Glaucoma is not only an eye disease but is also associated with degeneration of brain structures. We now investigated the pattern of visual and non-visual brain structural changes in 25 primary open angle glaucoma (POAG) patients and 25 age-gender-matched normal controls using T1-weighted imaging. MRI images were subjected to volume-based analysis (VBA) and surface-based analysis (SBA) in the whole brain as well as ROI-based analysis of the lateral geniculate nucleus (LGN), visual cortex (V1/2), amygdala and hippocampus. While VBA showed no significant differences in the gray matter volumes of patients, SBA revealed significantly reduced cortical thickness in the right frontal pole and ROI-based analysis volume shrinkage in LGN bilaterally, right V1 and left amygdala. Structural abnormalities were correlated with clinical parameters in a subset of the patients revealing that the left LGN volume was negatively correlated with bilateral cup-to-disk ratio (CDR), the right LGN volume was positively correlated with the mean deviation of the right visual hemifield, and the right V1 cortical thickness was negatively correlated with the right CDR in glaucoma. These results demonstrate that POAG affects both vision-related structures and non-visual cortical regions. Moreover, alterations of the brain visual structures reflect the clinical severity of glaucoma.

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct cortico-basal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age and gender matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small-worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default-mode, language, visual and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus and paracentral lobule.. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%.