Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework
Diagnostic prediction of autism spectrum disorder using complex network measures in a machine learning framework
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DOI：
10.1016/j.bspc.2020.102099
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发表时间：
2020-09-01
影响因子：
5.1
通讯作者：
Deshpande, Gopikrishna
中科院分区：
文献类型：
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作者：
Chaitra, N.;Vijaya, P. A.;Deshpande, Gopikrishna
目的基于影像的精神疾病生物标记物的发现对于准确的诊断和治疗至关重要。使用机器学习框架，研究了从功能磁共振成像(FMRI)功能连接(FC)中提取的大脑功能网络拓扑(复杂网络特征)作为自闭症谱系障碍(ASD)生物标志物的实用性。为此，我们利用了来自可公开获得的432名ASD患者和556名匹配的健康对照的ABST数据集的静息状态fMRI数据。经过标准的预处理，将3D+时间fMRI数据划分为200个功能均匀的区域，并从相应的区域平均时间序列中得到基于Pearson相关的全脑FC网络。使用图论技术从FC网络计算了一组复杂的网络特征。使用递归-聚类-消除支持向量机算法比较了(I)FC、(Ii)复杂网络度量和(Iii)两者结合的三个独立特征集的预测性能。研究发现，FC可以诊断ASD的准确率为67.3%，图形测量的准确率为64.5%，而组合特征集诊断的准确率为70.1%(所有准确率均有显著差异，p<；10(-30))。最具区分性的影像特征主要来自外侧颞区、枕区、楔前(房间隔缺陷区均缩小)和眶-额区(房间隔缺陷区升高)。我们的结论是，网络拓扑(图形测量)携带了一些关于ASD病理的独特信息，这在双变量连接性(FC)中是不存在的，并且两者结合使用比单独使用测量提供了更好的预测。未来的预测研究可以在其框架内结合多种功能磁共振分析策略，以实现更好的预测性能。
Objective imaging-based biomarker discovery for psychiatric conditions is critical for accurate diagnosis and treatment. Using a machine learning framework, this work investigated the utility of brain's functional network topology (complex network features) extracted from functional magnetic resonance imaging (fMRI) functional connectivity (FC) as viable biomarker of autism spectrum disorder (ASD). To this end, we utilized resting-state fMRI data from the publicly available ABIDE dataset consisting of 432 ASD patients and 556 matched healthy controls. Upon standard pre-processing, 3D + time fMRI data were parcellated into 200 functionally homogenous regions, and whole-brain FC network using Pearson's correlation was obtained from corresponding regional mean time series. A battery of complex network features were computed from the FC network using graph theoretic techniques. Recursive-Cluster-Elimination Support Vector Machine algorithm was employed to compare the predictive performance of three independent feature sets, (i) FC, (ii) complex network measures, and (iii) both combined. The study found that FC could diagnose ASD with 67.3 % accuracy and graph measures with 64.5 % accuracy, while the combined feature set could diagnose with 70.1 % accuracy (all accuracies were significantly different, p < 10(-30)). The most discriminative imaging features were mainly from lateral temporal, occipital, precuneus (all reduced in ASD) and orbito-frontal (elevated in ASD) regions. We concluded that network topology (graph measures) carried some unique information about ASD pathology not available in bivariate connectivity (FC), and that using both together provided better prediction than using individual measures. Future prediction studies could incorporate multiple fMRI analysis strategies within their framework to achieve superior prediction performances.