PDF(Pubmed)
Abstract：
Autism Spectrum Disorder (ASD) is a heterogeneous disorder in children, and the current clinical diagnosis is accomplished using behavioral, cognitive, developmental, and language metrics. These clinical metrics can be imperfect measures as they are subject to high test-retest variability, and are influenced by assessment factors such as environment, social structure, or comorbid disorders. Advances in neuroimaging coupled with machine-learning provides an opportunity to develop methods that are more quantifiable, and reliable than existing clinical techniques. In this paper, we design and develop a deep-learning model that operates on functional magnetic resonance imaging (fMRI) data, and can classify between ASD and neurotypical brains. We introduce a novel strategy to transform time-series data extracted from fMRI signals into Gramian Angular Field (GAF) while locking in the temporal and spatial patterns in the data. Our motivation is to design and develop a novel framework that could encode the time-series, acquired from fMRI data, into images that can be used by deep-learning architectures that have been successful in computer vision. In our proposed framework called ASD-GResTM, we used a Convolutional Neural Network (CNN) to extract useful features from GAF images. We then used a Long Short-Term Memory (LSTM) layer to learn the activities between the regions. Finally, the output representations of the last LSTM layer are applied to a single-layer perceptron (SPL) to get the final classification. Our extensive experimentation demonstrates high accuracy across 4 centers, and outperforms state-of-the-art models on two centers with an increase in the accuracy of 17.58% and 6.7%, respectively as compared to the state of the art. Our model achieved the maximum accuracy of 81.78% with high degree of sensitivity and specificity. All training, validation, and testing was accomplished using openly available ABIDE-I benchmarking dataset.
摘要：
自闭症谱系障碍（ASD）是儿童的异质性障碍，目前的临床诊断是通过行为学来完成的，认知，发展,和语言指标。这些临床指标可能是不完美的衡量标准，因为它们具有很高的重测变异性，并受环境等评估因素的影响，社会结构,或合并症。神经成像与机器学习相结合的进步为开发更可量化的方法提供了机会，比现有的临床技术可靠。在本文中,我们设计和开发了一个深度学习模型，该模型对功能磁共振成像（fMRI）数据进行操作，并且可以在ASD和神经典型大脑之间进行分类。我们引入了一种新颖的策略，将从fMRI信号中提取的时间序列数据转换为Gramian角场（GAF），同时锁定数据中的时间和空间模式。我们的动机是设计和开发一个可以编码时间序列的新框架，从功能磁共振成像数据中获得，转换为可由在计算机视觉中成功的深度学习架构使用的图像。在我们提出的名为ASD-GResTM的框架中，我们使用卷积神经网络（CNN）从GAF图像中提取有用的特征。然后，我们使用长短期记忆（LSTM）层来学习区域之间的活动。最后,最后一个LSTM层的输出表示应用于单层感知器（SPL）以获得最终分类。我们广泛的实验证明了4个中心的高精度，并在两个中心上优于最先进的模型，精度分别提高了17.58%和6.7%，分别与现有技术相比。我们的模型达到了81.78％的最大准确度，具有高度的灵敏度和特异性。所有的训练,验证,并且测试是使用公开可用的ABIDE-I基准测试数据集完成的。
