Journal of Neuroscience Methods ( IF 3 ) Pub Date : 2020-07-27 , DOI: 10.1016/j.jneumeth.2020.108884 Jac Fredo Agastinose Ronicko 1 , John Thomas 1 , Prasanth Thangavel 1 , Vineetha Koneru 1 , Georg Langs 2 , Justin Dauwels 1
Background
Autism Spectrum Disorder (ASD) is a neurodevelopmental disability with altered connectivity in brain networks.
New method
In this study, brain connections in Resting-state functional Magnetic Resonance Imaging (Rs-fMRI) of ASD and Typical Developing (TD) are analyzed by partial and full correlation methods such as Gaussian Graphical Least Absolute Shrinkage and Selection Operator (GLASSO), Max-Det Matrix Completion (MDMC), and Pearson Correlation Co-Efficient (PCCE). We investigated Functional Connectivity (FC) of ASD and TD brain from 238 functionally defined regions of interest. Furthermore, we constructed a series of feature sets by applying conditional random forests and conditional permutation importance. We built classifier models by Random Forest (RF), Oblique RF (ORF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) for each feature set. FC features are ranked based on p-value and we analyzed the top 20 FC features.
Results
We achieved a single-trial test accuracy of 72.5 %, though MDMC-SVM and PCCE-CNN pipelines. Further, PCCE-CNN pipeline gives better average test accuracy (70.31 %) and area under the curve (0.73) compared to other pipelines. We found that top-20 PCCE based FC features are from networks such as Dorsal Attention (DA), Cingulo-Opercular Task Control (COTC), somatosensory motor hand and subcortical. In addition, among top 20 PCCE features, many FC links are found between COTC and DA (4 connections) which helped to discriminate the ASD and TD.
Comparison with existing methods and Conclusions
The generalized classifier models built in our study for highly heterogeneous participants perform better than previous studies with similar data sets and diagnostic groups.
中文翻译:
与部分相关性相比,使用完全相关性功能性大脑连通性可以改善使用静止状态fMRI数据对自闭症的诊断分类。
背景
自闭症谱系障碍(ASD)是一种神经发育障碍,其大脑网络的连接性发生了变化。
新方法
在这项研究中,通过部分和全部相关方法(例如高斯图形最小绝对收缩和选择算子(GLASSO),Max)对ASD和典型发育(TD)的静息态功能磁共振成像(Rs-fMRI)的大脑连接进行了分析。 -Det矩阵补全(MDMC)和Pearson相关系数(PCCE)。我们研究了来自238个功能定义的目标区域的ASD和TD脑的功能连接性(FC)。此外,我们通过应用条件随机森林和条件置换重要性构造了一系列特征集。我们通过随机森林(RF),斜向RF(ORF),支持向量机(SVM)和卷积神经网络(CNN)为每个功能集建立了分类器模型。FC功能基于p排名-value,我们分析了前20个FC功能。
结果
通过MDMC-SVM和PCCE-CNN管道,我们实现了72.5%的单次测试精度。此外,与其他管道相比,PCCE-CNN管道具有更好的平均测试精度(70.31%)和曲线下面积(0.73)。我们发现基于PCCE的前20个FC功能来自诸如背心(DA),舌-眼任务控制(COTC),体感运动手和皮层下层等网络。此外,在PCCE的前20个功能中,在COTC和DA之间发现了许多FC链接(4个连接),这有助于区分ASD和TD。
与现有方法和结论的比较
在我们的研究中为高度异质性参与者建立的广义分类器模型比以前的具有相似数据集和诊断组的研究要好。