Neural imaging studies have found the connection between strabismus and brain cortex. However, the pathological mechanisms of intermittent exotropia are still not fully understood. In the present study, changes of binocular fusion related cortices in intermittent exotropia were investigated with blood oxygen level dependent functional magnetic resonance imaging. Activated cortices induced by fusion stimulus were found to be distributed in several regions such as bilateral middle occipital gyrus, bilateral middle temporal gyrus, left superior parietal lobule and so on. Compared with normal subjects, the increased activation intensity was observed in bilateral superior parietal lobule and inferior parietal lobule in subjects with intermittent exotropia. These findings indicate that binocular fusion involves a complicated brain network including several regions. And cortical activities of bilateral superior parietal lobule and inferior parietal lobule compensate for the binocular fusion dysfunction in intermittent exotropia.

Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features.