Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University Baltimore, MD, USA.
Front Syst Neurosci. 2012 Aug 30;6:61. doi: 10.3389/fnsys.2012.00061. eCollection 2012.
Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.
成功地使用影像学和功能生物标志物对注意力缺陷多动障碍（ADHD）进行自动化诊断，将对该疾病的公共卫生影响产生根本影响。在这项工作中，我们展示了使用影像学生物标志物预测 ADHD 的结果，并讨论了研究的科学和诊断意义。我们使用以静息态功能连接（rs-fc）和结构脑成像为重点的标志性 ADHD 200 数据集创建了一个预测模型。我们使用影像学数据、智商和其他协变量预测了通过行为检查获得的 ADHD 状态和亚型。本文稿的新颖贡献包括对这种形式的数据的预测和图像特征提取方法进行了彻底的探索，包括使用奇异值分解（SVD）、CUR 分解、随机森林、梯度提升、袋装、体素形态计量学和支持向量机以及对疾病影像学生物标志物的价值和潜在缺乏的重要见解。关键结果包括基于 CUR 的 rs-fc-fMRI 分解，以及基于运动网络分割和随机森林算法的梯度提升和预测算法。我们推测，CUR 分解在很大程度上诊断了头部运动的常见人群方向。值得注意的是，这项研究的一个副产品是一种潜在的自动检测细微扫描内运动的方法。最终的预测算法是几种算法的加权组合，其外部测试集特异性为 94%，敏感性为 21%。最有前途的影像学生物标志物是来自运动网络分割的相关图。总之，我们对独特的 ADHD 200 数据集进行了大规模的统计探索性预测练习。该练习为未来对 ADHD 的神经基础进行科学探索提供了几个潜在的线索。
