Zhuang Juntang, Dvornek Nicha C, Zhao Qingyu, Li Xiaoxiao, Ventola Pamela, Duncan James S
Biomedical Engineering, Yale University, New Haven, CT, USA.
Child Study Center, Yale University, New Haven, CT, USA.
Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:404-408. doi: 10.1109/ISBI.2019.8759156. Epub 2019 Jul 11.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is limited progress in understanding the effect of each type of treatment. In this project, we aim to detect structural changes in the brain after treatment and select structural features associated with treatment outcomes. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis problems are the challenges in this work. To select predictive features and build accurate models, we use the sure independence screening (SIS) method. SIS is a theoretically and empirically validated method for ultra-high dimensional general linear models, and it achieves both predictive accuracy and correct feature selection by iterative feature selection. Compared with step-wise feature selection methods, SIS removes multiple features in each iteration and is computationally efficient. Compared with other linear models such as elastic-net regression, support vector regression (SVR) and partial least squares regression (PSLR), SIS achieves higher accuracy. We validated the superior performance of SIS in various experiments: First, we extract brain structural features from FreeSurfer, including cortical thickness, surface area, mean curvature and cortical volume. Next, we predict different measures of treatment outcomes based on structural features. We show that SIS achieves the highest correlation between prediction and measurements in all tasks. Furthermore, we report regions selected by SIS as biomarkers for ASD.
自闭症谱系障碍（ASD）是一种复杂的神经发育障碍，行为治疗干预已显示出对患有ASD的幼儿有一定效果。然而，在理解每种治疗方法的效果方面进展有限。在本项目中，我们旨在检测治疗后大脑的结构变化，并选择与治疗结果相关的结构特征。建立接受特定治疗患者的大型数据库存在困难，以及医学图像分析问题的高维度性是这项工作面临的挑战。为了选择预测特征并建立准确的模型，我们使用了确定性独立筛选（SIS）方法。SIS是一种在理论和实证上都经过验证的用于超高维广义线性模型的方法，它通过迭代特征选择实现了预测准确性和正确的特征选择。与逐步特征选择方法相比，SIS在每次迭代中会去除多个特征，并且计算效率高。与其他线性模型如弹性网络回归、支持向量回归（SVR）和偏最小二乘回归（PSLR）相比，SIS具有更高的准确性。我们在各种实验中验证了SIS的优越性能：首先，我们从FreeSurfer中提取大脑结构特征，包括皮质厚度、表面积、平均曲率和皮质体积。接下来，我们基于结构特征预测治疗结果的不同指标。我们表明，在所有任务中，SIS在预测与测量之间实现了最高的相关性。此外，我们报告了SIS选择的区域作为ASD的生物标志物。
