Lin Huang, Haider Stefan P, Kaltenhauser Simone, Mozayan Ali, Malhotra Ajay, Constable R Todd, Scheinost Dustin, Ment Laura R, Konrad Kerstin, Payabvash Seyedmehdi
Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.
Child Neuropsychology Section, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital RWTH Aachen, Aachen, Germany.
Front Neurosci. 2023 Feb 22;17:1138670. doi: 10.3389/fnins.2023.1138670. eCollection 2023.
Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.
From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.
Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.
Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.
利用一个大规模的人群水平的形态学、微观结构和功能性神经影像数据集，我们旨在阐明儿童注意力缺陷多动障碍（ADHD）潜在的神经生物学机制。此外，我们评估了基于影像和临床信息的机器学习分类器预测ADHD诊断的适用性。
从青少年行为认知发展（ABCD）数据库中，我们纳入了1798名被诊断为ADHD的儿童和6007名未患ADHD的儿童。在根据年龄和性别进行调整的多变量逻辑回归分析中，我们研究了ADHD与不同神经影像指标之间的关联。神经影像指标包括白质（WM）束的分数各向异性（FA）、神经突密度（ND）、平均扩散率（MD）、径向扩散率（RD）和轴向扩散率（AD），T1-MPRAGE序列的皮质区域厚度和表面积，以及静息态功能磁共振成像的功能网络连接相关性。
患有ADHD的儿童表现出白质微观结构完整性普遍降低的标志物，神经密度和纤维束体积减少——在额叶和顶叶最为明显。此外，ADHD诊断与皮质体积和表面积减少有关，尤其是在颞叶和额叶区域。在功能磁共振成像研究中，患有ADHD的儿童在默认模式网络与中央和背侧注意力网络之间的连接性降低，这些网络与注意力集中功能有关。使用多模态影像指标和临床变量的组合，在独立验证中，特征选择和机器学习分类器的最佳组合能够达到曲线下面积为0.613（95%置信区间 = 0.580 - 0.645）来预测ADHD诊断。
我们的研究强调了额叶皮质和相关白质束在儿童ADHD发病机制中的神经生物学意义。我们还展示了基于多模态神经影像指标的机器学习模型在一般人群队列中辅助ADHD诊断的潜在可能性和局限性。
