Computational Intelligence Group, University of the Basque Country (UPV/EHU), San Sebastian, Spain.
Universidad Mayor de San Andres, La Paz, Bolivia.
Int J Neural Syst. 2021 Apr;31(4):2150009. doi: 10.1142/S012906572150009X. Epub 2021 Jan 20.
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.
自闭症谱系障碍 (ASD) 是一种广泛存在的神经发育障碍，对家庭的整个生命周期都有重大的社会和经济影响。人们正在积极寻找生物标志物，以便能够尽早进行评估，从而启动治疗，并让家庭为应对该病症带来的挑战做好准备。脑成像生物标志物具有特殊的研究意义。具体来说，从静息态功能磁共振成像(rs-fMRI)中提取的功能连接数据，应该可以检测到大脑连接的改变。机器学习管道涵盖了从大脑分割中估计功能连接矩阵、特征提取以及构建用于 ASD 预测的分类模型。从计算和方法学的角度来看，文献中的研究工作存在很大的异质性。在本文中，我们对构建这些机器学习管道时所涉及的选择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/选择方法以及九种分类器构建算法。我们报告了这些选择对预测性能的敏感性，以及与最先进技术相媲美的最佳结果。
