Psychiatry and Kennedy Center for Research on Human Development, Vanderbilt University, Nashville, Tennessee, USA Department of Biostatistics, Human Genome Sciences, Rockville, Maryland, USA Department of Computer Science, University of Utah, Salt Lake City, Utah, USA Psychiatry/Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, North Carolina, USA Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA Frank Porter Graham Child Development Institute, Chapel Hill, North Carolina, USA Department of Epidemiology and Health Policy Research, University of Florida, Gainesville, Florida, USA.
J Intellect Disabil Res. 2013 Nov;57(11):1037-49. doi: 10.1111/j.1365-2788.2012.01599.x. Epub 2012 Sep 24.
Increasing evidence suggests that autism is a disorder of distributed neural networks that may exhibit abnormal developmental trajectories. Characterisation of white matter early in the developmental course of the disorder is critical to understanding these aberrant trajectories.
A cross-sectional study of 2- to 6-year-old children with autism was conducted using diffusion tensor imaging combined with a novel statistical approach employing fractional anisotropy distributions. Fifty-eight children aged 18-79 months were imaged: 33 were diagnosed with autism, 8 with general developmental delay, and 17 were typically developing. Fractional anisotropy values within global white matter, cortical lobes and the cerebellum were measured and transformed to random F distributions for each subject. Each distribution of values for a region was summarised by estimating δ, the estimated mean and standard deviation of the approximating F for each distribution.
The estimated δ parameter, , was significantly decreased in individuals with autism compared to the combined control group. This was true in all cortical lobes, as well as in the cerebellum, but differences were most robust in the temporal lobe. Predicted developmental trajectories of across the age range in the sample showed patterns that partially distinguished the groups. Exploratory analyses suggested that the variability, rather than the central tendency, component of was the driving force behind these results.
While preliminary, our results suggest white matter in young children with autism may be abnormally homogeneous, which may reflect poorly organised or differentiated pathways, particularly in the temporal lobe, which is important for social and emotional cognition.
越来越多的证据表明，自闭症是一种分布式神经网络紊乱，可能表现出异常的发育轨迹。在该疾病的发育过程早期对脑白质进行特征描述对于理解这些异常轨迹至关重要。
本研究采用弥散张量成像（DTI）结合一种新颖的统计方法，对 2 至 6 岁自闭症儿童进行了横断面研究，该方法采用各向异性分数（FA）分布。对 18 至 79 月龄的 58 名儿童进行了成像：33 名被诊断为自闭症，8 名患有全面发育迟缓，17 名发育正常。测量了全脑白质、大脑皮质和小脑的 FA 值，并将其转换为每个受试者的随机 F 分布。为每个区域的每个值分布总结参数δ，即每个分布的近似 F 的估计均值和标准差。
与合并对照组相比，自闭症个体的估计δ参数显著降低。这在所有皮质脑回中以及小脑均为如此，但在颞叶中差异最为显著。在样本的整个年龄范围内，对进行预测性发育轨迹分析的结果显示，这些轨迹部分地区分了两组。探索性分析表明，是参数的变异性，而不是中心趋势成分，是这些结果的驱动力。
尽管结果初步，但我们的研究结果表明，自闭症幼儿的脑白质可能异常均匀，这可能反映了通路组织或分化不良，特别是在颞叶，而颞叶对于社会和情感认知很重要。
