The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
Hum Brain Mapp. 2021 Jul;42(10):3282-3294. doi: 10.1002/hbm.25434. Epub 2021 May 2.
Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.
个体形态脑网络基于 T1 加权磁共振成像 (MRI) 构建，反映了个体水平上解剖区域之间同步发育的强度。自闭症谱系障碍 (ASD) 是一种社会认知和神经发育障碍，具有高度的神经解剖异质性，但 ASD 个体水平上的形态网络的具体模式在很大程度上仍未得到探索。在这项研究中，使用来自 40 名年龄在 2-8 岁的 ASD 儿童（年龄范围）和 38 名年龄、性别和惯用手匹配的正常发育儿童（TDC）的高分辨率结构 MRI 数据构建了基于个体的形态网络。测量记录了三遍。结果表明，与 TDC 相比，患有 ASD 的幼儿个体水平形态脑网络的小世界特性（即 σ）值较低，皮质-纹状体-丘脑-皮质（CSTC）回路中的形态连接增加，皮质-皮质网络中的形态连接减少。此外，形态连接异常可以预测 ASD 幼儿社会交流缺陷的严重程度，从而在行为水平上证实了关联影响。这些发现表明，自闭症发育大脑中的形态脑网络在分离和分配信息方面效率低下。研究结果还强调了异常形态连接模式在 ASD 社会认知缺陷中的关键作用，并支持使用形态脑网络的异常发育模式来揭示 ASD 的新临床相关生物标志物。
