Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei City, Taiwan; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan.
Asian J Psychiatr. 2024 Jul;97:104087. doi: 10.1016/j.ajp.2024.104087. Epub 2024 May 20.
We aimed to identify important features of white matter microstructures collectively distinguishing individuals with attention-deficit/hyperactivity disorder (ADHD) from those without ADHD using a machine-learning approach.
Fifty-one ADHD patients and 60 typically developing controls (TDC) underwent diffusion spectrum imaging at two time points. We evaluated three models to classify ADHD and TDC using various machine-learning algorithms. Model 1 employed baseline white matter features of 45 white matter tracts at Time 1; Model 2 incorporated features from both time points; and Model 3 (main analysis) further included the relative rate of change per year of white matter tracts.
The random forest algorithm demonstrated the best performance for classification. Model 1 achieved an area-under-the-curve (AUC) of 0.67. Model 3, incorporating Time 2 variables and relative rate of change per year, improved the performance (AUC = 0.73). In addition to identifying several white matter features at two time points, we found that the relative rate of change per year in the superior longitudinal fasciculus, frontal aslant tract, stria terminalis, inferior fronto-occipital fasciculus, thalamic and striatal tracts, and other tracts involving sensorimotor regions are important features of ADHD. A higher relative change rate in certain tracts was associated with greater improvement in visual attention, spatial short-term memory, and spatial working memory.
Our findings support the significant diagnostic value of white matter microstructure and the developmental change rates of specific tracts, reflecting deviations from typical development trajectories, in identifying ADHD.
我们旨在使用机器学习方法，确定能够将注意力缺陷多动障碍（ADHD）患者与非 ADHD 患者区分开来的白质微观结构的重要特征。
51 名 ADHD 患者和 60 名正常发育对照者（TDC）在两个时间点接受了弥散张量成像。我们评估了三种模型，使用各种机器学习算法对 ADHD 和 TDC 进行分类。模型 1 使用时间 1 的 45 条白质束的基线白质特征；模型 2 结合了两个时间点的特征；模型 3（主要分析）进一步纳入了白质束每年的相对变化率。
随机森林算法在分类方面表现最好。模型 1 的曲线下面积（AUC）为 0.67。模型 3 纳入了时间 2 的变量和每年的相对变化率，提高了性能（AUC=0.73）。除了在两个时间点识别出几个白质特征外，我们还发现上纵束、额斜束、终纹、下额枕束、丘脑和纹状体束以及其他涉及感觉运动区域的白质束每年的相对变化率是 ADHD 的重要特征。某些束的相对变化率较高与视觉注意力、空间短期记忆和空间工作记忆的改善程度较高有关。
我们的发现支持白质微观结构和特定束的发育变化率在识别 ADHD 方面具有重要的诊断价值，反映了偏离典型发育轨迹的情况。
