Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction, and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this paper, we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines. Specifically, we consider six brain parcellation definitions, five methods for functional connectivity matrix construction, six feature extraction/selection approaches, and nine classifier building algorithms. We report the prediction performance sensitivity to each of these choices, as well as the best results that are comparable with the state of the art.

中文翻译：

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机器学习管道选择对功能连接数据预测自闭症的影响

自闭症谱系障碍 (ASD) 是一种非常普遍的神经发育疾病，对家庭的整个生活产生巨大的社会和经济影响。对可以尽早评估的生物标志物进行了激烈的搜索，以便开始治疗和准备家庭以应对疾病带来的挑战。脑成像生物标志物具有特殊的意义。具体来说，从静息状态功能磁共振成像 (rs-fMRI) 中提取的功能连接数据应该允许检测大脑连接改变。机器学习管道包括从大脑分区估计功能连接矩阵、特征提取和构建用于 ASD 预测的分类模型。从计算和方法论的角度来看，文献中报道的工作非常多样化。在本文中，我们对构建这些机器学习管道时所涉及的选择的影响进行了全面的计算探索。具体来说，我们考虑了六种大脑分割定义、五种功能连接矩阵构建方法、六种特征提取/选择方法和九种分类器构建算法。我们报告了对这些选择中的每一个的预测性能敏感性，以及与现有技术相当的最佳结果。我们考虑了六种大脑分割定义、五种功能连接矩阵构建方法、六种特征提取/选择方法和九种分类器构建算法。我们报告了对这些选择中的每一个的预测性能敏感性，以及与现有技术相当的最佳结果。我们考虑了六种大脑分割定义、五种功能连接矩阵构建方法、六种特征提取/选择方法和九种分类器构建算法。我们报告了对这些选择中的每一个的预测性能敏感性，以及与现有技术相当的最佳结果。