Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism
Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism
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DOI：
10.1016/j.cmpb.2017.11.017
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发表时间：
2018-02-01
影响因子：
6.1
通讯作者：
Zinger, Svitlana
中科院分区：
文献类型：
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作者：
Bernas, Antoine;Aldenkamp, Albert P.;Zinger, Svitlana
背景和目的：自闭症谱系障碍（ASD）的诊断需要一个漫长而复杂的过程。由于缺乏生物标志物，该程序是主观的，并限于评估行为。已经报道了几次使用功能性MRI作为辅助工具（作为分类器）的尝试，但它们几乎没有达到80%的准确性，并且通常没有被独立的数据集复制或验证。这些尝试使用了功能连接和结构测量。然而，有证据表明，不是网络的拓扑结构，而是它们的时间动态是ASD的关键特征。因此，我们提出了一种新的MRI为基础的ASD生物标志物通过分析颞叶脑动力学在静息状态fMRI。方法：我们调查静息状态fMRI数据从2个独立的数据集的青少年：我们的内部数据（12 ADS，12个控制），和鲁汶数据集（12 ASD，18个控制，从鲁汶大学）。使用独立成分分析，我们得到相关的社会执行静息状态网络（RSN）和它们相关的时间序列。在这些时间序列上，我们提取小波相干图。使用这些映射，我们计算我们的动力学度量：同相相干时间。然后，这种新的度量标准被用来训练自闭症诊断的分类器。性能评价采用留一交叉验证法。为了评估网站间的鲁棒性，我们还训练我们的分类器的内部数据，并测试他们对鲁汶dataset.Results：我们区分ASD从非ASD青少年在86.7%的准确性（91.7%的灵敏度，83.3%的特异性）。在第二个实验中，使用Leuven数据集，我们也获得了86.7%的分类性能（83.3%的灵敏度和88.9%的特异性）。最后，我们分类鲁汶数据集，与我们的内部数据训练的分类器，导致80%的准确性（100%的灵敏度，66.7%的特异性）。结论：这项研究表明，时间神经动力学的一致性的变化是ASD的生物标志物，小波相干为基础的分类器导致强大的和可复制的结果，并可用作ASD的客观诊断工具。(C)2017年，作者。出版社：Elsevier爱尔兰Ltd.
Background and Objective: The autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI.Methods: We investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset.Results: We distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity).Conclusions: This study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD. (C) 2017 The Authors. Published by Elsevier Ireland Ltd.