Cui Zaixu, Xia Zhichao, Su Mengmeng, Shu Hua, Gong Gaolang
State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
Hum Brain Mapp. 2016 Apr;37(4):1443-58. doi: 10.1002/hbm.23112. Epub 2016 Jan 20.
Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals.
发育性阅读障碍被认为是由多种原因导致的，并且有多种表现形式，这意味着它对人类大脑有分布式的多维度影响。在阅读障碍儿童中已观察到特定白质（WM）束/区域的破坏。然而，发育性阅读障碍是否以多维度方式影响人类大脑白质仍不清楚。作为评估这一假设的天然工具，本研究应用多变量机器学习方法，将28名学龄期阅读障碍儿童与33名年龄匹配的对照组进行比较。采集了结构磁共振成像（MRI）和扩散张量成像，以在区域水平提取五种多类型白质特征：白质体积、各向异性分数、平均扩散率、轴向扩散率和径向扩散率。使用这些MRI特征区分阅读障碍儿童和对照组时，线性支持向量机（LSVM）分类器的准确率达到了83.61%。值得注意的是，对分类有贡献的最具区分性的特征主要与假定阅读网络/系统内的白质区域（例如，上纵束、额枕下束、丘脑皮质投射和胼胝体）、边缘系统（例如，扣带和穹窿）以及运动系统（例如，小脑脚、放射冠和皮质脊髓束）相关。使用逻辑回归分类器很好地重复了这些结果。这些发现提供了直接证据支持发育性阅读障碍对人类大脑白质连接性的多维度影响，并强调了阅读障碍中公认阅读系统之外的白质束/区域的参与。最后，鉴别结果证明了白质神经影像特征作为识别阅读障碍个体的影像标志物的潜力。
