Open AccessDisrupted white matter connectivity underlying developmental dyslexia: A machine learning approach
Author(s) -
Cui Zaixu,
Xia Zhichao,
Su Mengmeng,
Shu Hua,
Gong Gaolang
Publication year - 2016
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.23112
Subject(s) - superior longitudinal fasciculus , dyslexia , white matter , fractional anisotropy , corpus callosum , diffusion mri , psychology , neuroscience , neuroimaging , tractography , arcuate fasciculus , uncinate fasciculus , artificial intelligence , magnetic resonance imaging , reading (process) , computer science , medicine , political science , law , radiology
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 . © 2016 Wiley Periodicals, Inc.