摘要
目的寻找自闭症(autism spectrum disorders,ASD)的客观生物标记以辅助临床诊断。静息态功能连接(resting-state functional connectivity,RSFC)反映了大脑不同脑区神经活动模式间的时间相关性,研究者常从RSFC中探索识别ASD的生物标记,然而大部分方法还不能有效选出具有识别力的RSFC。本文采用最小绝对值收缩与选择算子(least absolute shrinkage and selection operator,Lasso)来选择ASD组和正常发育组之间具有显著性差异的RSFC特征。方法首先基于Pearson相关分析提取RSFC特征,并进行阈值化处理保留具有较大正相关值的RSFC。然后采用Lasso方法提取有识别能力的RSFC,最后利用支持向量机进行ASD分类,并主要以分类准确率指标对分类性能进行评估。结果基于Lasso方法进行特征选择后,ASD分类准确率为81.52%,同时找出了能显著区分ASD儿童与正常儿童的RSFC。结论基于Lasso特征选择的方法提高了对ASD的识别准确率,识别的生物标记有潜力应用于临床诊断。
Objective Searching for objective biomarkers of autism spectrum disorders（ ASD） can provide assisted diagnosis for doctors. Resting-state functional connectivity（ RSFC） reflects temporal correlation of the neuron activity patterns between different brain regions. Researchers often explore biomarkers for identifying ASD from RSFCs. However,most approaches cannot select discriminative RSFCs effectively. In this paper,we apply least absolute shrinkage and selection operator（ Lasso） to select most discriminative RSFCs between ASD children and typically developing（ TD） children. Methods Firstly,RSFCs were extracted as features by using Pearson correlation analysis,and were thresholded to retain RSFCs with larger positive correlation values. Then,Lasso method was adopted to select discriminative features from RSFCs. Finally,we used support vector machine to identify ASD children from TD children and mainly took classification accuracy as index to evaluate classification performance. Results The method based on Lasso achieved an accuracy of 81. 52%,and the discriminative RSFCs had significant difference between ASD and TD children. Conclusions This method improves the classification accuracy of ASD,and the biomarkers have the potential to be applied in clinical diagnosis.
出处
《北京生物医学工程》
2017年第6期564-568,596,共6页
Beijing Biomedical Engineering
关键词
自闭症
Lasso算法
静息态
功能连接
分类
autism
Lasso algorithm
restings tate
func tiona l connectivity
classification
