Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach
Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach
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发育性阅读障碍背后的白质连接中断：一种机器学习方法
DOI：
10.1002/hbm.23112
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发表时间：
2016-04-01
影响因子：
4.8
通讯作者：
Gong, Gaolang
中科院分区：
文献类型：
--
作者：
Cui, Zaixu;Xia, Zhichao;Gong, Gaolang
发展性阅读障碍被认为是由多种原因引起的，具有多种表现形式，这意味着它对人类大脑的影响是分布的、多维的。在阅读困难儿童中观察到特定白质束/区域的破坏。然而，尚不清楚发展性阅读障碍是否以多维方式影响人脑WM。作为评估这一假设的自然工具，本研究应用多元机器学习方法比较了28名学龄阅读障碍儿童和33名年龄匹配的对照组。利用结构磁共振成像（MRI）和扩散张量成像（diffusion tensor imaging）在区域水平上提取5个多类型WM特征：白质体积、分数各向异性、平均扩散系数、轴向扩散系数和径向扩散系数。线性支持向量机（LSVM）分类器使用这些MRI特征来区分阅读障碍儿童和对照组的准确率达到83.61%。值得注意的是，有助于分类的最具区别性的特征主要与假定的阅读网络/系统中的WM区域相关（例如，上纵束、额枕束、丘脑皮质投影和胼胝体）、边缘系统（例如，扣带和穹窿）和运动系统（例如，小脑脚、辐射冠和皮质脊髓束）。使用逻辑回归分类器可以很好地复制这些结果。这些发现为支持发展性阅读障碍对人脑WM连通性的多维影响提供了直接证据，并强调了在阅读系统之外的WM束/区域参与阅读障碍。最后，鉴别结果证明了WM神经影像学特征作为识别阅读障碍个体的影像学标记的潜力。中国生物医学工程学报（英文版），2016。(c) 2016 Wiley Periodicals, Inc.；
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. Hum Brain Mapp 37:1443-1458, 2016. (c) 2016 Wiley Periodicals, Inc.