spatiotemporal features from the full 4-D data using 3-D CNNs and 3-D Convolutional LSTMs. We evaluate our proposed model on the publicly available ABIDE dataset to demonstrate the capability of our model to classify Autism Spectrum Disorder (ASD) from resting-state fMRI data. Our results show that the proposed model achieves state of the art results on single sites with F1-scores of 0.78 and 0.7 on NYU and UM sites, respectively.
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