Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved.

New method

To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task.

Results

Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification.

Comparison with existing methods

Compared with some previously published methods, our proposed method obtains the better performance for ASD identification.

Conclusion

The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.

中文翻译：

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AIMAFE：具有多图集深度特征表示和集成学习的自闭症谱系障碍识别。

背景

自闭症谱系障碍（ASD）是一种神经发育障碍，可能引起社交沟通问题。临床上，ASD的诊断主要依靠行为标准，而这种方法不够客观，可能会导致诊断延迟。由于功能磁共振成像（fMRI）可以测量大脑活动，因此它为研究脑功能障碍提供了数据，并已广泛用于ASD识别。但是，尚未达到令人满意的ASD识别精度。

新方法

为了提高ASD识别的性能，提出了一种基于多图集深度特征表示和集成学习的ASD识别方法。我们首先根据每个受试者的fMRI数据，根据不同的大脑图集计算出多种功能连接。然后，为了获得更具区分性的ASD识别特征，我们提出了一种基于堆叠降噪自动编码器（SDA）的多图集深度特征表示方法。最后，我们提出了多层感知器（MLP）和整体学习方法来执行最终的ASD识别任务。

结果

我们提出的方法通过自闭症脑成像数据交换（ABIDE）对949名受试者（包括419名ASD和530名典型对照（TC））进行了评估，达到了74.52％的准确度（灵敏度为80.69％，特异性为66.71％，AUC为0.8026） ）进行ASD识别。

与现有方法的比较

与以前发布的一些方法相比，我们提出的方法获得了更好的ASD识别性能。

结论

结果表明，我们提出的方法可以有效地提高ASD鉴定的性能，并有望用于ASD临床诊断。