ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements.
ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements.
复制标题
DOI：
10.3389/fnsys.2012.00069
复制
发表时间：
2012
影响因子：
3
通讯作者：
Dursun SM
中科院分区：
文献类型：
--
作者：
Brown MR;Sidhu GS;Greiner R;Asgarian N;Bastani M;Silverstone PH;Greenshaw AJ;Dursun SM
基于神经成像的诊断可能有助于临床医生做出更准确的诊断，从而获得更快、更有效的治疗。我们参加了2011年ADHD-200全球竞赛，其中涉及分析973名参与者的大型数据集，包括注意缺陷多动障碍（ADHD）患者和健康对照。每个参与者的数据包括静息状态功能磁共振成像（fMRI）扫描以及个人特征和诊断数据。我们的目标是学习一个机器学习分类器，该分类器使用参与者的静息状态fMRI扫描来诊断（分类）该个体为三种类型之一：健康对照，ADHD合并（ADHD-C）类型或ADHD疏忽（ADHD-I）类型。我们使用参与者的个人特征数据（数据收集地点，年龄，性别，惯用手，操作智商，言语智商和全量表智商），没有任何fMRI数据，作为逻辑分类器的输入，以生成诊断预测。令人惊讶的是，这种方法实现了最高的诊断准确率（62.52%）以及参加比赛的21支球队中的最高得分（124/195）。这些结果表明，在影像诊断研究中，考虑年龄、性别和其他个人特征差异的重要性。我们讨论了这些结果的进一步影响，以fMRI为基础的诊断以及fMRI为基础的临床研究。我们还记录了我们的测试与各种基于成像的诊断方法，没有一个执行以及逻辑分类器仅使用个人特征数据。
Neuroimaging-based diagnostics could potentially assist clinicians to make more accurate diagnoses resulting in faster, more effective treatment. We participated in the 2011 ADHD-200 Global Competition which involved analyzing a large dataset of 973 participants including Attention deficit hyperactivity disorder (ADHD) patients and healthy controls. Each participant's data included a resting state functional magnetic resonance imaging (fMRI) scan as well as personal characteristic and diagnostic data. The goal was to learn a machine learning classifier that used a participant's resting state fMRI scan to diagnose (classify) that individual into one of three categories: healthy control, ADHD combined (ADHD-C) type, or ADHD inattentive (ADHD-I) type. We used participants' personal characteristic data (site of data collection, age, gender, handedness, performance IQ, verbal IQ, and full scale IQ), without any fMRI data, as input to a logistic classifier to generate diagnostic predictions. Surprisingly, this approach achieved the highest diagnostic accuracy (62.52%) as well as the highest score (124 of 195) of any of the 21 teams participating in the competition. These results demonstrate the importance of accounting for differences in age, gender, and other personal characteristics in imaging diagnostics research. We discuss further implications of these results for fMRI-based diagnosis as well as fMRI-based clinical research. We also document our tests with a variety of imaging-based diagnostic methods, none of which performed as well as the logistic classifier using only personal characteristic data.