Payabvash Seyedmehdi, Palacios Eva M, Owen Julia P, Wang Maxwell B, Tavassoli Teresa, Gerdes Molly, Brandes-Aitken Annie, Mukherjee Pratik, Marco Elysa J
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Front Integr Neurosci. 2019 Mar 29;13:10. doi: 10.3389/fnint.2019.00010. eCollection 2019.
Sensory over-responsivity (SOR) commonly involves auditory and/or tactile domains, and can affect children with or without additional neurodevelopmental challenges. In this study, we examined white matter microstructural and connectome correlates of auditory over-responsivity (AOR), analyzing prospectively collected data from 39 boys, aged 8-12 years. In addition to conventional diffusion tensor imaging (DTI) maps - including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD); we used DTI and high-resolution T1 scans to develop connectome Edge Density (ED) maps. The tract-based spatial statistics was used for voxel-wise comparison of diffusion and ED maps. Then, stepwise penalized logistic regression was applied to identify independent variable (s) predicting AOR, as potential imaging biomarker (s) for AOR. Finally, we compared different combinations of machine learning algorithms (i.e., naïve Bayes, random forest, and support vector machine (SVM) and tract-based DTI/connectome metrics for classification of children with AOR. In direct sensory phenotype assessment, 15 (out of 39) boys exhibited AOR (with or without neurodevelopmental concerns). Voxel-wise analysis demonstrates extensive impairment of white matter microstructural integrity in children with AOR on DTI maps - evidenced by lower FA and higher MD and RD; moreover, there was lower connectome ED in anterior-superior corona radiata, genu and body of corpus callosum. In stepwise logistic regression, the average FA of left superior longitudinal fasciculus (SLF) was the single independent variable distinguishing children with AOR ( = 0.007). Subsequently, the left SLF average FA yielded an area under the curve of 0.756 in receiver operating characteristic analysis for prediction of AOR ( = 0.008) as a region-of-interest (ROI)-based imaging biomarker. In comparative study of different combinations of machine-learning models and DTI/ED metrics, random forest algorithms using ED had higher accuracy for AOR classification. Our results demonstrate extensive white matter microstructural impairment in children with AOR, with specifically lower connectomic ED in anterior-superior tracts and associated commissural pathways. Also, average FA of left SLF can be applied as ROI-based imaging biomarker for prediction of SOR. Finally, machine-learning models can provide accurate and objective image-based classifiers for identification of children with AOR based on white matter tracts connectome ED.
感觉反应过度（SOR）通常涉及听觉和/或触觉领域，并且可能影响有或没有其他神经发育挑战的儿童。在本研究中，我们检查了听觉反应过度（AOR）的白质微观结构和连接组相关性，分析了前瞻性收集的39名8至12岁男孩的数据。除了传统的扩散张量成像（DTI）图——包括分数各向异性（FA）、平均扩散率（MD）、径向扩散率（RD）和轴向扩散率（AD）；我们使用DTI和高分辨率T1扫描来生成连接组边缘密度（ED）图。基于纤维束的空间统计学用于对扩散图和ED图进行体素级比较。然后，应用逐步惩罚逻辑回归来识别预测AOR的独立变量，作为AOR的潜在成像生物标志物。最后，我们比较了机器学习算法（即朴素贝叶斯、随机森林和支持向量机（SVM））的不同组合以及基于纤维束的DTI/连接组指标对AOR儿童的分类。在直接感觉表型评估中，39名男孩中有15名表现出AOR（有或没有神经发育问题）。体素级分析表明，DTI图上AOR儿童的白质微观结构完整性存在广泛损害——表现为较低的FA以及较高的MD和RD；此外，前上辐射冠、胼胝体膝部和体部的连接组ED较低。在逐步逻辑回归中，左上纵束（SLF）的平均FA是区分AOR儿童的单一独立变量（P = 0.007）。随后，在用于预测AOR的受试者工作特征分析中，左上纵束平均FA产生的曲线下面积为0.756（P = 0.008），作为基于感兴趣区域（ROI）的成像生物标志物。在不同机器学习模型和DTI/ED指标组合的比较研究中，使用ED的随机森林算法对AOR分类具有更高的准确性。我们的结果表明，AOR儿童存在广泛的白质微观结构损害，特别是前上纤维束和相关连合通路的连接组ED较低。此外，左上纵束的平均FA可作为基于ROI的成像生物标志物用于预测SOR。最后，机器学习模型可以提供准确客观的基于图像的分类器，用于根据白质纤维束连接组ED识别AOR儿童。
