Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder
Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder
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DOI：
10.1001/jamapsychiatry.2017.2573
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发表时间：
2017-11-01
期刊：
影响因子：
25.8
通讯作者：
Di Martino, Adriana
中科院分区：
文献类型：
--
作者：
Aoki, Yuta;Yoncheva, Yuliya N.;Di Martino, Adriana
自闭症谱系障碍（ASD）和注意力缺陷/多动障碍（ADHD）的临床重叠越来越受到重视，但其潜在的大脑机制迄今仍不清楚。目的通过分类和维度研究白质组织与两种常见的神经发育疾病ASD和ADHD之间的关系。设计、环境和参与者本研究是在纽约大学朗格尼医学中心儿童和青少年精神病学门诊学术临床和研究中心进行的一项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，或正常发育的儿童。数据收集于2008年12月至2015年10月进行。主要结果和测量方法主要测量方法是通过基于文本的空间统计分析体向分数各向异性（FA）。其他体向DTI指标包括径向扩散系数（RD）、平均扩散系数（MD）、轴向扩散系数（AD）和各向异性模式（MA）。结果：本横断面DTI研究分析了174名儿童（年龄范围为6.0-12.9岁）的数据，这些数据是在质量保证后从更大的样本中挑选出来的，在年龄和性别上进行了分组匹配。经过质量控制，本研究分析了69例ASD患儿（平均[SD]年龄8.9[1.7]岁，男性62例）、55例ADHD患儿（平均[SD]年龄9.5[1.5]岁，男性41例）和50例发育正常儿童（平均[SD]年龄9.4[1.5]岁，男性38例）的数据。分类分析显示，ASD诊断对几个DTI指标（FA、MD、RD和AD）有显著影响，主要是在胼胝体中。例如，FA分析发现胼胝体后部有4179个体素（TFCE FEW校正P < 0.05）。维度分析显示，在所有个体中，无论诊断如何，ASD严重程度与胼胝体及胼胝体以外的更广泛部分（如辐射冠和下纵束）的FA、RD和MD之间存在关联。例如，FA分析显示集群总体包含12121体素（TFCE FWE校正P < 0.05），与父母在社会反应量表中的评分显著相关。使用ASD特征的独立测量（即，儿童沟通检查表，第二版），类似的结果也很明显。adhd特征的总严重程度与DTI指标无显著相关性，但在716体素的聚类中，注意力不集中得分与胼胝体中的AD相关。所有这些发现都对DTIPrep软件的运动伪影算法校正具有鲁棒性。结论和相关性维度分析提供了更完整的ASD特征与注意力不集中和白质组织指标之间的关联，特别是在胼胝体中。这种跨诊断方法可以揭示白质结构与神经发育症状之间的维度关系。
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.