Detection of differentiated changes in gray matter in children with progressive hydrocephalus and chronic compensated hydrocephalus using voxel‐based morphometry and machine learning

: Currently, no neuroimaging study has reported the detection of specific imaging biomarkers that distinguish the progressive hydrocephalus (PH) and chronic compensated hydrocephalus (CH). Our main focus is to evaluate the different structural changes in classifying the two types of hydrocephalus children. Twenty-two children with hydrocephalus (12 PHs and 10 CHs) and 30 age-matched healthy controls were enrolled and the T1-weighted imaging was collected in the study. A customized voxel-based morphometry (VBM) approach and support vector machine (SVM) were combined to investigate the structural changes and group classification. Comparing with the controls and CH, PH groups invariably showed a significant decrease of GM volume in the bilateral hippocampus/parahippocampus, insula, and motor-related areas. SVM applied to the GM volumes of bilateral hippocampus/parahippocampus, insula, and motor-related areas correctly identified hydrocephalus children from normal controls with a statistically significant accuracy of 88.46% (p ≤ .001). In addition, SVM applied to GM volumes of the same regions correctly identified PH from CH with a statistically significant accuracy of 77.27% (p ≤ .009). Using VBM analysis, we characterized and visualized the GM changes in children with hydrocephalus. Machine learning results further confirmed that a significant decrease of the bilateral hippocampus/parahippocampus, insula, and motor-related GM volume can serve as a specific neuroimaging index to distinguish the children with PH from the children with CH and controls at individual. The findings could help to aid the identification of individuals with PH in clinical practice.