Identification of autism spectrum disorder using deep learning and the ABIDE dataset
Identification of autism spectrum disorder using deep learning and the ABIDE dataset
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DOI：
10.1016/j.nicl.2017.08.017
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发表时间：
2018-01-01
影响因子：
4.2
通讯作者：
Meneguzzi, Felipe
中科院分区：
文献类型：
--
作者：
Heinsfeld, Anibal Solon;Franco, Alexandre Rosa;Meneguzzi, Felipe
本研究的目标是应用深度学习算法从大型脑成像数据集中识别自闭症谱系障碍（ASD）患者，仅基于患者的大脑激活模式。我们调查了来自世界范围内多站点数据库ABIDE（自闭症脑成像数据交换）的ASD患者脑成像数据。ASD是一种以大脑为基础的疾病，其特征是社交缺陷和重复行为。根据疾病控制中心最近的数据，ASD影响美国68名儿童中的一名。我们研究了从功能性脑成像数据中客观识别ASD参与者的功能连接模式，并试图揭示分类中出现的神经模式。结果通过在数据集中识别ASD与对照患者的准确性达到70%来改进最新技术水平。从分类中出现的模式显示了大脑前部和后部区域之间的大脑功能的关系;这种关系证实了ASD中大脑连接前后中断的现有经验证据。我们展示了结果，并根据我们的深度学习模型确定了最有助于区分ASD与典型发展对照的大脑区域。
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anteriorposterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.