Importance  Clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasingly appreciated, but the underlying brain mechanisms remain unknown to date.

Objective  To examine associations between white matter organization and 2 commonly co-occurring neurodevelopmental conditions, ASD and ADHD, through both categorical and dimensional approaches.

Design, Setting, and Participants  This investigation was a cross-sectional diffusion tensor imaging (DTI) study at an outpatient academic clinical and research center, the Department of Child and Adolescent Psychiatry at New York University Langone Medical Center. Participants were children with ASD, children with ADHD, or typically developing children. Data collection was ongoing from December 2008 to October 2015.

Main Outcomes and Measures  The primary measure was voxelwise fractional anisotropy (FA) analyzed via tract-based spatial statistics. Additional voxelwise DTI metrics included radial diffusivity (RD), mean diffusivity (MD), axial diffusivity (AD), and mode of anisotropy (MA).

Results  This cross-sectional DTI study analyzed data from 174 children (age range, 6.0-12.9 years), selected from a larger sample after quality assurance to be group matched on age and sex. After quality control, the study analyzed data from 69 children with ASD (mean [SD] age, 8.9 [1.7] years; 62 male), 55 children with ADHD (mean [SD] age, 9.5 [1.5] years; 41 male), and 50 typically developing children (mean [SD] age, 9.4 [1.5] years; 38 male). Categorical analyses revealed a significant influence of ASD diagnosis on several DTI metrics (FA, MD, RD, and AD), primarily in the corpus callosum. For example, FA analyses identified a cluster of 4179 voxels (TFCE FEW corrected P < .05) in posterior portions of the corpus callosum. Dimensional analyses revealed associations between ASD severity and FA, RD, and MD in more extended portions of the corpus callosum and beyond (eg, corona radiata and inferior longitudinal fasciculus) across all individuals, regardless of diagnosis. For example, FA analyses revealed clusters overall encompassing 12121 voxels (TFCE FWE corrected P < .05) with a significant association with parent ratings in the social responsiveness scale. Similar results were evident using an independent measure of ASD traits (ie, children communication checklist, second edition). Total severity of ADHD-traits was not significantly related to DTI metrics but inattention scores were related to AD in corpus callosum in a cluster sized 716 voxels. All these findings were robust to algorithmic correction of motion artifacts with the DTIPrep software.

Conclusions and Relevance  Dimensional analyses provided a more complete picture of associations between ASD traits and inattention and indexes of white matter organization, particularly in the corpus callosum. This transdiagnostic approach can reveal dimensional relationships linking white matter structure to neurodevelopmental symptoms.

重要性  自闭症谱系障碍（ASD）与注意力缺陷/多动障碍（ADHD）之间的临床重叠越来越受到人们的重视，但迄今为止尚不清楚潜在的脑机制。

目的  通过分类和维度方法研究白质组织与2种常见的神经发育状况（ASD和ADHD）之间的关联。

设计，设置和参与者  这项研究是在纽约大学Langone医学中心的儿童和青少年精神病学系门诊学术临床和研究中心进行的横断面弥散张量成像（DTI）研究。参与者是患有ASD的儿童，患有ADHD的儿童或通常发育中的儿童。从2008年12月到2015年10月，正在进行数据收集。

主要结果和措施  主要指标是通过基于区域的空间统计数据分析的体素分数各向异性（FA）。其他体素DTI指标包括径向扩散率（RD），平均扩散率（MD），轴向扩散率（AD）和各向异性模式（MA）。

结果  这项DTI横断面研究分析了174名儿童（年龄范围6.0-12.9岁）的数据，这些儿童是在质量保证后从更大的样本中选择的，并按年龄和性别进行分组。经过质量控制后，该研究分析了69名ASD儿童（平均[SD]年龄，8.9 [1.7]岁；男性62名），55名ADHD儿童（平均[SD]年龄，9.5 [1.5]岁； 41名男性）的数据，以及50名典型的发育中儿童（平均[SD]年龄为9.4 [1.5]岁；男性为38岁）。分类分析显示，ASD诊断对几个DTI指标（FA，MD，RD和AD）（主要是在call体中）有重大影响。例如，FA分析确定了4179个体素的簇（TFCE FEW校正了P <.05）在call体的后部。维度分析揭示了在所有个体中，无论诊断如何，ASD严重程度与call体的更扩展部分以及其他区域（例如，电晕辐射和下纵筋膜）的ASD严重程度与FA，RD和MD之间的关联。例如，FA分析揭示了总共包含12121个体素的簇（TFCE FWE校正了P <.05），与社交反应量表中的父母评分有显着相关性。使用独立测量的ASD特征（即儿童沟通清单，第二版），也可以得出类似的结果。ADHD特征的总严重程度与DTI指标没有显着相关，但注意力不集中的分数与簇状体716体中体中的AD相关。所有这些发现对于使用DTIPrep软件进行运动伪影的算法校正都是可靠的。

结论和相关性  维度分析提供了更完整的ASD性状与白质组织特别是call体白质组织注意力不集中和指标之间的关联的图景。这种经诊断的方法可以揭示将白质结构与神经发育症状联系起来的尺寸关系。