Almuqhim Fahad, Saeed Fahad
Knight Foundation School of Computing and Information Sciences (KFSCIS), Florida International University (FIU), Miami, FL, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2023 Dec;2023:2837-2843. doi: 10.1109/bibm58861.2023.10385743. Epub 2024 Jan 18.
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 , 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.
自闭症谱系障碍（ASD）是一种发生在儿童中的异质性疾病，目前的临床诊断是通过行为、认知、发育和语言指标来完成的。这些临床指标可能是不完美的测量方法，因为它们存在较高的重测变异性，并且受到环境、社会结构或共病等评估因素的影响。神经影像学与机器学习的进展为开发比现有临床技术更具可量化性和可靠性的方法提供了机会。在本文中，我们设计并开发了一种深度学习模型，该模型基于功能磁共振成像（fMRI）数据运行，能够对自闭症谱系障碍大脑和神经典型大脑进行分类。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格拉姆角场（GAF），同时锁定数据中的时间和空间模式。我们的动机是设计并开发一个新颖的框架，该框架可以将从fMRI数据中获取的时间序列编码为图像，以供在计算机视觉中取得成功的深度学习架构使用。在我们提出的名为 的框架中，我们使用卷积神经网络（CNN）从GAF图像中提取有用特征。然后，我们使用长短期记忆（LSTM）层来学习各区域之间的活动。最后，将最后一个LSTM层的输出表示应用于单层感知器（SPL）以获得最终分类。我们广泛的实验表明，该模型在4个中心都具有很高的准确率，并且在两个中心上优于现有最先进的模型，与现有技术相比，准确率分别提高了17.58%和6.7%。我们的模型实现了81.78%的最高准确率，同时具有高度的敏感性和特异性。所有的训练、验证和测试都是使用公开可用的ABIDE-I基准数据集完成的。
